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The purpose of this paper is to describe a range of artificial muscle and soft gripping technologies for robotic applications.

Following a short introduction, this paper first discusses the role of air muscles and other pneumatic actuation technologies. It then considers electroactive polymer and shape‐memory alloys and finally discusses the prospects for various classes of electrohydrodynamic fluids.

This paper shows that a technologically diverse range of novel actuation techniques exist, or are under development, which can act as artificial muscles and soft grippers. They are based on pneumatics, shape changing materials and electrohydrodynamic fluids and have prospects to impart robots with improved or unique capabilities.

The paper provides an insight into developments in artificial muscle and soft gripping technologies. These are expected to play a vital role in future robot generations.

The following paper is a “Q&A interview” conducted by Joanne Pransky of Industrial Robot Journal as a method to impart the combined technological, business and personal experience of a prominent, robotic industry PhD-turned successful innovator and entrepreneur regarding the commercialization and challenges of bringing his technological inventions to market. This paper aims to discuss these issues.

Considered one of the top biomechatronics researchers in the world, Dr Hugh Herr heads the MIT Biomechatronics Research Group and Center for Extreme Bionics. His research programs seek to advance technologies that promise to accelerate the merging of body and machine, including device architectures that resemble the body’s musculoskeletal design, actuator technologies that behave like muscle and control methodologies that exploit principles of biological movement. Herr’s methods encompass a diverse set of scientific and technological disciplines that are advancing an emerging field of engineering science that applies principles of biomechanics and neural control to guide the designs of human rehabilitation and augmentative devices.

As a teenager, Herr was a highly competitive mountain climber until he had to have both legs amputated below the knees after suffering severe frostbite during a 1982 mountain expedition at the age of 17. As a result of this experience, he directed his efforts and talent to try to improve the mobility of people with disabilities. He graduated in physics in 1990 from the Millersville University (Pennsylvania). He subsequently earned a Master’s degree in Mechanical Engineering at the Massachusetts Institute of Technology (MIT) in 1993 and a PhD in Biophysics at Harvard University in 1998. He then was a postdoctoral fellow in medical devices at MIT. He was Assistant Professor at the Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School. Since 2000, he has been heading the MIT Biomechatronics Group within the Media Lab and has been Co-directing the Lab’s Center for Extreme Bionics since 2014. To bring his inventions to market, Herr founded a spin-off company out of MIT under the name iWalk in 2007, which was relaunched as BionX Medical Technologies Inc. in 2015, and acquired by Ottobock in 2017.

Herr is a world leader and inventor in the field of bionics and biomechanics whose research accomplishments have already made a significant impact on physically challenged people. Herr has produced several groundbreaking products, starting with a computer-controlled artificial knee in 2003, called the Rheo Knee^™ System and commercialized by Össur Inc. He also designed his own bionic lower legs, the world’s first powered ankle-foot prosthesis to emulate the action of a biological leg and, for the first time, provides amputees with a natural gait. The Empower ankle system is now marketed by Ottobock. He is presently working on NeuroEmbodied Design methodology to restore proprioception to amputees. Herr has received major accolades including the Popular Mechanics Breakthrough Leadership Award (2005), the Heinz Award for Technology, the Economy and Employment (2007) and R&D Magazine’s 14th Innovator of the Year Award (2014) and a No Barriers Lifetime Achievement Award at the 2013 No Barriers Summit. His innovations were listed twice among TIME magazine’s Top Ten Inventions (2004; 2007) and which called him “Leader of the Bionic Age” in 2011. His life story has been told in the book Second Ascent: The Story of Hugh Herr (1991) and in the film Ascent: The Story of Hugh Herr, made in 2002 by National Geographic. He is the author and co-author of more than 150 peer-reviewed papers and patents.

Humans throughout history have always sought to mimic the appearance, mobility, functionality, intelligent operation, and thinking processes of biological creatures. Advancements in artificial muscles, artificial intelligence, artificial vision and many other biomimetic related fields are leading to many benefits for humankind. One of the newest among these fields is the artificial muscle, which is the moniker for electroactive polymers. The potential of these materials is enormous and, as challenges are addressed and new effective materials are introduced, capabilities that were considered as science fiction are becoming engineering reality. This paper covers the current state‐of‐the‐art and challenges to make biomimetic robots use artificial muscles.

This study aims to conduct text mining and semantic network analysis of muscle-supportive and posture-corrective wearable robots for the elderly to understand key terms related to the topic and to identify considerations for developing these types of clothing.

The authors searched and identified the key terms wearable robot, muscle-supportive, posture correction and elderly using the text-mining software Textom to extract terms as well as the network analysis software UCINET 6 to process and visualize the relationships among the terms. The authors compared and analyzed the term frequency (TF), the TF-inverse document frequency and the degree centrality of the terms, and the authors visualized and summarized the terms using NetDraw.

The key terms and their relationships in 3–4 groups were identified: wearable robot, muscle-supportive, posture correction and elderly. The authors identified the aspects of designing muscle-supportive and posture-corrective wearable robots for the elderly.

This study contributes to the field of muscle-supportive clothing and wearable robotics by deriving insights into what people are discussing and interested in, and by offering recommendations when developing these types of clothing for the elderly.

The following article is a “Q&A interview” conducted by Joanne Pransky of Industrial Robot journal as a method to impart the combined technological, business and personal experience of a prominent, robotic industry engineer-turned-entrepreneur regarding the evolution, commercialization and challenges of bringing a technological invention to market. The paper aims to discuss these issues.

The interviewee is Jacob Rosen, a Professor of Medical Robotics at the Department of Mechanical and Aerospace Engineering, University of California, Los Angeles (UCLA), where he directs the Bionics Lab. Professor Rosen is also the Director of Surgical Robotics Engineering at the UCLA School of Medicine’s Center for Advanced Surgical and Interventional Technology and has joint appointments at UCLA’s Department of Surgery and UCLA’s Department of Bioengineering. Professor Rosen is the co-founder of the companies Applied Dexterity, ExoSense and SPI. As a pioneer in medical robotics devices and technologies, Professor Rosen describes his unique approaches and philosophies.

Dr Rosen received his BSc degree in Mechanical Engineering, MSc and PhD degrees in Biomedical Engineering from Tel-Aviv University in 1987, 1993 and 1997, respectively. From 1987 to 1992, he served as an officer in the Israeli Defense Forces studying human–machine interfaces. From 1993 to 1997, he was a research associate at Tel-Aviv University, as well as held a position at a startup company developing innovative orthopedic spine/pelvis implants. From 2001-2013, he held faculty positions at the University of Washington and at University of California, Santa Cruz.

Dr Rosen developed several key systems in the field of medical robotics, such as the Blue and the Red Dragon, for minimally invasive surgical skill evaluation; RAVEN, a surgical robotic system for telesurgery; and several generations of upper and lower limb exoskeletons including the Exo-UL7 – a dual arm wearable robotic system. He is a co-author of 100 manuscripts in the field of medical robotics and a co-author and co-editor of two books entitled “Surgical Robotics – Systems, Applications, and Visions” and “Redundancy in Robot Manipulators and Multi-robot systems” published by Springer. Professor Rosen has filed eight different patent applications and also works as an expert witness and consultant on design, patent protection & litigation and malpractice regarding surgical robotics.

Recent requirements for drive systems in robotic technology, mainly for their performance, performance and weight ratio, compactness with minimal internal structure and with the integration of main functional parts, lead to intensive application of new, non‐traditional solutions. One of the possible approaches to a non‐traditional solution of drive systems in robotic technology is the application of pneumatic artificial muscle (PAM). The purpose of this paper is to review the designs and applications of the under‐pressure artificial muscle (UPAM) and the creation of non‐standard modules for robotic technology based on PAM.

Certain part of the disadvantages of an over‐pressure PAM can be solved by the use of an UPAM. As a performance output, UPAM principle guarantees linear movement along the axis with relevant traction force. This UPAM demonstration is evaluated as the drive in mechanic constructions.

Theoretical calculations, which have been performed, as well as experimental tests and evaluations of the model of this muscle have confirmed an agreement with theoretical relationships valid for PAM generally. The module TMPAM with lengthening action element is principally based on the change of input pressure energy, shape and volume change of action element into output mechanical (power, kinetic) energy. The analysis of the results of measurements (set of measurements, four samples of action element) of the given relationships allows to say that the tractive power F and the lift grow with the change of geometric arrangement of the action element in the box of the driving unit. The output parameters of the TMPAM can be regulated by the number of action elements integrated in the unit (creating two‐element and more‐elemnet parallel sets).

The UPAM maintains all advantages of the principle and recent constructions of the PAM, as well as lightness and compactness of the design. The results confirm that this construction principle of the translation modules is suitable mainly for small lifts, lower load and movements, where even, soft motion is required.

On the basis of author's own solutions of the underpressure artificial muscle (UPAM, original patent) and non‐traditional translation module (TMPAM, original design), the paper evaluates and generalizes the findings obtained from the use of PAM in robot construction.

The purpose of this review paper is to address the substantial challenges of the outdated exoskeletons used for rehabilitation and further study the current advancements in this field. The shortcomings and technological developments in sensing the input signals to enable the desired motions, actuation, control and training methods are explained for further improvements in exoskeleton research.

Search platforms such as Web of Science, IEEE, Scopus and PubMed were used to collect the literature. The total number of recent articles referred to in this review paper with relevant keywords is filtered to 143.

Exoskeletons are getting smarter often with the integration of various modern tools to enhance the effectiveness of rehabilitation. The recent applications of bio signal sensing for rehabilitation to perform user-desired actions promote the development of independent exoskeleton systems. The modern concepts of artificial intelligence and machine learning enable the implementation of brain–computer interfacing (BCI) and hybrid BCIs in exoskeletons. Likewise, novel actuation techniques are necessary to overcome the significant challenges seen in conventional exoskeletons, such as the high-power requirements, poor back drivability, bulkiness and low energy efficiency. Implementation of suitable controller algorithms facilitates the instantaneous correction of actuation signals for all joints to obtain the desired motion. Furthermore, applying the traditional rehabilitation training methods is monotonous and exhausting for the user and the trainer. The incorporation of games, virtual reality (VR) and augmented reality (AR) technologies in exoskeletons has made rehabilitation training far more effective in recent times. The combination of electroencephalogram and electromyography-based hybrid BCI is desirable for signal sensing and controlling the exoskeletons based on user intentions. The challenges faced with actuation can be resolved by developing advanced power sources with minimal size and weight, easy portability, lower cost and good energy storage capacity. Implementation of novel smart materials enables a colossal scope for actuation in future exoskeleton developments. Improved versions of sliding mode control reported in the literature are suitable for robust control of nonlinear exoskeleton models. Optimizing the controller parameters with the help of evolutionary algorithms is also an effective method for exoskeleton control. The experiments using VR/AR and games for rehabilitation training yielded promising results as the performance of patients improved substantially.

Robotic exoskeleton-based rehabilitation will help to reduce the fatigue of physiotherapists. Repeated and intention-based exercise will improve the recovery of the affected part at a faster pace. Improved rehabilitation training methods like VR/AR-based technologies help in motivating the subject.

The paper describes the recent methods for signal sensing, actuation, control and rehabilitation training approaches used in developing exoskeletons. All these areas are key elements in an exoskeleton where the review papers are published very limitedly. Therefore, this paper will stand as a guide for the researchers working in this domain.

Limited by the types of sensors, the state information available for musculoskeletal robots with highly redundant, nonlinear muscles is often incomplete, which makes the control face a bottleneck problem. The aim of this paper is to design a method to improve the motion performance of musculoskeletal robots in partially observable scenarios, and to leverage the ontology knowledge to enhance the algorithm’s adaptability to musculoskeletal robots that have undergone changes.

A memory and attention-based reinforcement learning method is proposed for musculoskeletal robots with prior knowledge of muscle synergies. First, to deal with partially observed states available to musculoskeletal robots, a memory and attention-based network architecture is proposed for inferring more sufficient and intrinsic states. Second, inspired by muscle synergy hypothesis in neuroscience, prior knowledge of a musculoskeletal robot’s muscle synergies is embedded in network structure and reward shaping.

Based on systematic validation, it is found that the proposed method demonstrates superiority over the traditional twin delayed deep deterministic policy gradients (TD3) algorithm. A musculoskeletal robot with highly redundant, nonlinear muscles is adopted to implement goal-directed tasks. In the case of 21-dimensional states, the learning efficiency and accuracy are significantly improved compared with the traditional TD3 algorithm; in the case of 13-dimensional states without velocities and information from the end effector, the traditional TD3 is unable to complete the reaching tasks, while the proposed method breaks through this bottleneck problem.

In this paper, a novel memory and attention-based reinforcement learning method with prior knowledge of muscle synergies is proposed for musculoskeletal robots to deal with partially observable scenarios. Compared with the existing methods, the proposed method effectively improves the performance. Furthermore, this paper promotes the fusion of neuroscience and robotics.