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The purpose of this paper is to apply virtual agonist–antagonist mechanisms (VAAMs) to robot joint control allowing for muscle-like functions and variably compliant joint motions. Biological muscles of animals have a surprising variety of functions, i.e. struts, springs and brakes.

Each joint is driven by a pair of VAAMs (i.e. passive components). The muscle-like functions as well as the variable joint compliance are simply achieved by tuning the damping coefficient of the VAAM.

With the VAAM, variably compliant joint motions can be produced without mechanically bulky and complex mechanisms or complex force/toque sensing at each joint. Moreover, through tuning the damping coefficient of the VAAM, the functions of the VAAM are comparable to biological muscles.

The model (i.e. VAAM) provides a way forward to emulate muscle-like functions that are comparable to those found in physiological experiments of biological muscles. Based on these muscle-like functions, the robotic joints can easily achieve variable compliance that does not require complex physical components or torque sensing systems, thereby capable of implementing the model on small-legged robots driven by, for example, standard servo motors. Thus, the VAAM minimizes hardware and reduces system complexity. From this point of view, the model opens up another way of simulating muscle behaviors on artificial machines.

The VAAM can be applied to produce variable compliant motions of a high degree-of-freedom robot. Only relying on force sensing at the end effector, this application is easily achieved by changing coefficients of the VAAM. Therefore, the VAAM can reduce economic cost on mechanical and sensing components of the robot, compared to traditional methods (e.g. artificial muscles).

The purpose of this study was to examine the influence of participating in an eight‐week physical training (ie. balance or weight training) on psychosocial outcomes for independently living healthy older adults. Eighteen older adults (65 years old or older) voluntarily participated in this study. Participants were randomly and evenly distributed in three different groups such as balance, weight or control group; six participants in each. Fear of falling and social activity levels were statistically tested by evaluating questionnaires validated in previous studies. Psychological factors improved in all groups after eight weeks (P < 0.05). Social interaction levels did not improve in any of the three groups, although all participants exhibited improvements in being physically independent (P < 0.05). Results suggested that being physically active as well as being socially active could result in being less fearful of falls, more confident of leaving residency, being more independent, and being more active.

The purpose of this paper is to make compliant training control of exoskeleton for ankle joint with electromyograph (EMG)-torque interface.

A virtual compliant mapping which is modeled by mass-spring-damper system is incorporated into the whole system at the reference input. The EMG-torque interface contains both data acquisition and torque estimator/predictor, and extreme learning machine is utilized for joint torque estimation/prediction from multiple channels of EMG signals.

The reference ankle joint angle to follow is produced from the compliance mapping whose input is the measured/predicted torque on healthy subjects. The control system works well with the desired angle to track. In the actuation level, the input torque to drive the ankle exoskeleton is less than the actual torque of the subject(s). This may have positive influence on diminishing overshoot of input torque from motors and protect the actuators. The torque prediction and final tracking control performance demonstrate the efficiency of the presented architecture.

This work can be beneficial to compliant training of ankle exoskeleton system for pilots and enhance current training control module in rehabilitation.

The purpose of this paper is to discuss the Daisyworld “parable” advanced by James Lovelock to account for the origin of global homeostasis, and to relate it to another type of model advanced in a physiological context.

The relevance of the Daisyworld model is examined in more detail than in an earlier discussion, and the relationship to physiological rein control is considered.

Both types of model exhibit effective and robust control and there is good reason to believe they usefully model forms of biological regulation.

There are implications for theories of global homeostasis and for physiology. A computer program modelling Daisyworld is made available.

The Javascript program that can be accessed online is new, though based on the earlier work of Lovelock and Watson. Much of the treatment depends on the work of Saunders et al.

The purpose of this chapter is to uplevel the two-by-two binary matrix of differences to a three-by-three cross-referential one, in order to inquire into the nature and movement of Spirit within us at different levels of analysis. Our design is a non-liner, post-structural inquiry. The implications of our findings include an invitation to co-explore the muddled middle area of relationship, such as Synthesis − between Thesis and Antithesis − and Breath − between Mind and Body, individually and collectively as a metaphorical set to explore Spirit as the relationship between Self and Other. The social implications reveal more possible interpretations than currently assumed, beyond the label of enemy and the erection of lines of containment, in the relational space between concepts and among people. Our essay is original, in its playful and post-modern interface of fact and fiction, mind and body, self and other, and spirit and breath.

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.

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.

This case challenges students to apply financial reporting concepts pertaining, most notably, to liabilities and expenses in a specific corporate situation. In the context of an interesting, but noncomplex, technical accounting issue, students debate the best way for Adenosine Therapeutics to present its compensation arrangements in its financial statements. In addition, this case also prompts students to debate the best way for a growing company, with cash constraints, to provide incentive and maintain top employees.

The purpose of this paper is to present the control methods of the exoskeleton robotic arm for stroke rehabilitation.

The robotic arm is driven by the pneumatic muscle actuators. The control system provides independent control for the robot. The joint axes of the robotic arm are arranged to mimic the natural upper limb workspace.

Findings are the classification of training modes and control methods of rehabilitation training, and the characters of both the instant spasm and the sustaining one.

This paper is a preliminary step in the control system and the kinematical characteristics should be analyzed to achieve high precision of movement.

Based on a hierarchical structure, the control system allows the execution of sequence of switching control methods: position, force, force/position and impedance. Patient‐active‐robot‐passive and patient‐passive‐robot‐active (PPRA) training modes are also presented in this paper. In PPRA mode, the robotic arm can provide pre‐specified resistances on the patient's arm. Both instant and sustaining spasms are taken into account for safety.