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Recent advances in robotics and intelligent robots applications.

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Share and Cite

Song, Q.; Zhao, Q. Recent Advances in Robotics and Intelligent Robots Applications. Appl. Sci. 2024 , 14 , 4279. https://doi.org/10.3390/app14104279

Song Q, Zhao Q. Recent Advances in Robotics and Intelligent Robots Applications. Applied Sciences . 2024; 14(10):4279. https://doi.org/10.3390/app14104279

Song, Qi, and Qinglei Zhao. 2024. "Recent Advances in Robotics and Intelligent Robots Applications" Applied Sciences 14, no. 10: 4279. https://doi.org/10.3390/app14104279

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Robots in Healthcare: a Scoping Review

Ahmed ashraf morgan.

1 Chelsea and Westminster Hospital NHS Foundation Trust, London, UK

Jordan Abdi

Mohammed a. q. syed.

2 University of Essex, Colchester, Essex UK

Ghita El Kohen

3 Queen Mary University of London, London, UK

Phillip Barlow

4 Imperial College London, London, UK

Marcela P. Vizcaychipi

Associated data, purpose of review.

Robots are increasingly being adopted in healthcare to carry out various tasks that enhance patient care. This scoping review aims to establish the types of robots being used in healthcare and identify where they are deployed.

Recent Findings

Technological advancements have enabled robots to conduct increasingly varied and complex roles in healthcare. For instance, precision tasks such as improving dexterity following stroke or assisting with percutaneous coronary intervention.

This review found that robots have played 10 main roles across a variety of clinical environments. The two predominant roles were surgical and rehabilitation and mobility. Although robots were mainly studied in the surgical theatre and rehabilitation unit, other settings ranged from the hospital ward to inpatient pharmacy. Healthcare needs are constantly evolving, as demonstrated by COVID-19, and robots may assist in adapting to these changes. The future will involve increased telepresence and infrastructure systems will have to improve to allow for this.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43154-022-00095-4.

Introduction

Since the advent of the COVID-19 pandemic, the healthcare industry has been flooded with novel technologies to assist the delivery of care in unprecedented circumstances. [ 1 , 2 ] Staff vacancy levels increased, [ 3 , 4 ] social restrictions curtailed many traditional means of care delivery, [ 5 ••] and stringent infection control measures brought new challenges to human-delivered care [ 6 ]. Although many of the challenges that the pandemic brought onto healthcare have subsided, staff burnout, [ 7 ] an increasingly elderly population, [ 8 ] and backlog strains [ 9 , 10 ] caused by the pandemic have meant that staff shortages persist across healthcare systems across the world.

Robotic systems have long been cited to be able to alleviate workforce pressures, not least in healthcare. [ 11 ] Such systems can include remote presence robots for virtual consultations or transportation robots for automated delivery of equipment within hospitals. In addition to supporting hospitals, robotic systems can offer the ability to support clinical practice in a variety of specialties. Examples include exoskeletons that assist stroke patients in mobilisation and surgical robots that allow surgeons to remotely perform operations. It is important to understand the landscape of roles that robots have in healthcare to inform the research and development of the future.

This scoping review aims to establish the types of robots being used in healthcare and identify where they are deployed by way of qualitative analysis of the literature. Through this, predictions can be made for the future of robotics.

Methodology

The protocol for this scoping review was conducted in accordance with the principles of the Cochrane Handbook for Systematic Reviews of Interventions [ 12 ].

Search Strategy

The following bibliographical databases were searched: CINAHL, Cochrane Library, Embase, MEDLINE, and Scopus using medical subject headings (MeSH or where appropriate, the database-specific thesaurus equivalent) or text word terms. The database search query was composed of two search concepts: the intervention (robots) and the context (clinical setting). Free text terms for the intervention included: “service robot*”, “surgical robot*” and “socially assistive robot*”; their associated MeSH term was “Robotics”. The names of specific robot systems were also searched for. The free words used for the context included the following: “Inpatient setting”, “outpatient setting”, “pharmacy”, “trauma centre”, “acute centre”, "rehabilitation hospital”, “geriatric hospital” and “field hospital”; their associated MeSH term was “Hospitals”. The use of the asterisk (*) enables the word to be treated as a prefix. For example, “elder*” will represent “elderly” and “eldercare” amongst others (Supplementary Material A ). Additional studies were selected through a free search (Google Scholar) and from reference lists of selected publications and relevant reviews. The search was conducted on 11th March 2022.

Study Selection

Two reviewers (AM and MS) independently screened the publications in a three-step assessment process: the title, abstract and full text, and selections were made in accordance with inclusion and exclusion criteria. Inclusion: physical robot, used within a healthcare setting. Exclusion: review/meta-analysis, non-English, technical report, wrong setting, wrong intervention (e.g. artificial intelligence, no robot), full manuscript not available. All publications collected during the database search, free search and reference list harvesting were scored on a 3-point scale (0, not relevant; 1, possibly relevant; 2, very relevant) and those with a combined score of 2 between the reviews would make it through to the next round of scoring. All publications with a total score of 0 were excluded. A publication with a combined score of 1 indicated a disagreement between the reviewers and would be resolved through discussion. At the end of the full-text screening round, a final set of publications to be included into the review was acquired. Cohen’s kappa coefficient was calculated to ascertain the agreement between the reviewers in the title, abstract and full-text screening phases.

Data Extraction

The data extraction form was designed in line with the PICO approach (participants, intervention, comparator and outcomes). This process was conducted by 4 reviewers (JA, AM, GE and MPV) according to the same extraction pro forma. All clinical outcome measures reported in selected studies were extracted. Data extraction included, in addition to outcomes, the number of participants, participant age group, specific robot(s) used, study setting, study design, comparators and specialty.

Duplicate reports of the same study may be present in different journals, manuscripts or conference proceedings and may each focus on different outcome measures or include a follow up data point. The data extraction process was conducted on the most comprehensive report of a given study.

Data Synthesis and Analysis

The identified robots were grouped in this review by their predominant role. These groupings were created by the authors and are not outwardly referenced or defined by the studies from which they are identified. Data that are not clearly defined in the studies, such as robot name, were labelled “n/a”.

Search Results

The database search yielded 3836 publications and a further 96 were included from reference harvesting and the free search. Duplicate publications were removed ( n  = 98) and following three screening phases, 1123 publications were eligible for inclusion in the review. During data extraction, further 196 manuscripts were removed due to duplication, missing data, reviews, non-clinical evaluation with healthy participants or without enough appropriate data to extract, leaving a total of 927 original studies. The literature search is illustrated through the PRISMA flow diagram [ 13 ] in Fig.  1 , which highlights the review process and reasons for exclusion.

PRISMA diagram of selection process

The inter-rater agreement between the reviewers was calculated to be 0.23 for the title screen, 0.46 for the abstract screen and 0.53 the final report, demonstrating fair, moderate and moderate correlation between the reviewers respectively according to Cohen’s Kappa coefficient [ 14 ].

The included studies have publication dates ranging from 1994 to 2022, with between 0 and 152 publications per year. The median number of publications per year was 16 (IQR = 46). The number of publications peaked in 2021, with the number being 585% higher than 10 years prior. The publications per year can be seen in Fig.  2 . A full list of the final studies can be found in Supplementary Material B . Of the included studies, 65% were observational. The name of the robot evaluated was not clearly stated in 19% of publications. Of these, 89% were surgical robots.

Number of publications released per year about robots in healthcare

Participants and Settings

A total of 5,173,190 participants were included in the studies. Fifty-three percent of publications included fewer than 45 participants, with the larger populations generally coming from publications that analysed data from national databases. Eighty-nine percent of the manuscripts focused on adult populations, with only 7% solely including paediatrics. The specialties with most publications were stroke ( n  = 194, 21%), urology ( n  = 149, 16%) and general surgery ( n  = 137, 15%).

A range of clinical settings was used, but the two most common were the surgical theatre ( n  = 498) and the rehabilitation unit ( n  = 353). Catheterisation labs ( n  = 17), pharmacies ( n  = 16) and general wards ( n  = 10) were next in line. The remaining 4% of publications included elderly care units ( n  = 7), outpatient clinics ( n  = 6) and pathology labs ( n  = 4). Table ​ Table1 1 provides a further breakdown of settings.

Number of publications that explored each setting

Footnote: n/a refers to papers that do not clearly identify the study setting

Identified Robots and Their Roles in Healthcare

One hundred and seventy-one named robots were identified. The da Vinci Surgical System (Intuitive Surgical, USA) was most frequently studied ( n  = 291); the Lokomat® (Hocoma, Switzerland) ( n  = 72) and Hybrid Assistive Limb (HAL) (Cyberdyne, Japan) ( n  = 46) followed. A list of all identified and named robots can be found in Supplementary Material C .

The identified robots were categorised by their role, leading to the formation of 10 different groups. These groups represent the 10 overarching roles that robots have been found to have within healthcare. Table ​ Table2 2 summarises the robot groups, the number of robots found in each and the most common robot(s). Figure  3 shows the number of publications within each robot group.

The 10 robot groups identified and the most common robot in each

The number of studies in each of the 10 robot groups

Surgical robots can be used to assist in performing surgical procedures. Their specific roles within surgery are varied, ranging from instrument control to automated surgical table movement. This is a well-explored role, making up 51% of included studies and with 19 named robots identified. Most studies within this category are observational in nature (90%).

The da Vinci Surgical System is the predominant robot in use and thus has the largest literature base behind it. The system provides instruments that can be controlled by a surgeon through a console to perform minimally invasive surgery. It can be used in procedures including cholecystectomy, pancreatectomy and prostatectomy. For example, Jensen et al. [ 15 ] carried out a retrospective cohort study with 103 patients and compared robot assisted anti-reflux surgery with the da Vinci Surgical system to conventional laparoscopy and evaluated peri-operative outcomes. Other robotic systems that have been studied include the ROBODOC® Surgical System (Curexo Technology, USA) which was used in orthopaedics to plan and carry out total knee arthroplasties, [ 16 ] and Robotized Stereotactic Assistant (ROSA®) (Zimmer Biomet, France) which can assist with neurosurgical procedures such as intracranial electrode implantation [ 17 ].

Some of the identified robots can also assist with biopsy. For example, the iSR’obot™ Mona Lisa (Biobot Surgical, Singapore) can assist with visualisation and robotic needle guidance in prostate biopsy. One included publication studied this robot prospectively in a group of 86 men undergoing prostate biopsy with the researchers primarily evaluating detection of clinically significant prostate cancer [ 18 ].

Rehabilitation and Mobility

Rehabilitation and mobility robots are those that can physically assist or assess patients to aid in achieving goals. They can function to improve dexterity, achieve rehabilitation targets or aid in mobilisation. These robots may be used in the inpatient setting as well as in community rehabilitation centres. Rehabilitation is one of the major roles of robots in healthcare, making up 39% of reviewed manuscripts. This group of robots had the highest proportion of interventional studies, with 75% of all interventional studies originating from this group.

There are 102 named robots within this group, and they can be used for a variety of functions. Most are used for their ability to provide physical support to patients, assisting with rehabilitation. This can include single-joint or whole-body support. Others may be used for posture training through robotic tilt tables or for mobilisation through robotic wheelchairs.

The most common robot, Lokomat®, is a gait orthosis robot that can be used for rehabilitation in disorders such as stroke. Its primary role is to increase lower limb strength and range of motion. One study that evaluated this robot came from Husemann et al. [ 19 ] who carried out a randomised controlled trial with 30 acute stroke patients and compared those receiving conventional physiotherapy alone to those receiving conventional plus Lokomat therapy and evaluated outcomes such as ambulation ability. The second most studied robot, HAL, is a powered exoskeleton with multiple variants including a lower limb and single-joint version. Studies predominantly explore its use in neurological rehabilitation, but research is also present in areas such as post-operative rehabilitation.

Two studies showed robots being used to evaluate different patient parameters, such as gait speed. Hunova (Movendo Technology, Italy) is a robot that can be used for trunk and lower limb rehabilitation but can also be used for sensorimotor assessment such as limits of stability. An example of this robot being used was demonstrated by Cella et al. [ 20 ] who utilised the robot to obtain patient parameters that could be used in a fall risk assessment model within the elderly community, with the idea that robotic assessment can augment clinical evaluation and provide more robust data.

Radiotherapy

Radiotherapy robots can be used to assist with delivery of radiotherapy. This review identified one robot in this group: Cyberknife (Accuray, USA) ( n  = 18). This robot can assist with application of radiotherapy and image guidance to manage conditions such as liver and orbital metastases. All publications were observational with no comparator groups. One such publication was from Staehler et al. [ 21 ] who carried out a prospective case–control trial with 40 patients with renal tumour and evaluated safety and efficacy of Cyberknife use.

Telepresence

A core feature of the telepresence robotic group is the ability to allow individuals to have a remote presence through means of the robot. The robot may be used for activities such as remote ward rounds, remote surgical mentoring or remote assessment of histology slides. This group included 17 publications with the most common robots being remote presence (RP) (InTouch Technologies, USA) and Double (Double Robotics, USA). Double is a self-driving robot with two wheels and a video interface. Croghan et al. [ 22 ] used this robot for surgical ward rounds with a remote consultant surgeon and compared the experience to conventional ward rounds.

Interventional

Separate from their surgical counterpart, robots from this group are used to assist with interventional procedures. This includes procedures such as ablation in atrial fibrillation, percutaneous coronary intervention (PCI) and neuro-endovascular intervention. Their function can range from catheter guidance to stent positioning. There were 17 publications included that cover nine robots, with the most common being the Niobe System (Stereotaxis, USA) and Hansen Sensei Robotic Catheter System (Hansen Medical, USA), followed by the Corpath systems (GRX and 200) (Corindus, USA). The Niobe system uses robotically controlled magnets to allow for catheter direction. Arya et al. [ 23 ] carried out a case–control study comparing the Niobe system with conventional manual catheter navigation and evaluated effectiveness and safety in managing atrial fibrillation. The Corpath 200 system has been used for procedures such as PCI, [ 24 ] with robotic catheter guidance and the GRX system has also been reported to be used in endo-neurovascular procedures [ 25 •].

Socially Assistive

Socially assistive robots can take multiple forms, such as humanoid or animal-like, and work to provide support in areas traditionally done by humans such as companionship and service provision. Nine robots across 16 studies were included with the most popular being PARO (AIST, Japan) followed by Pepper (SoftBank Robotics, Japan) and NAO (SoftBank Robotics, Japan). PARO is a robotic seal that can move and make sounds in addition to responding to stimuli. Hung et al. [ 26 ] studied dementia patient perception of PARO on the hospital ward and its potential benefits. Pepper is a humanoid robot with a touch screen, capable of interacting with people through conversation. Boumans et al. [ 27 ] explored the use of Pepper in outpatient clinics with a randomised clinical trial. They compared human and Pepper-mediated patient interviews and evaluated patient perception following this.

There are a group of robots with the specific role of assisting with the management and delivery of pharmacy services. This includes drug storage, dispensing and compounding. For example, a robot may assist in preparation of cytotoxic drugs with the goal of reducing errors and minimising operator risk. Sixteen manuscripts with 10 robots were included. BD Rowa™ Vmax (BD Rowa, Germany) and APOTECA Chemo (Loccioni Humancare, Italy) were the most frequently studied robots. The BD Rowa™ Vmax is an automated system that allows for storage of medication and dispensing at the request of a user. Berdot et al. [ 28 ] used this system in a teaching hospital pharmacy and evaluated the return on investment including the rate of dispensing errors. The APOTECA Chemo system can be used to automate the production of chemotherapeutic treatment. Buning et al. [ 29 ] explored the environmental contamination of APOTECA Chemo compared to conventional drug compounding.

Imaging Assistance

Robots in this group have been specifically used for their ability to assist in carrying out imaging in different areas of medicine. Ten publications were included, with 8 robots in total. They predominantly include robotic camera holders in theatre but can also include robotic microscopes in neurosurgery and transcranial magnetic stimulation robots. Soloassist® (AKTORmed, Germany) and Freehand® (Freehand, UK), robotic camera controllers, were the most common in literature. Robotic camera holders may be controlled by various inputs such as voice and a joystick. In one publication, Soloassist was compared to a human scope assistant in colorectal cancer and safety and feasibility were assessed [ 30 ].

Disinfection

Robots may be used to disinfect clinical areas such as the ward or outpatient clinic. This group included 2 studies that evaluated the robotic systems LightStrike™ (Xenex, USA) and Ultra Violet Disinfection Robot® (UVD-Robot) (Clean Room Solutions). Both systems use ultraviolet (UV) light for disinfection of rooms, with the UVD-R being able to move autonomously. UVD-R was explored by Astrid et al. [ 31 ] who analysed its ability to disinfect waiting rooms in hospital outpatient clinics and compared this to conventional manual disinfection.

Delivery and Transport

There exists a role for robots in the transfer of items between areas. One publication was included that explored a delivery robot in the intensive care unit (ICU) [ 32 ]. The TUG Automated Delivery System (Aethon, USA) is a robot that after being loaded by an operator was used to autonomously deliver drugs from the pharmacy department to the ICU.

Evaluation of Robots in Clinical Settings

There has been an explosion of publications about the use of robots in healthcare in the past few years. This coincides with the COVID-19 pandemic, which highlighted a need for robots to carry out roles in challenging environments. It can also be linked with the ongoing development of technologies and the promise of robots alleviating the healthcare works’ burden and improving patient outcomes. The successful implementation of a robotic system is multifactorial, driven by social need, regulatory approval and the financial impact of deploying the system. Once introduced into healthcare, the durability and ongoing use of the robot are difficult to predict. Certain systems may go on to see long-term use, whilst others are underutilised or removed from practice. The outcome may be related to ease of use, perceived and objective benefit or availability of a newer system. Following successful introduction, robotic systems go on to be used for a variety of roles.

Ten overarching roles for robots in healthcare were identified in this review: surgical, rehabilitation and mobility, radiotherapy, socially assistive, telepresence, pharmacy, disinfection, delivery and transport, interventional and imaging assistance. In each group, robots may have different sub-roles, such as a focus on upper limb or lower limb strengthening in the rehabilitation category or for drug compounding or dispensing within the pharmacy category. These 10 groups have been created to consolidate a variety of robots, but it should be noted that there is an overlap between them as a robot may have multiple functions. For example, the low-intensity collimated ultrasound (LICU) system is categorised as an interventional robot with the primary role of ablation in conditions such as atrial fibrillation [ 33 •]. However, it also involves automated ultrasound (US) imaging which overlaps with the imaging assistance group. These roles allow robots to be used across a range of healthcare settings.

Certain robot groups have a well-defined area of use. For instance, the surgical group is unsurprisingly found predominantly within the hospital theatre setting. However, other robot groups are not so restricted to a well-circumscribed area. The pharmacy and socially assistive group of robots are such examples, which can be found in both inpatient and outpatient settings. Although numerous environments have been identified, most publications evaluated robots within only two: the theatre and rehabilitation unit. Robots have been less well explored in other settings, such as ED and ICU. This may be because some environments are more unpredictable, with fewer repetitive tasks that are well suited for a robot. The use of robots in more challenging and less controlled environments is a potential area for further research.

No matter the setting or role of the robot, a similar benefit is found with all robotic systems. They allow for a task to be carried out with less direct involvement of a human. The socially assistive and telepresence groups are good examples of this. This means that robots can be used in situations where services are needed but with restrictions on human presence. For instance, COVID-19 provides a clear example of where telepresence robots may be used to safely conduct remote ward rounds.

Quality of Selected Studies

This review did not exclude publications based on quality of methodology. Most studies were observational, with the interventional design being mainly used with rehabilitation and mobility robots. Many studies included in this review are also descriptive, with retrospectively defined outcomes. This highlights a need for further high-quality interventional studies to establish the potential benefits of robots across a range of roles. Additionally, a large portion of studies, outside of those using national databases, is of a small sample size. This, combined with the observational nature, reduces the overall quality of the dataset.

Review Strengths and Limitations

Review strengths include the large number of publications analysed and broad scope of the subject. This large dataset provides a comprehensive overview of the field of robotics in healthcare, and for synthesis of the data to establish the main robot roles in practice. As no limit was placed on date of publication, trends can also be established.

Given the broad area of exploration, there is a risk of missing relevant studies. Although many robots have been included, there will be some used in clinical practice that have not been identified by this review. However, it is unlikely that the missing robots will have a major impact on the 10 robot groups identified, given the substantial number of papers reviewed.

Several robots have multiple editions, but these were counted as singular entities, precluding more detailed analysis of each edition. Additionally, some publications did not specify the name of the robot used, and so there may be unique robots that were not identified in this review. For the same reason, some robots may be more commonly studied than described in this review. However, given the significant disparity in number of publications behind the predominant robots and those below them, the big picture is unlikely to drastically change. Finally, it should also be noted that there is a possibility of overlapping patient populations, with some studies utilising similar datasets.

Future of Robotics

The future of robots in healthcare predominantly lies with remote presence, and the performance of tasks detached from human presence. For instance, safe disinfection of a clinical environment or ward rounds with an at home specialist. Robots will allow for people to be present with increasing flexibility. This will aid in providing consistent services that are resilient to change and easy to adapt. For instance, a well-established robotic system that allows for remote surgery or telepresence ward rounds could mean that care can continue to be provided in a consistent manner during a pandemic.

To fully realise a future of widespread robot adoption, the necessary infrastructure must be developed. The best robotic system may be foiled by a poor internet connection. Investment in the systems that allow robots to operate is vital. The adoption of certain robot groups is also more likely to be seen due to the barriers of implementation. A socially assistive robot that moves on two wheels is likely much cheaper and easier to implement, especially in areas with fewer resources, compared to a large drug dispensing or surgical robot. Therefore, these more complex robots may struggle to see widespread use. It is important to focus on robots that are more likely to be globally utilised and have far-reaching effects, especially with scarcity of human resources. This is even more important when in crisis.

With ongoing technological advancements, robots may also be developed to carry out new functions. The roles described in this review arise from robots that have been used in a current clinical setting, but there are robots in development or pre-clinical evaluation that may yet be introduced. Advancement in the areas of artificial intelligence may lead to socially assistive robots that can function more independently and perform more complex tasks. Evolving technology such as augmented reality with haptic feedback may also provide a new scope for telepresence, such as remote physical guidance during a complex procedure.

Generally, there is a need to further evaluate the financial and clinical impact of robots with high-quality studies, larger population groups and an interventional design where possible. A need also exists to evaluate the use of robots in different populations and settings.

The evidence base for the use of robots in healthcare is expanding, and robots are being used across a range of specialties and settings. Ten overall roles for robots were identified, with the best explored being surgical and rehabilitation roles. However, there is a need for further high-quality research, particularly with less well-established robot roles such as disinfection. The future of robots lies in remote presence and the ability to carry out tasks in challenging environments; this will depend on the development of robust infrastructure and network capabilities to allow for successful adoption.

Below is the link to the electronic supplementary material.

Declarations

Marcela P. Vizcaychipi reports the following: Health-IT Meeting at the Chelsea & Westminster NHS Foundation Trust, Chair; One London Clinical Safety Officer — Board member; Annual Magill Symposium — Director.

The other authors declare no competing interests.

This article does not contain any studies with human or animal subjects performed by any of the authors.

This article is part of the Topical Collection on Medical and Surgical Robotics

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Robots Practice Skills Independently, Adapt to Unfamiliar Environments

New algorithm could help put robots to work in houses, hospitals, and factories.

Robots need practice before working in multiple settings—but they’re getting there. Photo by Dominik Scythe  on  Unsplash

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T he phrase “practice makes perfect” is usually reserved for humans, but it’s also a great maxim for robots newly deployed in unfamiliar environments.

Picture a robot arriving in a warehouse. It comes packaged with the skills it was trained on, like placing an object, and now it needs to pick items from a shelf it’s not familiar with. At first, the machine struggles with this, since it needs to get acquainted with its new surroundings. To improve, the robot will need to understand which skills within an overall task it needs to improve, then specialize (or parameterize ) that action.

A human onsite could program the robot to optimize its performance. But researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the AI Institute have developed a more effective alternative. Presented at the “Robotics: Science and Systems Conference” last July, their “Estimate, Extrapolate, and Situate” (EES) algorithm enables these machines to practice on their own, potentially helping them improve at useful tasks in factories, households, and hospitals.

Sizing up the situation

To help robots get better at activities like sweeping floors, EES works with a vision system that locates and tracks the machine’s surroundings. Then, the algorithm estimates how reliably the robot executes an action (like sweeping) and whether it would be worthwhile to practice more. EES forecasts how well the robot could perform the overall task if it refines that particular skill, and finally, it practices. The vision system checks whether that skill was done correctly after each attempt.

EES could come in handy in places like a hospital, factory, house, or coffee shop. For example, if you wanted a robot to clean up your living room, it would need help practicing skills like sweeping. According to Nishanth Kumar and his colleagues, though, EES could help that robot improve without human intervention with only a few practice trials.

“Going into this project, we wondered if this specialization would be possible in a reasonable amount of samples on a real robot,” says Kumar, co-lead author of a paper describing the work, who is a Ph.D. student in electrical engineering and computer science, and a CSAIL affiliate.

Kumar says, “Now, we have an algorithm that enables robots to get meaningfully better at specific skills in a reasonable amount of time with tens or hundreds of data points, an upgrade from the thousands or millions of samples that a standard reinforcement learning algorithm requires.”

See Spot sweep

EES’s knack for efficient learning was evident when implemented on Boston Dynamics’ quadruped, Spot, during research trials at the AI Institute. The robot, which has an arm attached to its back, completed manipulation tasks after practicing for a few hours. In one demonstration, the robot learned how to securely place a ball and ring on a slanted table in roughly three hours. In another, the algorithm guided the machine to improve at sweeping toys into a bin within about two hours. Both results appear to be an upgrade from previous frameworks, which would have likely taken more than 10 hours per task.

“We aimed to have the robot collect its own experience so it can better choose which strategies will work well in its deployment,” says co-lead author Tom Silver, Ph.D., an electrical engineering and computer science (EECS) alumnus and CSAIL affiliate who is now an assistant professor at Princeton University. “By focusing on what the robot knows, we sought to answer a key question: In the library of skills that the robot has, which is the one that would be most useful to practice right now?”

EES could eventually help streamline autonomous practice for robots in new deployment environments. But for now, it comes with a few limitations. For starters, they used tables that were low to the ground, which made it easier for the robot to see its objects. Kumar and Silver also 3D-printed an attachable handle that made the brush easier for Spot to grab. The robot didn’t detect some items and identified objects in the wrong places, so the researchers counted those errors as failures.

Giving robots homework

The researchers note that the practice speeds from the physical experiments could be accelerated further with the help of a simulator. Instead of physically working at each skill autonomously, the robot could eventually combine real and virtual practice.

They hope to make their system faster with less latency, engineering EES to overcome the imaging delays the researchers experienced. In the future, they may investigate an algorithm that reasons over sequences of practice attempts instead of planning which skills to refine.

“Enabling robots to learn on their own is both incredibly useful and extremely challenging,” says Danfei Xu, an assistant professor in the School of Interactive Computing at Georgia Tech, and a research scientist at NVIDIA AI (who was not involved with this work). “In the future, home robots will be sold to all sorts of households and expected to perform a wide range of tasks. We can’t possibly program everything they need to know beforehand, so it’s essential that they can learn on the job. However, letting robots loose to explore and learn without guidance can be very slow and might lead to unintended consequences. The research by Silver and his colleagues introduces an algorithm that allows robots to practice their skills autonomously in a structured way. This is a big step toward creating home robots that can continuously evolve and improve on their own.”

Silver and Kumar’s co-authors are AI Institute researchers Stephen Proulx and Jennifer Barry, plus four CSAIL members: Northeastern University Ph.D. student and visiting researcher Linfeng Zhao, MIT EECS Ph.D. student Willie McClinton, and MIT EECS professors Leslie Pack Kaelbling and Tomás Lozano-Pérez. Their work was supported, in part, by the AI Institute, the U.S. National Science Foundation, the U.S. Air Force Office of Scientific Research, the U.S. Office of Naval Research, the U.S. Army Research Office, and MIT Quest for Intelligence, with high-performance computing resources from the MIT SuperCloud and Lincoln Laboratory Supercomputing Center.

Published Aug. 8, 2024, by MIT .

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Motion planning method and experimental research of medical moxibustion robot of double manipulator arms

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• Zhengyao Yi 1 ,
• Haoming Li 1 ,
• Jiasheng Zhu   ORCID: orcid.org/0009-0001-1657-8793 1 ,
• Bingxing Feng 1 ,
• Jie Cao 1 ,
• Xianshu Lu 2 &
• Baocheng Wang 3  

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In order to alleviate the contradiction between the increasing demand of seafarers for moxibustion physiotherapy and the shortage of moxibustion doctors, a medical double-arm moxibustion robot was designed by using a six-degree-of-freedom mechanical arm and a four-degree-of-freedom mechanical arm to simulate traditional Chinese medicine moxibustion techniques. The robot coordinate system was established by D-H parameter method, and the forward and inverse kinematics of the robot model were calculated. The robot model was established and simulated by Robotics Toolbox in MATLAB. The angular velocity and angular acceleration curves of each joint and the trajectory and displacement of the robot end were obtained, and the feasibility of robot trajectory planning was verified. Through the preliminary design, the collaborative process of task assignment for double moxibustion robot was established. The simulation test bench was built to further simulate the temperature of human epidermis, and the relationship between the end distance of moxibustion robot and the heating of human epidermis was determined. The simulation and experimental results show that: a) The robot does not appear serious impact or stutter phenomenon in the simulation process, and the kinematics performance is good, which verifies the feasibility of the robot model; and b) during the simulation test, the heating temperature of human epidermis can be maintained at 43 °C, which realizes the expected moxibustion temperature of patients and verifies the effectiveness of the robot model.

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This work was supported by the Dalian Science and Technology Innovation Fund (Grant No. 2021JJ13SN50) and Dalian Shield Safe Technology Ltd. (Grant No. 2020067).

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Yi, Z., Li, H., Zhu, J. et al. Motion planning method and experimental research of medical moxibustion robot of double manipulator arms. J Braz. Soc. Mech. Sci. Eng. 46 , 564 (2024). https://doi.org/10.1007/s40430-024-05139-8

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What is robotics made of? The interdisciplinary politics of robotics research

• Ola Michalec   ORCID: orcid.org/0000-0003-3807-0197 1 ,
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Under framings of grand challenges, robotics has been proposed as a solution to a wide range of societal issues such as road safety, ageing society, economic productivity and climate change. However, what exactly is robotics research? From its inception, robotics has been an inherently interdisciplinary field, bringing together diverse domains such as engineering, cognitive science, computer science and, more recently, knowledge from social sciences and humanities. Previous research on interdisciplinarity shows that this mode of knowledge production is often driven by societal concerns and political choices. The politics of who gets to make these choices and on what terms is the focus of empirical research in this paper. Using a novel mixed-method approach combining bibliometrics, desk-based analysis and fieldwork, this article builds a narrative of interdisciplinarity at the UK’s largest public robotics lab, the Bristol Robotics Laboratory. This paper argues for the recognition of the plural ways of knowing interdisciplinarity. From citation analysis, through tracing of the emerging fields and disciplines, to, finally, the investigation of researchers’ experiences; each method contributes a distinct and complementary outlook on “what robotics is made of”. While bibliometrics allows visualising prominent disciplines and keywords, document analysis reveals influential and missing stakeholders. Meanwhile, fieldwork explores the logics underpinning robotics and identifies the capabilities necessary to perform the research. In doing so, the paper synthesises plural ways of locating politics in interdisciplinary research and provides recommendations for enabling “structural preparedness for interdisciplinarity”.

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“The primary mission of Bristol Robotics Lab (BRL) is to understand the science, engineering and social role of robotics and embedded intelligence. In particular, the key challenges surrounding adaptive robotics, namely: dealing with people and their unpredictability, unstructured and uncertain environments, and equipping robots for flexible roles.” Mission statement of the Bristol Robotics Laboratory (BRL, 2020 ).

Introduction

We encounter robots with increasing regularity in everyday life. Emerging from secluded military settings and heavy industry, and now characterised as self-driving vehicles, assistive living technologies and home cleaning, robots are designed to work both in and for society. Grand challenges and industrial strategies frame and fund such technologies, often justifying them in terms of their contribution to addressing societal problems such as road safety, an ageing European society, economic productivity and even climate change (Ridley et al., 2014 ; Waldrop 2016 ; Chance et al., 2017 ). Notably, robotics—the design and application of robots—is often presumed in policy to be something that happens after politics; technology as a palliative for economic and societal ills.

For example, innovation in self-driving vehicles is today’s quintessential case of robots entering society. Here, as with other forms of innovation, technology development is itself a site of political contestation (Winner, 1997 ; Noble, 1984 ; Feenberg, 2002 ). Moreover, at these sites lie a host of political questions and choices. In the case of self-driving vehicles questions are so often framed narrowly around road safety. However, if the transformation of our public and private transport systems are to be as radical as proponents make out, other questions arise. Who will benefit from these transformations and how will those benefits be distributed? Will inequalities in society worsen? What environmental or economic harms might be created through the manufacture and use of such technologies? How will self-driving vehicles re-shape personal responsibility on roads and in public spaces, and with what consequences for litigation, education and regulation?

The political challenge of research in self-driving vehicles and robotics more broadly is not to scale up technology with utmost haste. Rather, it is to ensure the most appropriate possibilities for society, the economy and the environment come to fruition (Sparrow and Howard, 2017 ). More consequentially for this paper, redirecting technological change in ways that are more beneficial to society requires political action that extends into the procedures and practices of research (Stilgoe, 2020 ; Lo Piano, 2020 )—taking the form sometimes of policy, but often of mundane everyday choices made by researchers.

Interdisciplinary robotics is twice implicated in these politics and choices. First, in how interdisciplinary modes are so often championed in addressing grand challenges (Hrynaszkiewicz and Acuto, 2015 ). As robots, autonomous systems and underlying infrastructures are integrated into the fabric of social worlds, they require a wider range of actors to engage with them (Goulden et al., 2017 ). In particular, the expansion from military and industrial development to domestic and non-industrial applications (e.g., cars, carers, cleaners) has led to the interdisciplinary inclusion of social sciences, humanities, arts, end-users and policymakers (Goulden et al., 2017 ; Patel et al., 2019 ). By funding interdisciplinary projects, typically industry-academia collaborations, governments hope to bring science and technology closer to the citizens, hastening public acceptance and adoption.

Second, robots are both material and social—robots shape society as much as people shape robots (Šabanović, 2010 ). Politics in technology development comes about then because the emergence of science and technology is co-produced with social orders rather than a product of them (Jasanoff, 2004 ). And so, in this paper we aim to understand how robotics is co-produced both within research institutions and with wider society.

Our research questions are: what are the politics of interdisciplinary robotics research? What can this tell us about what robotics is made of? Finally, how could research projects be structurally prepared for interdisciplinarity, so that they’re aligned with the public interest?

In answering these questions, we understand politics in three complementary ways. A first kind assesses, cartographically, how interdisciplinary robotics is. What are the kinds and number of disciplines involved, the modes and tools of integration (Siedlok and Hibbert, 2014 ), and the knowledge communities that emerge from these relations? A second kind is discursive and institutional; it highlights the multiple and contested interpretations over the potentials and performance of robotics research. Moreover particularly, how these interpretations are used as logics that legitimise, shape and steer the development of technologies and the co-production of social orders (Barry et al., 2008 ). A third kind is based on the capabilities that underpin the doing of interdisciplinary work, and making the everyday research choices we mention above (O’Donovan et al., 2020 ). These capabilities typically include technical and disciplinary skills, but also collective powers to convene, to work collaboratively and to negotiate alternative visions for robotics futures (ibid.).

Rather than focussing on a narrow application domain, say self-driving vehicle technologies, we locate our enquiry at the Bristol Robotics Laboratory (BRL) as a whole, together with its’ researchers and partners. The paper proceeds by investigating the cartographic, discursive and institutional features of interdisciplinarity and its politics at BRL along with the capabilities that underpin interdisciplinary practices.

In the next section, we expand a co-productionist account of the politics of technology to help us understand how science and society are mutually implicated in robotics. We then propose an evaluation of the politics of interdisciplinarity as a form of cartographic-discursive capability assessment. In section “Methods”, we operationalise these ideas using a novel mixed-methods framework integrating cartographic scientometric analysis with discursive document analysis.

Results are further discussed in section “Results”, where we introduce three case studies, each used to identify capabilities valued in three interdisciplinary research areas at BRL. Through the cases we assess, respectively, an ongoing portfolio of self-driving vehicle research projects; bioenergy research; and research on assisted living robotics.

In section “Discussion”, we discuss the implications of these politics for how researchers at BRL might better structure projects and align them with societal needs. In other words, we provide recommendations enabling “structural preparedness for interdisciplinarity” (Engwall, 2018 ). In particular, we argue that cultivating interdisciplinary capabilities at every research stage is the key aspect of acknowledging and addressing the politics of robotics.

Locating politics in interdisciplinary robotics research

Coproducing robotics, coproducing robots.

The emergence of modern robotics can be traced back to World War II when Soviet and Nazi armies launched remotely operated mines and tanks (Murphy, 2019 ). In 1962, Unimate, the first industrial robot, began working on a General Motors assembly line in Ewing Township, New Jersey (Diodato et al., 2004 ). Since then, the main logic of robotics has been replacing “dull, dirty and dangerous” jobs (Takayama et al., 2008 ). Meanwhile, universities have embraced this opportunity and established several research centres and industry collaborations. Since the early 1960s, artificial intelligence laboratories at Massachusetts Institute of Technology, Stanford, and The University of Edinburgh have worked on the design, development and conceptualisation of robotics (Diodato et al., 2004 ).

These labs, and others, have included scholars from a wider diversity of disciplines. As Birk ( 2011 ) argues, robotics is interdisciplinary, applied and collaborative by nature as it “combines electrical and mechanical body with computer brains” (p. 94). Furthermore, the emergence of new applications (e.g., transport or healthcare) enabled the opening up of the field towards new disciplines like biosciences, cognitive sciences and psychology (ibid.). The disciplinary distribution of publications in robotics between 1989 and 2009 (Fig. 1 adapted from Birk, 2011 ) shows the rise of bio- and cognitive sciences. How has the interdisciplinary landscape of robotics changed since then? Moreover, what are the dominating logics of robotics research and innovation?

The figure shows a rise of biological and cognitive sciences in addition to engineering and computer science disciplines underpinning robotics (adapted from Birk, 2011 ). This figure is not covered by the Creative Commons Attribution 4.0 International License. Reproduced with permission of Brik, copyright © Birk, all rights reserved.

Currently, the public funding landscape for robotics research is characterised by “grand challenges” or “mission-oriented” interdisciplinary funds, the ongoing work towards ethical standards and AI Ethics governance frameworks (DBEIS, 2019 ). Under this paradigm, robots and robotics are “co-produced”; they co-evolve with the people, processes and policies (Jasanoff, 2004 ). Jasanoff (ibid.) challenges linear modes of innovation, which emphasises science push or demand pull. Instead, innovation is susceptible to political influences, which can be traced in everyday practices and policies (ibid.). Co-production arises through interactions and collaboration; this includes interdisciplinary mode of research. It can be found in the milieu of institutions, imaginaries and infrastructures in and around robotics labs. Therefore, focusing on the work inside robotics labs provides a viewpoint on the “making of” technologies, science, standards, controversies and problems (Stephens and Lewis, 2017 ).

Steering robots for the society

From the IEEE ethical product standards (Winfield, 2019 ; O’Donovan, 2019 ), through robotics legislation (Palmerini et al., 2016 ), to AI ethics frameworks (Floridi, 2018 ), numerous initiatives are working towards enabling collaborative, reflexive, responsible and ethical research on robotics. While they are designed to align the direction of innovation with the contemporary social challenges, standards and high-level frameworks do not always reflect research activities on the ground. Concerns about robots in society, voiced through social movement organisations, collective action, media commentary and academic research suggest that the existing governance of robotics and autonomous systems technologies is insufficient (Torresen 2018; Winfield et al., 2019 ; Johnson and Verdicchio, 2017 ; Döring and Poeschl, 2019 ). In particular, Johnson and Verdicchio ( 2017 ) argue that the fears about AI and robotics governance focus too much on software capabilities. Instead, the missing object of governance is humans who make decisions about designing embedding robots and AI in society.

Whether ethical frameworks are possible to enact through research activities depends on people’s capabilities to perform socially beneficial interdisciplinary research. Drawing from O’Donovan et al. ( 2020 ), we understand capabilities as opportunities to conduct interdisciplinary work valued by researchers themselves. Foregrounding capabilities is a critical feature of our approach. It shifts the analytical focus to the cultivation of means rather than solely measuring outputs or achievements. As such, we ought to acknowledge that capability building starts well before the research activities commence. Embedding non-academic partners in the research setting, building user testing infrastructure and living labs, and establishing professional networks are all common tools for enhancing collaborations in the UK research settings (Carnabuci and Bruggeman, 2009 ; O’Donovan et al., 2020 ).

For that reason, we regard mapping interdisciplinarity as a form of capability assessment, and further, capability building. Taking stock of interdisciplinary activities means funders and policymakers know what is going on inside research labs, what their strengths and gaps are. We argue that a systematic analysis of multiple ways interdisciplinarity is organised will enable us to think better about research evaluation and enhance traditional research quality metrics with considerations of responsibility, ethics and power.

Defining, describing and measuring interdisciplinarity

Disciplines come to life through establishing common concerns, sets of methods, and vocabulary. They are then reified through disciplinary journals and academic departments (Strathern, 2004 ; Barry et al., 2008 ). However, as Huutoniemi et al., ( 2010 ) noticed, “disciplines aren’t watertight boundaries”. This is particularly true of the emerging, evolving or challenge-oriented fields, which have more flexibility to incorporate the contributions from other disciplines (Abbot, 2001 ).

But inter disciplinarity is a slippery concept, escaping rigid definitions, typologies and measurements (Klein, 1996 ). It is not always clear, for example, when and how the integration of disciplines happens. Huutoniemi’s et al. ( 2010 ) review of 20 typologies of interdisciplinary research concluded that we can analyse interdisciplinarity according to the following foci of interest: 1. Which disciplines are integrated? 2. How is it done in practice? 3. Why does interdisciplinarity take place?

Kelly’s ( 1996 ) conceptualisation of “wide” and “narrow” interdisciplinarity addresses the first question. “Narrow” collaborations are carried out within the framework of an epistemologically and methodologically homogeneous field; while “wide” interdisciplinarity originates from conceptually diverse areas. Consider Shen’s et al. ( 2019 ) analysis of interdisciplinarity in robotics surgery. One way to conceptualise it is labelling it as “narrow” interdisciplinarity, as disciplines involved (surgery, engineering; radiology, nuclear medicine, medical imaging; and neurosciences) typically share a positivist epistemological position, and often, an overarching scientific method (Kelly, 1996 ). While analysing the breadth of interdisciplinary integration is the first step to understanding robotics, lab ethnography studies show that we ought to pay attention to work practices behind the labels of disciplines. As Knorr Cetina ( 1999 ) shows, there is no such thing as a single “scientific method” as each lab can be distinguished by their particular “epistemic culture”—a set of practices used to advance knowledge in particular fields, colloquially known as “tricks of the trade”. For example, robotics’ epistemic culture can be characterised by conventions in publishing that aren’t present in, for example, neuroscience. While robotics encourages publication of prototypes and patents, in neuroscience, it is commonplace to see publications focusing on theory development (Fitzgerald et al., 2014 ; Callard et al., 2015 ). Therefore, we ought to investigate both conceptual relationships between fields and practices hindering or enabling interdisciplinary collaborations. Such integrated accounts have a solid empirical grounding and contribute to a deeper understanding of political processes underpinning interdisciplinary research.

Previous research on interdisciplinarity shows that this mode of knowledge production is often driven by societal concerns and political choices. The politics of who gets to make these choices and on what terms can be revealed through the analysis of research practices. Indeed, this follows a rich tradition of lab studies within the field of science and technology studies (STS) (Sormani, 2016 ; Latour and Woolgar, 2013 ; Stephens and Lewis, 2017 ; Collins, 1985 ; Knorr Cetina, 1999 ). Past STS accounts of interdisciplinary collaborations called for an analytical shift from measuring disciplines alone to investigating lived experiences and practices (O’Donovan et al., 2020 ; Holmes et al., 2018 ). In particular, O’Donovan et al. ( 2020 ), highlighted that capabilities to perform inter- and transdisciplinary research go beyond cognitive abilities to synthesise theories, methods and concepts across faculties. They called for effective steering towards societal challenges and opportunities for researchers to build networks. Indeed, paying attention to actors involved in collaborations and their roles allows building a comprehensive case of interdisciplinarity at research institutions (Holmes et al., 2018 ).

Finally, interdisciplinarity is analysed through the lens of its guiding logics and motivations. Barry, Born and Weszkalnys ( 2008 ) specify accountability, innovation and ontological change as key drivers for interdisciplinarity. The political backdrop of this shift towards interdisciplinarity is, as Nowotny ( 2003 ) describes, a rise in accountability culture and a growing interest in justifying research applications for commercial or industrial settings. However, while innovation and accountability seem to be the most frequently used justifications, interdisciplinarity cannot be reduced to them. For Barry et al. ( 2008 ) the key rationale of interdisciplinarity is ontological change—reframing technical objects as both material and social constructs.

No single method would suffice to analyse, measure and evaluate interdisciplinarity (McLeish and Strang, 2016 ; Balsiger, 2004 ; Huutoniemi et al., 2010 ). This is partially due to the multiplicity of its logics and practices, which precisely make interdisciplinarity impossible to essentialise. This paper, therefore, follows calls for mixed-method approaches (Huutoniemi et al., 2010 ), as it incorporates qualitative (document analysis, workshops, interviews) and quantitative (bibliometrics) methods. Building on an approach from O’Donovan et al. ( 2020 ), we argue that mixed-method account of interdisciplinary capabilities will shine a light on how robots and robotics knowledge are co-produced in research labs.

To answer the research questions, we use a set of complementary quantitative and qualitative methods to build an in-depth case study on interdisciplinary research conducted at a single site, the Bristol Robotics Laboratory (Flyvbjerg, 2012 ; Eisenhardt, 1989 ). To visualise interdisciplinarity at an institutional scale, we conducted a bibliometric analysis of peer-reviewed publications co-authored by BRL researchers between 2004 and 2020. We augmented this knowledge with information about how collaboration and disciplinary integration happens at the project level. This was achieved through content analysis of publicly available documents on 63 recent BRL projects. Finally, a set of three embedded case studies of research areas within BRL were constructed from observational and desk-based research. These methods were designed to draw out researchers’ experiences of interdisciplinary research practices and the related research capabilities they valued.

Case study description

Bristol Robotics Laboratory is one of the major academic centres of public robotics research in the UK. Founded in 2004 as a multi-research group lab, it hosts over 300 researchers and industry practitioners (BRL, 2020 ). The laboratory is a collaboration between two local universities: The University of the West of England (UWE) and the University of Bristol. The site also provides professional services priming local and national innovation and entrepreneurial activity, such as Knowledge Transfer Partnerships, internships and start-up incubators. Finally, BRL’s reach extends beyond the robotics industry as the lab frequency partners with UWE’s Science Communication Unit to run programmes of public engagement activities.

The lab, according to its website, is organised in 16 groups addressing contemporary robot capabilities and applications (Table 1 ). While this structure indicates how the lab presents itself to the general public, funders and potential collaborations, it is less indicative of how BRL constitutes its day-to-day practices. For that, we undertook our empirical research (Figs. 2 and 3 ).

Participants and robot use the blocks to create alphanumeric characters on a seven-segment using coloured blocks. (Credit: Mehdi Sobhani).

TacTip (black semi-sphere) is developed to create a sense of touch for the robots so when the robot touches a surface of an object it can detect patterns and shapes based on deformation of the TacTip sensor. (Credit: Mehdi Sobhani).

Bibliometrics: peer-reviewed publications 2004–2020

We analysed peer-reviewed publications in pursuit of indicators that account for how research is performed at BRL. A corpus for analysis was compiled using publications retrieved from Scopus (644 publications) and Web of Science (WoS; 485 publications) as these databases yielded the most comprehensive datasets in the field (Carley et al., 2017 ). We searched for variants of “Bristol Robotics Laboratory” in the “affiliation” field. Our search returned results for the entire period BRL has been in operation (2004–2020). The corpus includes peer-reviewed articles, as well as conference proceedings—the most frequently used output formats for dissemination in robotics.

Interdisciplinary collaborations were visualised using VOSviewer software based on how each article was categorised under the respective schemes used by Scopus and WoS (Leydesdorff and Rafols, 2011 ). WoS, for example, decomposes its entire collection into 255 categories, what we call WoS categories , each of which can be considered a field of science (Carley et al., 2017 ). In graphs created using WoS data, each node represents a WoS category while connections between nodes indicate interdisciplinary collaborations. Furthermore, clusters of cognate disciplines based on citation flows in the overall WoS corpus are represented by nodes sharing the same colour (Shen et al., 2019 ). For this paper, we followed Carley et al. ( 2017 ) clustering of WoS categories: (1) biology and medicine, (2) psychology and social sciences, (3) chemistry and physics, (4) ecology and environmental science and technology, (5) engineering and mathematics. We complement the visualisation of WoS categories with keyword analysis. For this purpose, we use Scopus corpus showing the co-occurrence of the most prominent keywords associated with BRL publications and relationships between them, based on publications’ citations (van Eck and Waltman, 2013 ).

As Martínez-Gómez ( 2015 , p. 209) noted, the popularity of bibliometrics as a tool in science studies can be explained as it “provides a certain sense of objectivity for descriptive purposes”. Bibliometric analysis is time-specific and accounts for the tangible outputs of research projects. However, while the method provides a useful overview of interdisciplinarity at BRL, as with any method, resultant knowledge is partial in several regards. First, the analysis was bound to the papers co-authored by BRL affiliates, excluding potential BRL-related outputs written only by partners from other institutions. It, therefore, means that the analysis can provide insights on interdisciplinarity at the publication level, rather than at a project level. Second, due to the academic nature of the Scopus and WoS databases, the analysis excluded output types such as policy reports, public engagement or patents. Finally, the long timescales of peer-review and databases indexing lag of up to a year in some disciplines affected the completeness of the dataset for 2020. These limitations are mitigated through careful triangulation with qualitative data discussed below.

Content analysis: research documentation, 2015–2020

To find out about how interdisciplinarity is enacted beyond the peer-reviewed publications, we analysed publicly available documents on 63 recent projects at BRL using content analysis (Mayring, 2008 ). The qualitative review allowed to see how interdisciplinarity is conceptualised through project proposals (grant announcements at “Grants of the Web”; EPSRC, 2019 ) team building (staff profiles at university websites, partnerships with non-academic organisations) and results’ dissemination (news releases and project reports). Owing to the limited availability of information on the earliest projects, we focused our review on the projects active in the past 5 years (2015–2020). This resulted in a comprehensive review of 63 projects (including the publicly available information on Ph.D projects and Knowledge Transfer Partnerships). In some cases, we supplemented the review of the secondary materials with email information requests to lab research theme leaders, so they could ensure the validity of the answers. Out of 16 email requests sent to lab leaders, we received 4 responses.

We analysed the project data combining the following techniques: (1) charting collaborations between disciplines, as well as practitioners to analyse who is and who is not involved in coproducing robotics at BRL; (2) content analysis of how researchers themselves conceptualise interdisciplinarity in reports, proposals and staff websites. Table 2 summarises concepts guiding our analytical framework.

Assessing the research situation through interviews, observation and workshops

A series of grounded qualitative data generating tasks were carried out between November 2018 and March 2020 as part of a wider project investigating innovation practices and policies in robotics and informed by principles of situational analysis (Clarke, 2007 ) and discourse analysis (Keller, 2012 ). The analytic goal in the analysis was to specify which entities—of varying scale and composition—make a difference to the situation at BRL. Moreover in turn, to assess how the situation influences, shapes and co-produces action, both strategic and routine. Specifically, we were interested in the conditions that make interdisciplinary practices acceptable at any given time.

Research tasks included fact-finding visits to BRL for tours of the main facility, as well as off-site research and development infrastructure. Semi-structured interviews were then conducted with 13 staff from BRL and research partner organisations. All interviews were transcribed and coded with the help of QDA software, as were documents and images.

BRL researchers were also included in research tasks at the European Robotics Forum in 2019 and 2020—one of the European robotics community’s major annual networking and dissemination events. At the 2020 forum, one of this paper’s co-authors organised three practice and policy workshops, which included current and former BRL staff. We performed a conceptually informed coding of materials (Corbin and Strauss, 2008 ) and created analytical maps that progressed from charting the interdisciplinary field and identifying gaps in the materials, to the relations in the field and their meaning. Data collection and analysis thus mutually reinforced each other. These data and analysis are presented in the form of three embedded narrative case studies in section “Embedded case study narratives: interdisciplinary research projects at BRL”.

Reflective note

For the purpose of this manuscript, we formed an interdisciplinary team and took an interest in our own research practices. The first and the second author are social scientists, external to BRL, while the third author is a roboticist based at BRL. With all of us being early career researchers, we are usually positioned in the middle of research activities, yet far away from the discussions (and decisions) on funding, strategic directions or hiring processes. This allowed us to gain a critical distance, as we were liberated from the pressure to “sugar coat” our analysis. Yet, we believe we were able to draw a fair and constructive critique of the lab—we acknowledged successes and recommended practical opportunities for the expansion of interdisciplinary capabilities.

Diversity of disciplines and topics

A research profile of BRL is constructed using bibliometric data and illustrated in Figs. 4 and 5 . This highlights disciplines and topics represented in the lab’s research, their relative frequency, and proximity to other fields. According to the WoS database, the ten most frequently occurring disciplines (ranked by their relative sizes) are: robotics, computer science, automation control systems, biomedical engineering, instrumentation, material science, multidisciplinary sciences, applied physics, optics, pharmacology.

The overlay map illustrates the following “disciplinary” clusters: machine software (purple), machine hardware (blue), environment (yellow), medicine (green), human factors and society (red) (based on 485 publications; 2004–2020).

Nodes represent 1000 tops author keywords with co-occurrence ≥2; keywords were found in individual articles and the size of the node indicates relative prominence of the keywords in the corpus. Links represent co-occurrence relations between keywords in individual publications.

Furthermore, Fig. 4 groups five coloured interdisciplinary clusters based on the number of citations between WoS categories. Here, we overlay the BRL publications data on top of the default VOSviewer base map (Carley et al., 2017 ). To reflect how WoS categories apply to BRL, we name them as follows: machine software (purple), machine hardware (blue), environment (yellow), medicine (green), human factors and society (red). Notably, disciplines in a red cluster (social sciences and human factors) are overall further apart from their counterparts. They are also much less prominent in the BRL portfolio in terms of peer-reviewed outputs.

Although BRL researchers might not publish in social science and humanities venues, they do have a lot to say about “humans”, based on Fig. 5 visualisation of top keywords. Indeed, Fig. 5 draws our attention to certain usages of the term “human”: (1) to denote emerging fields like “human-robot interaction” (Winkle et al., 2018 ; Sobhani et al., 2015 ); (2) to describe people as subjects of experiments, i.e., references such as “controlled study”, “adult”, “reproducibility” (3) to exemplify beneficiaries of robotic inventions, e.g., recipients of robotic surgeries (Tzemanaki et al., 2014 ). However, judging from the poorer representation of social sciences and humanities journals in Fig. 4 , “humans” are yet to feature in BRL as contributors to the political debates.

Taken together, Figs. 4 and 5 indicate a number of prominent research themes in the lab. For example, there is a large green cluster around “microbial fuel cells” (e.g., Ieropoulos, Greenman and Melhuish, 2008 ) in Fig. 5 and a similar interdisciplinary cluster in the top right corner of Fig. 4 (the convergence of biochemistry, biotechnology and applied microbiology). Similarly, the yellow “electroactive polymer actuators” cluster in Fig. 5 (e.g., Chorley et al., 2009 ) is commonly referred to as “robotic muscles” and used by medical roboticists (green “medicine” cluster in Fig. 4 ). In other words, Figs. 4 and 5 illustrate success stories from the perspective of the lab, where a measure of success are peer-reviewed publications and citations.

Next, we complemented bibliometrics with content analysis of recent projects. An inductive discipline categorisation of 63 recent projects at BRL revealed that computer science is the most common discipline present in 37 projects. This is followed by mechanical engineering (18 occurrences), robotics (17) psychology (13), electronics engineering (12), design (9) and medicine (9) (Table 3 ). The importance of psychology and design ought to be noted here. Furthermore, content analysis of the recent BRL projects shows how inherently interdisciplinary certain fields of robotics are. Integrating citation flows in Figs. 4 and 5 with content analysis, we found several fields that would be difficult to place into a single discipline such as: design, synthetic biology and bioinformatics. We characterise them as the emerging interdisciplinary areas where BRL researchers are actively influencing the direction of the field.

Following Kelly ( 1996 ), we conceptualised “wide interdisciplinarity” as BRL projects involving social sciences, humanities or arts. “Medium interdisciplinarity” included diverse positivist disciplines (i.e., “human factors” disciplines like cognitive sciences, behavioural sciences and psychology) and “narrow disciplinarily” was limited only to “traditional” robotics fields (i.e., engineering, computer science; see Birk, 2011 ). Tables 3 and 4 summarise the results of content analysis while Appendix 1 details line-by-line analysis of each project.

Further analysis of project documents reveals numerous idiosyncrasies about the ways interdisciplinarity is mobilised. We found eight cases of research projects solely justified through framings of industry need, for example, a machine vision project on characterising window cracks in automotive vehicles or a project on underfloor insulation (Appendix 1). However, not all projects were explicitly applied, whether to the societal or industry challenges. We identified 25 projects that were predominantly framed as “theoretical”, most commonly in swarm robotics and bioenergy centres. These labs were also found to push disciplinary boundaries and work with emerging fields, such as synthetic biology or bioinformatics.

Still, the framing of “societal challenges” was the most frequently occurring one, with 40 projects charted in this category. In the case of “societal challenges” research, the knowledge base is often drawn from non-academic partners. For example, a project on self-driving vehicles drew legal and ethics expertise from a law firm and an insurance firm; a project on the future of care robots derived gerontology expertise from a care home. It is not uncommon for research projects to be framed as simultaneously responding to societal and industry challenges; we found this characteristic across 13 projects. For example, a project on applying drones to volcanic observatories, arctic research stations and bridge measurements also aims to “accelerate the commercial exploitation of unmanned air systems”; (CASCADE, 2020 ). Similarly, these challenge-oriented projects made the biggest claims about what we call “wide” interdisciplinarity, i.e., integration across technical disciplines, life sciences and social sciences.

The majority of the large consortium projects were funded by the European Commission or EPSRC, however, a few initiatives were industry-led R&D collaborations funded by Innovate UK or commercial firms. In terms of industry stakeholders, agronomics, automotive, defence and tech companies were the most prominent. In contrast, arts and voluntary sector organisations were the least common. These observations encourage reflections on the power relations present in the academic-industry collaborations and ethical concerns arising from the positionality of funders and stakeholders. Ultimately, they point at the need to understand what logics and narratives are present when performing research across explicitly interdisciplinary fields of robotics like “human factors”, “ethics” or “design”.

Using this research profile as an entry point, we examine in closer detail three cases of how interdisciplinary research has been done at BRL, revealing logics and capabilities underpinning a set of research projects.

Embedded case study narratives: interdisciplinary research projects at BRL

Aligning capabilities with funders’ requirements at bristol bioenergy centre.

SlugBot was “the world’s first artificial predator”, according to Time Magazine, (2001) “one of the world’s best inventions of 2001” and an early exemplar of interdisciplinary robotics at the lab that went on to become BRL. It worked by “hunting and catching slugs, and fermenting the corpses to produce the biogas, which is its sole source of energy” (Kelly et al., 2000 ; Kelly and Melhuish, 2001 , p.470). The goal was to build an agricultural robot that was both computationally and energetically autonomous. This could be achieved only through an interdisciplinary approach that brought together diverse knowledge areas: visual identification and obstacle detection, gripper and robot control engineering, GPS, mobile robotics, ecology and, finally, biogas and fuel cell sciences.

SlugBot is notable for two ways in which it contributed to a nascent interdisciplinary culture at BRL. First, for its foundational position in BRL’s cluster of bioenergy expertise (the large green cluster around “microbial fuel cells” in Fig. 5 ). Using Microbial Fuel Cell technology developed in SlugBot, researchers later developed an influential series of robotics innovations, which were the building blocks of the Bristol Bioenergy Centre (e.g., Eco-bots or Urine-tricity bot; Ieropoulos et al., 2010 ; Davies and Ieropoulos, 2019 ).

Second, the project was an exemplar of opportunistic, adaptable funding capabilities cultivated at BRL. The project was initially funded through a single seed-corn grant from the EPSRC. Further and more substantial funds were later secured from national funding agencies in the UK and the Bill and Melinda Gates Foundation (Ieropoulos et al., 2013 ). In particular, the Gates Foundation funded research on “pee-powered toilets” aimed to tackle the issues of personal safety and access to electricity in the poorest regions of the Global South. Over the last few years, Ieropoulos’ team have further developed and trialled urine-powered lights, which have been fitted inside toilets in Uganda and Kenya (Robial, 2020 ).

This is typical in that early funding successes coupled with industry recognition allowed BRL researchers to cultivate and maintain capabilities at the intersection of knowledge domains, in this case, bioenergy and robotics. Yet research funding is not neutral—funders influence not only who and what gets funded, but how interdisciplinary knowledge production is shaped along the way. One senior roboticist told us:

“The EPSRC and the [European] Commission tend to slice the domain differently. So EPSRC slices it by discipline… Whereas the European Commission is intrinsically cross-disciplinary. It doesn’t slice by discipline; it slices by problem domain.”

The point here is that the European model favours the type of applied interdisciplinary research BRL was coming to specialise in—developing deep interdisciplinary capabilities in application domains such as bioenergy. Our interviewee continued:

“So [in the UK] people who were making cars might be doing some robotics or doing research into making cars might be doing some robotics. People who were doing underwater things might be doing some robot, robotics, you know, underwater surveying. People who were doing space engineering might think, oh, we need a bit of robotics. But the funding streams were all focused on the application domains and then each one of them might have a bit of robotics in it.”

As a result, robotics capabilities were diffused across industrial sectors and academic disciplines. Moreover so, developing an interdisciplinary research culture was core to BRL’s unique proposition as a multi-research centre robotics lab. In words of one of our interviewees: “the core question we were asking ourselves [was] ‘how should we even try to do robotics, regardless of what the application is?’”

This is an important question because, according to the same interviewee, certain kinds of research are possible only if robotics is central to a project’s aim. BRL responded to this funding regime by developing interdisciplinary capabilities in fundraising and network building with European collaborators that would keep European Commission money coming in.

“So if the problem domain, it, it chooses to focus on a search and rescue…Then it doesn’t care, you know, it, it doesn’t determine, it doesn’t prescribe that that has to be solved by mechanical engineers or mathematicians or… Essentially all EU projects, and, and I think this is one of the great joys and strengths of EU projects, is that they’re a mixture of disciplines.

And so, part of the BRL growth story has been the ability of researchers to marry not only the funds, but the interdisciplinary cultures of the funders in building capabilities in house. And to align their capabilities with the needs of funders. Yet funding is by no means deterministic. Cultures of interdisciplinary research at BRL are plural and shaped by other exogenous and endogenous factors as the following cases illustrate.

Driving autonomous vehicle research at BRL

“Fully self-driving cars on the UK roads by 2021”—that was the plan announced in the UK government’s 2017 Industrial Strategy (HM Government, 2017 ). A bold ambition given the driverless technology innovation is led by a small number of powerful firms such as Google, Uber and Tesla, with traditional manufacturing giants following behind (Borrás and Edler, 2020 ). Public research is not in the driving seat. Nevertheless, the UK government has been seeking to develop capabilities in the testing and trialling of driverless technologies committing £250 m to this aim since 2015, via the Centre for Connected and Autonomous Vehicles (CCAV, 2020 ) and its portfolio of projects. CCAV promises “highly automated solutions”, “real-world benefits”, and a model of interdisciplinary innovation in which projects are typically mainly funded by the government with significant industry contributions.

BRL has participated in six CCAV-funded projects; Robopilot, Venturer, Capri, Flourish, MultiCAV, Connected Autonomous Vehicles (CAV)-Forth (Appendix 2). This work intersects with and builds on several of BRL’s stated research themes including Assisted Living, Safe Human-Robot Interaction, Swarm Robotics and Verification and Validation for Safety.

A logic of testing underpins interdisciplinary research present in CCAV. Driverless technologies require a large amount of cyber-physical infrastructure in their testing and ultimately in their deployment. And so, rather than develop driverless vehicles from the ground up, BRL researchers are applying their capabilities specifically in testing environments. These social and technical infrastructures are mobilised in procedures to test issues such as technical competence, safety and public acceptability (e.g., Flourish project).

In recent years, engineers and robotics researchers have increasingly transgressed the lab’s boundaries to conduct experiments and trials closer to the public, in living labs, test-beds and on public roads (Engels et al., 2019 ; Marres, 2020 ; Paddeu et al., 2020 ). However, the infrastructures, procedures and capabilities of testing don’t merely produce test-results. Testing is generative in itself; expert-led testing deliberately introduces something new into society (Marres and Stark, 2020 ; Marres, 2020 ). The explicit goal of CCAV’s test-beds, after all, is not so much to gatekeep driverless technologies, as to get them onto Britain’s roads as quickly as possible. The stakes here are high because once self-driving technologies are widely diffused within our transport systems, “we are relatively powerless in our attempts to individually opt out of something to which we are all collectively locked-in” (Stilgoe, 2020 ; p. 16)

All this means that the procedures of testing being established by BRL researchers, as well as the end results of this testing are deeply implicated in how technologies and society progress. Researchers, unbeknownst to them or not, have assumed the role in what is an emerging regime of experimental governance of driverless technologies. Broader societal concerns of accountability (Strathern, 2004 ), responsibility (Bryson et al., 2017 ) and democracy (Laurent, 2011 ) are brought to the fore.

Capabilities that might test these broader concerns are often unacknowledged in official documentation such as bid documents and projects or siloed in narrowly defined low-resource work packages. Yet concerns about these issues are evident in post hoc reflections on BRL projects (Parkhurst and Lyons, 2018 ), who critically reviewed the narratives of inevitability and the vested interests of actors influencing CAVs. They proposed that the research on CAVs should acknowledge deep uncertainties and be explicit about assumptions made about technologies adoption. However, it is unclear if these valued capabilities are available to researchers.

This matters because of a second observed interdisciplinary logic, that of market creation and market growth. This logic is evident in how certain kinds of social orders are produced through testing. Project documentation includes aims of addressing “blockers and drivers to the wide-scale adoption of CAV capability” (Venturer, 2020 ) bringing “autonomous racing technology to the light commercial vehicle market and demonstrate SAE Footnote 1 level 4 autonomy” (Robopilot project; quote from UKRI, 2020 ). The emphasis here is on bringing together a network of actors and research to drive the take-up of “publicly acceptable” driverless technologies.

Under this logic, it is the market that will be both the arbitrator and the arena of arbitration for the issues of accountability, responsibility and democracy that are central to governance. Involvement by a wider set of actors, be the researchers, end-users or others, is foreclosed without debate. As a result, through their role as experts in testing, BRL researchers have developed capabilities to accelerate or decelerate. We find less evidence for capabilities that would allow them to steer innovation. Nevertheless, we anticipate that with the explicit foregrounding of the themes like “ethical black boxes” and “responsibility” in the recent projects on driverless vehicles (i.e., Robo-TIPS and Driverless Futures? Footnote 2 ; see Appendix 1 and Sitlgoe, 2020 ) BRL will be better situated to align robotics with the societal challenges.

Assistive living robotics

Even before Covid-19, a logic of crisis was driving research in adult social care (O’Donovan, 2020 ). Ageing populations, insufficient finance in health and social care budgets and shortage of care workers are all rationales for innovating urgently in this domain (Prescott and Caleb-Solly, 2017 ). Assistive Living Robotics (ALR) is one set of responses to these challenges. From a research funder’s perspective (ibid.), ALR can be understood as a knowledge production phenomenon of crisis response in which interdisciplinary logics of innovation are mobilised in pursuit of societal challenges (Strathern, 2004 ). Motivated by this rhetoric, robotics in health and social care has been identified as a target area for development by several funding bodies including the EPSRC Healthcare Technologies Grand Challenge and the Long-Term Care Revolution initiative from Innovate UK (EPSRC, 2019 ; Marshall-Cyrus, 2016 ).

In particular, BRL has built research capabilities in the area of independent living . Here, interdisciplinary robotics technologies are used in support of maintaining an independent and healthy homelife. For example, robots like CHIRON are made of interchangeable material components connected to a user’s room. CHIRON can help people with a range of domestic and self-care tasks such as fetching, moving and lifting day-to-day objects in the home (see Appendix 2). The explicit goal of ALR technologies in these situations is to provide the support that could help avert an early move into institutionalised care, and, so goes the rhetoric, contribute to economic efficiencies and individual end-user wellbeing.

Advances in the mechanisms of behaviour modification, human robotics interfaces, participative design methodologies, surveillance technologies and machine learning techniques offer an opportunity to both health and social care practitioners, and prospective end-users for change in the provision of care services (Spanakis et al., 2016 ).

An effective strategy to introduce robotics into social care sectors requires that roboticists partner with actors in those sectors (Prescott and Caleb-Solley, 2017 ); people in need of care, their formal and informal carers, healthcare and service providers, clinicians and third sector organisations. Practically, this means designing and testing robots that will be acceptable and even enjoyable to use and ensuring that the technology meets ethical and cultural requirements. In determining and addressing these requirements, the research progresses not only through technological advances, but through innovations in methods, notably interdisciplinary approaches in Human-Robot Interaction such as participative design (e.g., Winkle et al., 2020 ), and the cultivation of relevant capabilities that underpin them (Table 5 ).

An important component of ALR research at BRL is testing infrastructure. The most prominent example of this is the Anchor Robotics Personalised Assisted Living Studio, a replica test-bed apartment centrally located within BRL’s main building. The living lab is a critical resource in facilitating research methodologies for doing innovation together with the end-users. It facilitates interdisciplinary methods that aim to understand people’s context of use, social situation, and issues of acceptability. Moreover, the living lab is a built and existing asset, deployed in response to funding calls that seek to frame, mobilise and address socio-economic challenges. As such, the living lab demonstrates historic research success, as well as future potential.

Living labs and test beds are, however, an approximation of reality that necessarily rely on a range of assumptions about social use contexts, regulatory and market conditions, and cultural acceptability. These assumptions, and the choices underpinning them, create challenges for “scaling up” from such test sites to wider society or transferring the user experiences to other contexts (Engels et al., 2019 ). For BRL’s ALR researchers three challenges are notable. Socio-economic challenges such as social isolation, ill health and poverty mean people who most need assistance are the least likely to gain access to the living lab. Challenges relating to the complexity of care makes it difficult for assistive technology to stay useful over time, as people’s needs change (Buhalis and Darcy, 2010 ). There is also an issue of taking this technology out of the lab and making it operational in the real-world (Spanakis et al., 2016 ).

These are challenges that cannot be met through the provision of more testing infrastructure. Instead, researchers are addressing them by up-scaling researcher-practitioner networks for interdisciplinary research and broadening out their analytic focus on the service user. One outcome of such partnership was a Ph.D research project, which conducted a series of field experiments with social robots placed in retirement villages (see Socrates project, Appendix 1; van Maris et al., 2020 ). In such settings, researchers see success as reliant on “building strong connections with local health and social care providers and older adult organisations” Footnote 3 .

In recognising a plurality of social and technical complexities, ALR researchers de-centre material robotics technologies in a shift to building relational aspects of interdisciplinary knowledge production, and human-centred methodologies. What is interesting here is the choices taken by researchers in addressing given crisis framings. In establishing a network of partners from wider society, researchers are blending a logic of innovation, with logics of accountability (Barry et al., 2008 ). Better robotics solutions here, according to the robotics researchers, are those mediated and co-created with a wider range of end-users and societal actors. This is in stark contrast to historic and contemporary anxieties of automation—which fear how technology might close-down personal and collective freedoms (Bassett and Roberts, 2019 )—and demonstrates how interdisciplinary capabilities might support a plurality of possible robotics futures.

Politics of interdisciplinary research

In this paper, we set to investigate what at first seems to be a simple question: what is robotics made of? We have shown that even within a single robotics lab, BRL, there is no single way of performing interdisciplinarity. We argue that highlighting the plurality of interdisciplinary activities helps to improve the understanding of robotics labs. In avoiding a one-fits-all blueprint for public robotics, we demonstrate how different actors: funders, scientists, industry partners and end-users both gain and lack capabilities to steer the direction of research and research policy.

Through a mixed-method approach, our research revealed a range of conceptual, relational and material characteristics of Bristol Robotics Lab: prominent disciplines and keywords, funding strategies, leading researchers, relationships with non-academic collaborators and testing infrastructures. In terms of the breadth and depth of interdisciplinarity at BRL, we found that the majority of recent research projects crossed multiple disciplines. As many as 28 (out of 63) projects were classified as “medium interdisciplinarity” and 24 projects were charted as “wide interdisciplinarity”. Most importantly, 40 projects were framed explicitly as “responding to societal challenges”. Researching robotics beyond the narrow questions of hardware and software translates to the academic impact, where biology and medicine-inspired innovations like microbial fuel cells (aka. Urine-powered toilet lights) and electroactive polymer actuators (aka. Robot muscles) have become flagship technologies at the lab.

Yet, researchers and funders ought to be careful about how they mobilise expertise on social issues, especially in the growing robotics-related fields like ethics or law. As the analysis of CCAV projects has shown, it’s common for robotics projects to be comprised of large multi-stakeholder consortia of universities and commercial enterprises, with end-users having limited capacity to co-create the research agenda. Although our analysis of CCAV projects shows “wide” interdisciplinarity in terms of diversity of knowledge areas and stakeholders, capabilities to steer the research framing were kept firmly in hands of the automotive industry. On the other hand, ALR projects included the end-users at multiple stage in their design, through the development of participative methods and infrastructures. This contrast presents an opportunity for future inclusion of the end-users and critical social sciences. What our research shows is that with ample government financial support for robotics, there is a diversity of possibilities for distributing, rather than concentrating, research resources and power. Moreover, the political decisions that guide distribution of resources and research can take place at multiple levels. Indeed, some roboticists at BRL have built methods that at least in part facilitate a diversity of approaches to epistemic power and the politics of decision making in research.

Mapping how researchers and funders contribute to research at BRL provides further evidence on actors present and missing from “the making of” robotics. As such, while organisations from automotive industry, agri-tech, health & social care, and defence industry were frequent collaborators, voluntary and community sector organisations were rarely included in the projects. This, once again, does raise a question about the representation of end-users’ interests. “Humans” might be at the centre of the BRL’s agenda (as Fig. 5 shows), but these “humans” are often rid of their complexities, relationships or socio-economic contingencies. Like the mythical “average man” in design and medicine, humans in robotics often exist outside of the society (Perez, 2019 ). Meanwhile, we argue that humans are inherently relational and political—the research on robotics in the society should reflect that. There is a risk that if end-users’ role will be limited to appraising usability and public acceptance, some more critical questions about justice, data rights, labour or sustainability will not be raised.

Structural preparedeness for interdisciplinarity

If societal challenges are to be addressed systemically, they ought to meaningfully involve societal partners and critical social sciences. We call for careful assembling of interdisciplinary work. While large interdisciplinary consortia may involve a diverse range of stakeholders, they do not necessarily disrupt the power dynamics of knowledge creation by design. This could be achieved by precise steering of research, which invites different voices and opens up research spaces to those usually excluded from them. In doing so, researchers and funders should pay attention to the power arrangements situated within such collaborations: who sets the agenda? Who defines the key terms? Who can afford to critique? We offer some practical steps how to act on these questions in Box 1 .

Although knowing disciplinary representation is informative for mapping the scope of BRL’s cognitive efforts, we have shown how interdisciplinarity can be understood as more than simply the synthesis of two or more disciplines. In practice, the performance and rationales of interdisciplinary work here are revealing. As are the infrastructures (i.e., test beds, living labs) that are being built. We showed several rationales that motivate the work of roboticists.

Bioenergy centre: experimentation, tackling poverty

Assistive living robotics: accountability, innovation, market creation, dealing with crisis;

Connected autonomous vehicles: testing innovation, market growth.

These logics and infrastructures are important as they demonstrate how BRL engages with the “real-world needs” such as government agenda, funders’ criteria, collaborators’ expectations. Ultimately, the logics of interdisciplinarity shape the nature of funding (i.e., the EU, UKRI, commercial), as well as the disciplines involved. Often the “choice” of research motivations is mediated by the funding criteria. In other words, BRL’s interdisciplinary activities shapeshift to fit perceptions of the world, which are co-constructed and negotiated between the researchers and funders (Weingart, 2000 ).

Finally, we investigated capabilities developed to mobilise interdisciplinary collaborations and enable “structural preparedness for interdisciplinarity” (Engwall, 2018 ). We have shown that BRL has developed capabilities to: (a) align the needs of funders with the lab’s own narratives; (b) build a diverse network of regional stakeholders. However, we found ambivalent capabilities to steer innovation and challenge assumptions underlying key narratives around technological progress. In case of projects with a significant influence of industrial partners, we found that BRL researchers could accelerate or decelerate innovation, rather than open it up to critical questions. We recommend that BRL researchers and funders stay reflective of the power relations present in the academic-industry collaborations and ethical concerns arising from the positionality of project stakeholders (Box 1 ).

Box 1. Recommendations for BRL

Reflect on your research proposals: who sets the agenda? Who doesn’t?

Who can critique? Who cannot? In particular, put effort into steering innovation : open it up to political and ethical questions.

Assemble interdisciplinary teams with care, so that new actors and disciplines are able to disrupt, or at least question, the existing power arrangements.

Support and cultivate initiatives aiming to connect BRL with the community sector and charities.

Consider the implications of your research environment: how are participants affected by being placed in a lab, a test-bed, or a public road?

Embed these practices in every aspect of your research culture: from building partnerships, bidding, setting research questions, conducting empirical research, dissemination to, finally, research evaluation.

Conclusions

In this article, we sought to understand how interdisciplinary research at BRL is positioned to address grand challenges. While the researchers used a number of strategic motivations to gain funding and build collaborations, they also demonstrated a willingness to engage with interdisciplinary robotics at the ontological level. In its early days, roboticists were asking themselves: “how should we even try to do robotics, regardless of the application?”. And so, through iterations of experiments and prototypes, we can see emerging innovation practices that are often co-creating robots, making choices together and doing politics with a wider range of societal actors

Our paper presents a practical intervention into the politics of interdisciplinarity at BRL. Based on the rich and diverse empirical data, we highlighted the current capabilities and recommended further opportunities to create better robots. After all, robotics research reflects the values and possibilities of society itself. Importantly, how that society is represented, is not given, but a result of funders’, researchers’, practitioners’ and (to a lesser degree) end-users’ political choices and contingencies . In opening some trajectories of the future, while closing the others, these choices and contingencies are what robotics is made of.

Data availability

Where appropriate, data generated or analysed during this study are publicly available via appendices as well as data repository: http://researchdata.uwe.ac.uk/579/

SAE International, (previously known as the Society of Automotive Engineers) is a U.S.-based, organisation developing standards and convening engineering professionals across various industries.

These projects are funded by the UK Research Councils, rather than the Government’s CCAV initiative.

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Acknowledgements

The research project received ethical approval (no FET.18.11.023) for collecting data from workshop observations and participants’ reflections. The project was funded by UWE grant for building interdisciplinary collaborations. The research of one of the authors was funded through the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 788359 (“SCALINGS: Scaling up co-creation? Avenues and limits for including society in science and innovation”). Many thanks to BRL researchers for participating in interviews and workshops, as well as to Becky Upton for helpful comments on the manuscript.

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Ola Michalec

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Mehdi Sobhani

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OM: design and analysis of data (bibliometrics, content analysis), manuscript drafting (introduction, literature review, methods, results, conclusions), manuscript revising, final approval. COD: design and analysis of quantitative data (bibliometrics), manuscript drafting (methods) manuscript revising, final approval. MS: design and analysis of qualitative data (interviews), manuscript drafting (results), manuscript revising, final approval.

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Michalec, O., O’Donovan, C. & Sobhani, M. What is robotics made of? The interdisciplinary politics of robotics research. Humanit Soc Sci Commun 8 , 65 (2021). https://doi.org/10.1057/s41599-021-00737-6

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Received : 17 August 2020

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DOI : https://doi.org/10.1057/s41599-021-00737-6

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