research papers in robotics

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• NATURE INDEX
• 12 October 2022

Growth in AI and robotics research accelerates

It may not be unusual for burgeoning areas of science, especially those related to rapid technological changes in society, to take off quickly, but even by these standards the rise of artificial intelligence (AI) has been impressive. Together with robotics, AI is representing an increasingly significant portion of research volume at various levels, as these charts show.

Across the field

The number of AI and robotics papers published in the 82 high-quality science journals in the Nature Index (Count) has been rising year-on-year — so rapidly that it resembles an exponential growth curve. A similar increase is also happening more generally in journals and proceedings not included in the Nature Index, as is shown by data from the Dimensions database of research publications.

Source: Nature Index, Dimensions. Data analysis by Catherine Cheung; infographic by Simon Baker, Tanner Maxwell and Benjamin Plackett

Leading countries

Five countries — the United States, China, the United Kingdom, Germany and France — had the highest AI and robotics Share in the Nature Index from 2015 to 2021, with the United States leading the pack. China has seen the largest percentage change (1,174%) in annual Share over the period among the five nations.

AI and robotics infiltration

As the field of AI and robotics research grows in its own right, leading institutions such as Harvard University in the United States have increased their Share in this area since 2015. But such leading institutions have also seen an expansion in the proportion of their overall index Share represented by research in AI and robotics. One possible explanation for this is that AI and robotics is expanding into other fields, creating interdisciplinary AI and robotics research.

Nature 610 , S9 (2022)

doi: https://doi.org/10.1038/d41586-022-03210-9

This article is part of Nature Index 2022 AI and robotics , an editorially independent supplement. Advertisers have no influence over the content.

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Robots in Healthcare: a Scoping Review

• Medical and Surgical Robotics (F Ernst, Section Editor)
• Open access
• Published: 22 October 2022
• Volume 3 , pages 271–280, ( 2022 )

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• Ahmed Ashraf Morgan 1 ,
• Jordan Abdi 1 ,
• Mohammed A. Q. Syed 2 ,
• Ghita El Kohen 3 ,
• Phillip Barlow 4 &
• Marcela P. Vizcaychipi 1  

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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.

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Robotics in Healthcare: A Survey

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Avoid common mistakes on your manuscript.

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 1 provides a further breakdown of settings.

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 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 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.

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Ahmed Ashraf Morgan, Jordan Abdi & Marcela P. Vizcaychipi

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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.

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Supplementary file1 (DOCX 12 KB) Supplement A. Search string used.

Supplementary file2 (docx 138 kb) supplement b. list of all included studies, organised by date of publication., 43154_2022_95_moesm3_esm.docx.

Supplementary file3 (DOCX 35 KB) Supplement C. List of all named robots identified, a brief description, the category of robot, and number of publications that explored the robot. Sorted by most to least commonly studied. Excluding robots that were not clearly named in the study.

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Morgan, A.A., Abdi, J., Syed, M.A.Q. et al. Robots in Healthcare: a Scoping Review. Curr Robot Rep 3 , 271–280 (2022). https://doi.org/10.1007/s43154-022-00095-4

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500 research papers and projects in robotics – Free Download

The recent history of robotics is full of fascinating moments that accelerated the rapid technological advances in artificial intelligence , automation , engineering, energy storage, and machine learning. The result transformed the capabilities of robots and their ability to take over tasks once carried out by humans at factories, hospitals, farms, etc.

These technological advances don’t occur overnight; they require several years of research and development in solving some of the biggest engineering challenges in navigation, autonomy, AI and machine learning to build robots that are much safer and efficient in a real-world situation. A lot of universities, institutes, and companies across the world are working tirelessly in various research areas to make this reality.

In this post, we have listed 500+ recent research papers and projects for those who are interested in robotics. These free, downloadable research papers can shed lights into the some of the complex areas in robotics such as navigation, motion planning, robotic interactions, obstacle avoidance, actuators, machine learning, computer vision, artificial intelligence, collaborative robotics, nano robotics, social robotics, cloud, swan robotics, sensors, mobile robotics, humanoid, service robots, automation, autonomous, etc. Feel free to download. Share your own research papers with us to be added into this list. Also, you can ask a professional academic writer from  CustomWritings – research paper writing service  to assist you online on any related topic.

Navigation and Motion Planning

• Robotics Navigation Using MPEG CDVS
• Design, Manufacturing and Test of a High-Precision MEMS Inclination Sensor for Navigation Systems in Robot-assisted Surgery
• Motion Control of a Three Active Wheeled Mobile Robot and Collision-Free Human Following Navigation in Outdoor Environment
• One Point Perspective Vanishing Point Estimation for Mobile Robot Vision Based Navigation System
• Application of Ant Colony Optimization for finding the Navigational path of Mobile Robot-A Review
• Robot Navigation Using a Brain-Computer Interface
• Path Generation for Robot Navigation using a Single Ceiling Mounted Camera
• Exact Robot Navigation Using Power Diagrams
• Learning Socially Normative Robot Navigation Behaviors with Bayesian Inverse Reinforcement Learning
• Pipelined, High Speed, Low Power Neural Network Controller for Autonomous Mobile Robot Navigation Using FPGA
• Proxemics models for human-aware navigation in robotics: Grounding interaction and personal space models in experimental data from psychology
• Optimality and limit behavior of the ML estimator for Multi-Robot Localization via GPS and Relative Measurements
• Aerial Robotics: Compact groups of cooperating micro aerial vehicles in clustered GPS denied environment
• Disordered and Multiple Destinations Path Planning Methods for Mobile Robot in Dynamic Environment
• Integrating Modeling and Knowledge Representation for Combined Task, Resource and Path Planning in Robotics
• Path Planning With Kinematic Constraints For Robot Groups
• Robot motion planning for pouring liquids
• Implan: Scalable Incremental Motion Planning for Multi-Robot Systems
• Equilibrium Motion Planning of Humanoid Climbing Robot under Constraints
• POMDP-lite for Robust Robot Planning under Uncertainty
• The RoboCup Logistics League as a Benchmark for Planning in Robotics
• Planning-aware communication for decentralised multi- robot coordination
• Combined Force and Position Controller Based on Inverse Dynamics: Application to Cooperative Robotics
• A Four Degree of Freedom Robot for Positioning Ultrasound Imaging Catheters
• The Role of Robotics in Ovarian Transposition
• An Implementation on 3D Positioning Aquatic Robot

Robotic Interactions

• On Indexicality, Direction of Arrival of Sound Sources and Human-Robot Interaction
• OpenWoZ: A Runtime-Configurable Wizard-of-Oz Framework for Human-Robot Interaction
• Privacy in Human-Robot Interaction: Survey and Future Work
• An Analysis Of Teacher-Student Interaction Patterns In A Robotics Course For Kindergarten Children: A Pilot Study
• Human Robotics Interaction (HRI) based Analysis–using DMT
• A Cautionary Note on Personality (Extroversion) Assessments in Child-Robot Interaction Studies
• Interaction as a bridge between cognition and robotics
• State Representation Learning in Robotics: Using Prior Knowledge about Physical Interaction
• Eliciting Conversation in Robot Vehicle Interactions
• A Comparison of Avatar, Video, and Robot-Mediated Interaction on Users’ Trust in Expertise
• Exercising with Baxter: Design and Evaluation of Assistive Social-Physical Human- Robot Interaction
• Using Narrative to Enable Longitudinal Human- Robot Interactions
• Computational Analysis of Affect, Personality, and Engagement in HumanRobot Interactions
• Human-robot interactions: A psychological perspective
• Gait of Quadruped Robot and Interaction Based on Gesture Recognition
• Graphically representing child- robot interaction proxemics
• Interactive Demo of the SOPHIA Project: Combining Soft Robotics and Brain-Machine Interfaces for Stroke Rehabilitation
• Interactive Robotics Workshop
• Activating Robotics Manipulator using Eye Movements
• Wireless Controlled Robot Movement System Desgined using Microcontroller
• Gesture Controlled Robot using LabVIEW
• RoGuE: Robot Gesture Engine

Obstacle Avoidance

• Low Cost Obstacle Avoidance Robot with Logic Gates and Gate Delay Calculations
• Advanced Fuzzy Potential Field Method for Mobile Robot Obstacle Avoidance
• Controlling Obstacle Avoiding And Live Streaming Robot Using Chronos Watch
• Movement Of The Space Robot Manipulator In Environment With Obstacles
• Assis-Cicerone Robot With Visual Obstacle Avoidance Using a Stack of Odometric Data.
• Obstacle detection and avoidance methods for autonomous mobile robot
• Moving Domestic Robotics Control Method Based on Creating and Sharing Maps with Shortest Path Findings and Obstacle Avoidance
• Control of the Differentially-driven Mobile Robot in the Environment with a Non-Convex Star-Shape Obstacle: Simulation and Experiments
• A survey of typical machine learning based motion planning algorithms for robotics
• Linear Algebra for Computer Vision, Robotics , and Machine Learning
• Applying Radical Constructivism to Machine Learning: A Pilot Study in Assistive Robotics
• Machine Learning for Robotics and Computer Vision: Sampling methods and Variational Inference
• Rule-Based Supervisor and Checker of Deep Learning Perception Modules in Cognitive Robotics
• The Limits and Potentials of Deep Learning for Robotics
• Autonomous Robotics and Deep Learning
• A Unified Knowledge Representation System for Robot Learning and Dialogue

Computer Vision

• Computer Vision Based Chess Playing Capabilities for the Baxter Humanoid Robot
• Non-Euclidean manifolds in robotics and computer vision: why should we care?
• Topology of singular surfaces, applications to visualization and robotics
• On the Impact of Learning Hierarchical Representations for Visual Recognition in Robotics
• Focused Online Visual-Motor Coordination for a Dual-Arm Robot Manipulator
• Towards Practical Visual Servoing in Robotics
• Visual Pattern Recognition In Robotics
• Automated Visual Inspection: Position Identification of Object for Industrial Robot Application based on Color and Shape
• Automated Creation of Augmented Reality Visualizations for Autonomous Robot Systems
• Implementation of Efficient Night Vision Robot on Arduino and FPGA Board
• On the Relationship between Robotics and Artificial Intelligence
• Artificial Spatial Cognition for Robotics and Mobile Systems: Brief Survey and Current Open Challenges
• Artificial Intelligence, Robotics and Its Impact on Society
• The Effects of Artificial Intelligence and Robotics on Business and Employment: Evidence from a survey on Japanese firms
• Artificially Intelligent Maze Solver Robot
• Artificial intelligence, Cognitive Robotics and Human Psychology
• Minecraft as an Experimental World for AI in Robotics
• Impact of Robotics, RPA and AI on the insurance industry: challenges and opportunities

Probabilistic Programming

• On the use of probabilistic relational affordance models for sequential manipulation tasks inrobotics
• Exploration strategies in developmental robotics: a unified probabilistic framework
• Probabilistic Programming for Robotics
• New design of a soft-robotics wearable elbow exoskeleton based on Shape Memory Alloy wires actuators
• Design of a Modular Series Elastic Upgrade to a Robotics Actuator
• Applications of Compliant Actuators to Wearing Robotics for Lower Extremity
• Review of Development Stages in the Conceptual Design of an Electro-Hydrostatic Actuator for Robotics
• Fluid electrodes for submersible robotics based on dielectric elastomer actuators
• Cascaded Control Of Compliant Actuators In Friendly Robotics

Collaborative Robotics

• Interpretable Models for Fast Activity Recognition and Anomaly Explanation During Collaborative Robotics Tasks
• Collaborative Work Management Using SWARM Robotics
• Collaborative Robotics : Assessment of Safety Functions and Feedback from Workers, Users and Integrators in Quebec
• Accessibility, Making and Tactile Robotics : Facilitating Collaborative Learning and Computational Thinking for Learners with Visual Impairments
• Trajectory Adaptation of Robot Arms for Head-pose Dependent Assistive Tasks

Mobile Robotics

• Experimental research of proximity sensors for application in mobile robotics in greenhouse environment.
• Multispectral Texture Mapping for Telepresence and Autonomous Mobile Robotics
• A Smart Mobile Robot to Detect Abnormalities in Hazardous Zones
• Simulation of nonlinear filter based localization for indoor mobile robot
• Integrating control science in a practical mobile robotics course
• Experimental Study of the Performance of the Kinect Range Camera for Mobile Robotics
• Planification of an Optimal Path for a Mobile Robot Using Neural Networks
• Security of Networking Control System in Mobile Robotics (NCSMR)
• Vector Maps in Mobile Robotics
• An Embedded System for a Bluetooth Controlled Mobile Robot Based on the ATmega8535 Microcontroller
• Experiments of NDT-Based Localization for a Mobile Robot Moving Near Buildings
• Hardware and Software Co-design for the EKF Applied to the Mobile Robotics Localization Problem
• Design of a SESLogo Program for Mobile Robot Control
• An Improved Ekf-Slam Algorithm For Mobile Robot
• Intelligent Vehicles at the Mobile Robotics Laboratory, University of Sao Paolo, Brazil [ITS Research Lab]
• Introduction to Mobile Robotics
• Miniature Piezoelectric Mobile Robot driven by Standing Wave
• Mobile Robot Floor Classification using Motor Current and Accelerometer Measurements
• Sensors for Robotics 2015
• An Automated Sensing System for Steel Bridge Inspection Using GMR Sensor Array and Magnetic Wheels of Climbing Robot
• Sensors for Next-Generation Robotics
• Multi-Robot Sensor Relocation To Enhance Connectivity In A WSN
• Automated Irrigation System Using Robotics and Sensors
• Design Of Control System For Articulated Robot Using Leap Motion Sensor
• Automated configuration of vision sensor systems for industrial robotics

Nano robotics

• Light Robotics: an all-optical nano-and micro-toolbox
• Light-driven Nano- robotics
• Light-driven Nano-robotics
• Light Robotics: a new tech–nology and its applications
• Light Robotics: Aiming towards all-optical nano-robotics
• NanoBiophotonics Appli–cations of Light Robotics
• System Level Analysis for a Locomotive Inspection Robot with Integrated Microsystems
• High-Dimensional Robotics at the Nanoscale Kino-Geometric Modeling of Proteins and Molecular Mechanisms
• A Study Of Insect Brain Using Robotics And Neural Networks

Social Robotics

• Integrative Social Robotics Hands-On
• ProCRob Architecture for Personalized Social Robotics
• Definitions and Metrics for Social Robotics, along with some Experience Gained in this Domain
• Transmedia Choreography: Integrating Multimodal Video Annotation in the Creative Process of a Social Robotics Performance Piece
• Co-designing with children: An approach to social robot design
• Toward Social Cognition in Robotics: Extracting and Internalizing Meaning from Perception
• Human Centered Robotics : Designing Valuable Experiences for Social Robots
• Preliminary system and hardware design for Quori, a low-cost, modular, socially interactive robot
• Socially assistive robotics: Human augmentation versus automation
• Tega: A Social Robot

Humanoid robot

• Compliance Control and Human-Robot Interaction – International Journal of Humanoid Robotics
• The Design of Humanoid Robot Using C# Interface on Bluetooth Communication
• An Integrated System to approach the Programming of Humanoid Robotics
• Humanoid Robot Slope Gait Planning Based on Zero Moment Point Principle
• Literature Review Real-Time Vision-Based Learning for Human-Robot Interaction in Social Humanoid Robotics
• The Roasted Tomato Challenge for a Humanoid Robot
• Remotely teleoperating a humanoid robot to perform fine motor tasks with virtual reality

Cloud Robotics

• CR3A: Cloud Robotics Algorithms Allocation Analysis
• Cloud Computing and Robotics for Disaster Management
• ABHIKAHA: Aerial Collision Avoidance in Quadcopter using Cloud Robotics
• The Evolution Of Cloud Robotics: A Survey
• Sliding Autonomy in Cloud Robotics Services for Smart City Applications
• CORE: A Cloud-based Object Recognition Engine for Robotics
• A Software Product Line Approach for Configuring Cloud Robotics Applications
• Cloud robotics and automation: A survey of related work
• ROCHAS: Robotics and Cloud-assisted Healthcare System for Empty Nester

Swarm Robotics

• Evolution of Task Partitioning in Swarm Robotics
• GESwarm: Grammatical Evolution for the Automatic Synthesis of Collective Behaviors in Swarm Robotics
• A Concise Chronological Reassess Of Different Swarm Intelligence Methods With Multi Robotics Approach
• The Swarm/Potential Model: Modeling Robotics Swarms with Measure-valued Recursions Associated to Random Finite Sets
• The TAM: ABSTRACTing complex tasks in swarm robotics research
• Task Allocation in Foraging Robot Swarms: The Role of Information Sharing
• Robotics on the Battlefield Part II
• Implementation Of Load Sharing Using Swarm Robotics
• An Investigation of Environmental Influence on the Benefits of Adaptation Mechanisms in Evolutionary Swarm Robotics

Soft Robotics

• Soft Robotics: The Next Generation of Intelligent Machines
• Soft Robotics: Transferring Theory to Application,” Soft Components for Soft Robots”
• Advances in Soft Computing, Intelligent Robotics and Control
• The BRICS Component Model: A Model-Based Development Paradigm For ComplexRobotics Software Systems
• Soft Mechatronics for Human-Friendly Robotics
• Seminar Soft-Robotics
• Special Issue on Open Source Software-Supported Robotics Research.
• Soft Brain-Machine Interfaces for Assistive Robotics: A Novel Control Approach
• Towards A Robot Hardware ABSTRACT ion Layer (R-HAL) Leveraging the XBot Software Framework

Service Robotics

• Fundamental Theories and Practice in Service Robotics
• Natural Language Processing in Domestic Service Robotics
• Localization and Mapping for Service Robotics Applications
• Designing of Service Robot for Home Automation-Implementation
• Benchmarking Speech Understanding in Service Robotics
• The Cognitive Service Robotics Apartment
• Planning with Task-oriented Knowledge Acquisition for A Service Robot
• Cognitive Robotics
• Meta-Morphogenesis theory as background to Cognitive Robotics and Developmental Cognitive Science
• Experience-based Learning for Bayesian Cognitive Robotics
• Weakly supervised strategies for natural object recognition in robotics
• Robotics-Derived Requirements for the Internet of Things in the 5G Context
• A Comparison of Modern Synthetic Character Design and Cognitive Robotics Architecture with the Human Nervous System
• PREGO: An Action Language for Belief-Based Cognitive Robotics in Continuous Domains
• The Role of Intention in Cognitive Robotics
• On Cognitive Learning Methodologies for Cognitive Robotics
• Relational Enhancement: A Framework for Evaluating and Designing Human-RobotRelationships
• A Fog Robotics Approach to Deep Robot Learning: Application to Object Recognition and Grasp Planning in Surface Decluttering
• Spatial Cognition in Robotics
• IOT Based Gesture Movement Recognize Robot
• Deliberative Systems for Autonomous Robotics: A Brief Comparison Between Action-oriented and Timelines-based Approaches
• Formal Modeling and Verification of Dynamic Reconfiguration of Autonomous RoboticsSystems
• Robotics on its feet: Autonomous Climbing Robots
• Implementation of Autonomous Metal Detection Robot with Image and Message Transmission using Cell Phone
• Toward autonomous architecture: The convergence of digital design, robotics, and the built environment
• Advances in Robotics Automation
• Data-centered Dependencies and Opportunities for Robotics Process Automation in Banking
• On the Combination of Gamification and Crowd Computation in Industrial Automation and Robotics Applications
• Advances in RoboticsAutomation
• Meshworm With Segment-Bending Anchoring for Colonoscopy. IEEE ROBOTICS AND AUTOMATION LETTERS. 2 (3) pp: 1718-1724.
• Recent Advances in Robotics and Automation
• Key Elements Towards Automation and Robotics in Industrialised Building System (IBS)
• Knowledge Building, Innovation Networks, and Robotics in Math Education
• The potential of a robotics summer course On Engineering Education
• Robotics as an Educational Tool: Impact of Lego Mindstorms
• Effective Planning Strategy in Robotics Education: An Embodied Approach
• An innovative approach to School-Work turnover programme with Educational Robotics
• The importance of educational robotics as a precursor of Computational Thinking in early childhood education
• Pedagogical Robotics A way to Experiment and Innovate in Educational Teaching in Morocco
• Learning by Making and Early School Leaving: an Experience with Educational Robotics
• Robotics and Coding: Fostering Student Engagement
• Computational Thinking with Educational Robotics
• New Trends In Education Of Robotics
• Educational robotics as an instrument of formation: a public elementary school case study
• Developmental Situation and Strategy for Engineering Robot Education in China University
• Towards the Humanoid Robot Butler
• YAGI-An Easy and Light-Weighted Action-Programming Language for Education and Research in Artificial Intelligence and Robotics
• Simultaneous Tracking and Reconstruction (STAR) of Objects and its Application in Educational Robotics Laboratories
• The importance and purpose of simulation in robotics
• An Educational Tool to Support Introductory Robotics Courses
• Lollybot: Where Candy, Gaming, and Educational Robotics Collide
• Assessing the Impact of an Autonomous Robotics Competition for STEM Education
• Educational robotics for promoting 21st century skills
• New Era for Educational Robotics: Replacing Teachers with a Robotic System to Teach Alphabet Writing
• Robotics as a Learning Tool for Educational Transformation
• The Herd of Educational Robotic Devices (HERD): Promoting Cooperation in RoboticsEducation
• Robotics in physics education: fostering graphing abilities in kinematics
• Enabling Rapid Prototyping in K-12 Engineering Education with BotSpeak, a UniversalRobotics Programming Language
• Innovating in robotics education with Gazebo simulator and JdeRobot framework
• How to Support Students’ Computational Thinking Skills in Educational Robotics Activities
• Educational Robotics At Lower Secondary School
• Evaluating the impact of robotics in education on pupils’ skills and attitudes
• Imagining, Playing, and Coding with KIBO: Using Robotics to Foster Computational Thinking in Young Children
• How Does a First LEGO League Robotics Program Provide Opportunities for Teaching Children 21st Century Skills
• A Software-Based Robotic Vision Simulator For Use In Teaching Introductory Robotics Courses
• Robotics Practical
• A project-based strategy for teaching robotics using NI’s embedded-FPGA platform
• Teaching a Core CS Concept through Robotics
• Ms. Robot Will Be Teaching You: Robot Lecturers in Four Modes of Automated Remote Instruction
• Robotic Competitions: Teaching Robotics and Real-Time Programming with LEGO Mindstorms
• Visegrad Robotics Workshop-different ideas to teach and popularize robotics
• LEGO® Mindstorms® EV3 Robotics Instructor Guide
• DRAFT: for Automaatiop iv t22 MOKASIT: Multi Camera System for Robotics Monitoring and Teaching
• MOKASIT: Multi Camera System for Robotics Monitoring and Teaching
• Autonomous Robot Design and Build: Novel Hands-on Experience for Undergraduate Students
• Semi-Autonomous Inspection Robot
• Sumo Robot Competition
• Engagement of students with Robotics-Competitions-like projects in a PBL Bsc Engineering course
• Robo Camp K12 Inclusive Outreach Program: A three-step model of Effective Introducing Middle School Students to Computer Programming and Robotics
• The Effectiveness of Robotics Competitions on Students’ Learning of Computer Science
• Engaging with Mathematics: How mathematical art, robotics and other activities are used to engage students with university mathematics and promote
• Design Elements of a Mobile Robotics Course Based on Student Feedback
• Sixth-Grade Students’ Motivation and Development of Proportional Reasoning Skills While Completing Robotics Challenges
• Student Learning of Computational Thinking in A Robotics Curriculum: Transferrable Skills and Relevant Factors
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• Transforming a Middle and High School Robotics Curriculum
• Geometric Algebra for Applications in Cybernetics: Image Processing, Neural Networks, Robotics and Integral Transforms
• Experimenting and validating didactical activities in the third year of primary school enhanced by robotics technology

Construction

• Bibliometric analysis on the status quo of robotics in construction
• AtomMap: A Probabilistic Amorphous 3D Map Representation for Robotics and Surface Reconstruction
• Robotic Design and Construction Culture: Ethnography in Osaka University’s Miyazaki Robotics Lab
• Infrastructure Robotics: A Technology Enabler for Lunar In-Situ Resource Utilization, Habitat Construction and Maintenance
• A Planar Robot Design And Construction With Maple
• Robotics and Automations in Construction: Advanced Construction and FutureTechnology
• Why robotics in mining
• Examining Influences on the Evolution of Design Ideas in a First-Year Robotics Project
• Mining Robotics
• TIRAMISU: Technical survey, close-in-detection and disposal mine actions in Humanitarian Demining: challenges for Robotics Systems
• Robotics for Sustainable Agriculture in Aquaponics
• Design and Fabrication of Crop Analysis Agriculture Robot
• Enhance Multi-Disciplinary Experience for Agriculture and Engineering Students with Agriculture Robotics Project
• Work in progress: Robotics mapping of landmine and UXO contaminated areas
• Robot Based Wireless Monitoring and Safety System for Underground Coal Mines using Zigbee Protocol: A Review
• Minesweepers uses robotics’ awesomeness to raise awareness about landminesexplosive remnants of war
• Intelligent Autonomous Farming Robot with Plant Disease Detection using Image Processing
• Auotomatic Pick And Place Robot
• Video Prompting to Teach Robotics and Coding to Students with Autism Spectrum Disorder
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Computer Science > Robotics

Title: contextual affordances for safe exploration in robotic scenarios.

Abstract: Robotics has been a popular field of research in the past few decades, with much success in industrial applications such as manufacturing and logistics. This success is led by clearly defined use cases and controlled operating environments. However, robotics has yet to make a large impact in domestic settings. This is due in part to the difficulty and complexity of designing mass-manufactured robots that can succeed in the variety of homes and environments that humans live in and that can operate safely in close proximity to humans. This paper explores the use of contextual affordances to enable safe exploration and learning in robotic scenarios targeted in the home. In particular, we propose a simple state representation that allows us to extend contextual affordances to larger state spaces and showcase how affordances can improve the success and convergence rate of a reinforcement learning algorithm in simulation. Our results suggest that after further iterations, it is possible to consider the implementation of this approach in a real robot manipulator. Furthermore, in the long term, this work could be the foundation for future explorations of human-robot interactions in complex domestic environments. This could be possible once state-of-the-art robot manipulators achieve the required level of dexterity for the described affordances in this paper.

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Human Factors and Cognitive Ergonomics in Advanced Industrial Human-Robot Interaction

SOCIALLY ADAPTIVE COGNITIVE ARCHITECTURE FOR HUMAN-ROBOT COLLABORATION IN INDUSTRIAL SETTINGS Provisionally Accepted

• 1 Donders Institute for Brain, Cognition and Behaviour, Radboud University, Netherlands

The final, formatted version of the article will be published soon.

This paper introduces DAC-HRC, a novel cognitive architecture designed to optimize human-robot collaboration (HRC) in industrial settings, particularly within the context of Industry 4.0. The architecture is grounded in the Distributed Adaptive Control theory and the principles of joint intentionality and interdependence, which are key to effective HRC. Joint intentionality refers to the shared goals and mutual understanding between a human and a robot, while interdependence emphasizes the reliance on each other's capabilities to complete tasks. DAC-HRC is applied to a hybrid recycling plant for the disassembly and recycling of Waste Electrical and Electronic Equipment (WEEE) devices. The architecture incorporates several cognitive modules operating at different timescales and abstraction levels, fostering adaptive collaboration that is personalized to each human user. The effectiveness of DAC-HRC is demonstrated through several pilot studies, showcasing functionalities such as turn-taking interaction, personalized error-handling mechanisms, adaptive safety measures, and gesture-based communication. These features enhance human-robot collaboration in the recycling plant by promoting real-time robot adaptation to human needs and preferences. The DAC-HRC architecture aims to contribute to the development of a new HRC paradigm by paving the way for more seamless and efficient collaboration in Industry 4.0 by relying on socially adept cognitive architectures.

Keywords: Cognitive Architecture, social robotics, Human-robot collaboration, Industry 4.0, Distributed adaptive control

Received: 27 Jun 2023; Accepted: 14 May 2024.

Copyright: © 2024 Freire, Guerrero-Rosado, Amil and Verschure. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mr. Ismael T. Freire, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands Mr. Oscar Guerrero-Rosado, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands Prof. Paul F. Verschure, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands

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This algorithm makes robots perform better

• Artificial Intelligence

Northwestern University engineers have developed a new artificial intelligence (AI) algorithm designed specifically for smart robotics. By helping robots rapidly and reliably learn complex skills, the new method could significantly improve the practicality — and safety — of robots for a range of applications, including self-driving cars, delivery drones, household assistants and automation.

Called Maximum Diffusion Reinforcement Learning (MaxDiff RL), the algorithm’s success lies in its ability to encourage robots to explore their environments as randomly as possible in order to gain a diverse set of experiences. This “designed randomness” improves the quality of data that robots collect regarding their own surroundings. And, by using higher-quality data, simulated robots demonstrated faster and more efficient learning, improving their overall reliability and performance.

When tested against other AI platforms, simulated robots using Northwestern’s new algorithm consistently outperformed state-of-the-art models. The new algorithm works so well, in fact, that robots learned new tasks and then successfully performed them within a single attempt — getting it right the first time. This starkly contrasts current AI models, which enable slower learning through trial and error.

The research was published today in the journal Nature Machine Intelligence.

“Other AI frameworks can be somewhat unreliable,” said Northwestern’s Thomas Berrueta , who led the study. “Sometimes they will totally nail a task, but, other times, they will fail completely. With our framework, as long as the robot is capable of solving the task at all, every time you turn on your robot you can expect it to do exactly what it’s been asked to do. This makes it easier to interpret robot successes and failures, which is crucial in a world increasingly dependent on AI.”

Berrueta is a Presidential Fellow at Northwestern and a Ph.D. candidate in mechanical engineering at the McCormick School of Engineering . Robotics expert Todd Murphey , a professor of mechanical engineering at McCormick and Berrueta’s adviser, is the paper’s senior author. Berrueta and Murphey co-authored the paper with Allison Pinosky , also a Ph.D. candidate in Murphey’s lab.

The disembodied disconnect

To train machine-learning algorithms, researchers and developers use large quantities of big data, which humans carefully filter and curate. AI learns from this training data, using trial and error until it reaches optimal results. While this process works well for disembodied systems, like ChatGPT and Google Gemini (formerly Bard), it does not work for embodied AI systems like robots. Robots, instead, collect data by themselves — without the luxury of human curators.

“Traditional algorithms are not compatible with robotics in two distinct ways,” Murphey said. “First, disembodied systems can take advantage of a world where physical laws do not apply. Second, individual failures have no consequences. For computer science applications, the only thing that matters is that it succeeds most of the time. In robotics, one failure could be catastrophic.”

To solve this disconnect, Berrueta, Murphey and Pinosky aimed to develop a novel algorithm that ensures robots will collect high-quality data on-the-go. At its core, MaxDiff RL commands robots to move more randomly in order to collect thorough, diverse data about their environments. By learning through self-curated random experiences, robots acquire necessary skills to accomplish useful tasks.

Getting it right the first time

To test the new algorithm, the researchers compared it against current, state-of-the-art models. Using computer simulations, the researchers asked simulated robots to perform a series of standard tasks. Across the board, robots using MaxDiff RL learned faster than the other models. They also correctly performed tasks much more consistently and reliably than others.

Perhaps even more impressive: Robots using the MaxDiff RL method often succeeded at correctly performing a task in a single attempt. And that’s even when they started with no knowledge.

“Our robots were faster and more agile — capable of effectively generalizing what they learned and applying it to new situations,” Berrueta said. “For real-world applications where robots can’t afford endless time for trial and error, this is a huge benefit.”

Because MaxDiff RL is a general algorithm, it can be used for a variety of applications. The researchers hope it addresses foundational issues holding back the field, ultimately paving the way for reliable decision-making in smart robotics.

“This doesn’t have to be used only for robotic vehicles that move around,” Pinosky said. “It also could be used for stationary robots — such as a robotic arm in a kitchen that learns how to load the dishwasher. As tasks and physical environments become more complicated, the role of embodiment becomes even more crucial to consider during the learning process. This is an important step toward real systems that do more complicated, more interesting tasks.”

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• Munich Institute of Robotics and Machine Intelligence (MIRMI) – The AI Mission Institute (AIM)
• Technical University of Munich

What is a Digital Robot Judge (DR.J)? - ICRA'24 Paper Highlight

NEWS, Research, Robotics | 13.05.2024

The 2024 IEEE International Conference on Robotics and Automation (ICRA) is being held from May 13th to 17th in Yokohama, Japan, and brings together robotics researchers, students and industrial partners from around the world to discuss the latest innovations and breakthroughs in robotics, and their ability to address global challenges.

Included among the papers accepted for presentation is one by MIRMI research associate, Peter So, and colleagues. We asked him about the paper, " Digital Robot Judge (DR.J): Building a Task-Centric Performance Database of Real-World Manipulation with Electronic Task Boards" , which he will be presenting in Yokohama today:

What did you find out?  

Peter So: We measured the performance gap between humans and robots for various manipulation skills across several top robotics labs in the world. We examined skills including localizing and pressing buttons, inserting and turning keys, reading and setting dials, opening and extracting batteries from electronic devices, and probing electrical circuits and wrapping up probing cables. We found that while robots still lag behind in overall skill dexterity and performance across the range of tasks they can greatly exceed human speed in specialized tasks with engineered tools. 

What challenges did you face during your research? 

Peter So: Collecting real-world robot experimental data is difficult, getting many disparate research labs to work on the exact same problem is even harder. That is why we organized a competition in partnership with the automatica trade show, Messe Muenchen through the Bavarian High-Tech Agenda munich_i to create a platform for roboticists around the world to demonstrate their capabilities. The competition was initially intended to take place in person, however due to the travel restrictions from COVID-19, we pivoted and designed an internet-of-things task board and conducted a remote competition which we believe widened the number and diversity of participants. This change also resulted in the creation of an ongoing community of roboticists who can stay connected through the web platform and continue to improve their robot's skills against our benchmark even after the competition.

Where do you see practical applications? 

Peter So: The robot manipulation skills developed on the task board are tailored to meet pressing industry needs. The task boards are designed with input from MIRMI's industry partners to capture the gaps in rolling out automation projects. Autonomous robot solutions to the task board from top performing teams in our competition are tested in the "Bring your own Device" (BYOD) skill transferability challenge to redeploy the same robot skills on an electronic waste device. So far we have designed two task board focused on manipulation skills for the handling and disassembly of electronic waste and plan to release new designs annually. 

Send an email to Peter So ( peter.so(at)tum.de ) if you would like to get your own task board to join our growing community and benchmark your robot's performance. 

ICRA 2024 will be running until 17th May, and will include plenary and keynote sessions, contributed paper sessions, workshops and tutorial sessions, forums, expo and exhibitions. More information regarding the event can be found by following this link .

Paper: P. So, A. Sarabakha, F. Wu, U. Culha, F. J. Abu-Dakka and S. Haddadin, "Digital Robot Judge: Building a Task-centric Performance Database of Real-World Manipulation With Electronic Task Boards," in IEEE Robotics & Automation Magazine , doi: 10.1109/MRA.2023.3336473.