Simulating Motion Success With Muscle Deficiency in a Musculoskeletal Model Using Reinforcement Learning

Humans possess an extraordinary ability to execute complex movements, captivating the attention of researchers who strive to develop methods for simulating these actions within a physics-based environment. Motion Capture data stands out as a crucial tool among the proven approaches to tackle this challenge. In this research, we explore the effects of decreased muscle force on the body\u27s capacity to perform various tasks, ranging from simple walking to executing complex jumping jacks. Through a systematic reduction of the allowed force applied to individual muscles or muscle groups, we aim to identify the threshold at which the body\u27s muscles tolerate the deficiency before the motion becomes unattainable. Additionally, we seek to analyze how the model adapts its muscle activation levels, in scenarios where the motion is still achievable despite the applied deficiencies

Bio-inspired Tensegrity Soft Modular Robots

In this paper, we introduce a design principle to develop novel soft modular robots based on tensegrity structures and inspired by the cytoskeleton of living cells. We describe a novel strategy to realize tensegrity structures using planar manufacturing techniques, such as 3D printing. We use this strategy to develop icosahedron tensegrity structures with programmable variable stiffness that can deform in a three-dimensional space. We also describe a tendon-driven contraction mechanism to actively control the deformation of the tensegrity mod-ules. Finally, we validate the approach in a modular locomotory worm as a proof of concept.Comment: 12 pages, 7 figures, submitted to Living Machine conference 201

DEVELOPMENT OF A SOFT PNEUMATIC ACTUATOR FOR MODULAR ROBOTIC MECHANISMS

Soft robotics is a widely and rapidly growing field of research today. Soft pneumatic actuators, as a fundamental element in soft robotics, have gained huge popularity and are being employed for the development of soft robots. During the last decade, a variety of hyper-elastic robotic systems have been realized. As the name suggests, such robots are made up of soft materials, and do not have any underlying rigid mechanical structure. These robots are actuated employing various methods like pneumatic, electroactive, jamming etc. Generally, in order to achieve a desired mechanical response to produce required actuation or manipulation, two or more materials having different stiffness are utilized to develop a soft robot. However, this method introduces complications in the fabrication process as well as in further design flexibility and modifications. The current work presents a design scheme of a soft robotic actuator adapting an easier fabrication approach, which is economical and environment friendly as well. The purpose is the realization of a soft pneumatic actuator having functional ability to produce effective actuation, and which is further employable to develop modular and scalable mechanisms. That infers to scrutinize the profile and orientation of the internal actuation cavity and the outer shape of viii the actuator. Utilization of a single material for this actuator has been considered to make this design scheme convenient. A commercial silicone rubber was selected which served for an economical process both in terms of the cost as well as its accommodating fabrication process through molding. In order to obtain the material behavior, \u2018Ansys Workbench 17.1 R \u2019 has been used. Cubic outline for the actuator aided towards the realization of a body shape which can easily be engaged for the development of modular mechanisms employing multiple units. This outer body shape further facilitates to achieve the stability and portability of the actuator. The soft actuator has been named \u2018Soft Cubic Module\u2019 based on its external cubic shape. For the internal actuation cavity design, various shapes, such as spherical, elliptical and cylindrical, were examined considering their different sizes and orientations within the cubic module. These internal cavities were simulated in order to achieve single degree of freedom actuation. That means, only one face of the cube is principally required to produce effective deformation. \u2018Creo Perametric 3.0 M 130\u2019 has been used to design the model and to evaluate the performance of actuation cavities in terms of effective deformation and the resulting von-mises stress. Out of the simulated profiles, cylindrical cavity with desired outcomes has been further considered to design the soft actuator. \u2018Ansys Workbench 17.1 R \u2019 environment was further used to assess the performance of cylindrical actuation cavity. Evaluation in two different simulation environments helped to validate the initially achieved results. The developed soft cubic actuator was then employed to develop different mechanisms in a single unit configuration as well as multi-unit robotic system developments. This design scheme is considered as the first tool to investigate its capacity to perform certain given tasks in various configurations. Alongside its application as a single unit gripper and a two unit bio-mimetic crawling mechanism, this soft actuator has been employed to realize a four degree ix of freedom robotic mechanism. The formation of this primitive soft robotic four axis mechanism is being further considered to develop an equivalent mechanism similar to the well known Stewart platform, with advantages of compactness, simpler kinematics design, easier control, and lesser cost. Overall, the accomplished results indicate that the design scheme of Soft Cubic Module is helpful in realizing a simple and cost-effective soft pneumatic actuator which is modular and scalable. Another favourable point of this scheme is the use of a single material with convenient fabrication and handling

Breathing Life Into Biomechanical User Models

Forward biomechanical simulation in HCI holds great promise as a tool for evaluation, design, and engineering of user interfaces. Although reinforcement learning (RL) has been used to simulate biomechanics in interaction, prior work has relied on unrealistic assumptions about the control problem involved, which limits the plausibility of emerging policies. These assumptions include direct torque actuation as opposed to muscle-based control; direct, privileged access to the external environment, instead of imperfect sensory observations; and lack of interaction with physical input devices. In this paper, we present a new approach for learning muscle-actuated control policies based on perceptual feedback in interaction tasks with physical input devices. This allows modelling of more realistic interaction tasks with cognitively plausible visuomotor control. We show that our simulated user model successfully learns a variety of tasks representing different interaction methods, and that the model exhibits characteristic movement regularities observed in studies of pointing. We provide an open-source implementation which can be extended with further biomechanical models, perception models, and interactive environments.publishedVersio

Temporal-Difference Learning to Assist Human Decision Making during the Control of an Artificial Limb

In this work we explore the use of reinforcement learning (RL) to help with human decision making, combining state-of-the-art RL algorithms with an application to prosthetics. Managing human-machine interaction is a problem of considerable scope, and the simplification of human-robot interfaces is especially important in the domains of biomedical technology and rehabilitation medicine. For example, amputees who control artificial limbs are often required to quickly switch between a number of control actions or modes of operation in order to operate their devices. We suggest that by learning to anticipate (predict) a user's behaviour, artificial limbs could take on an active role in a human's control decisions so as to reduce the burden on their users. Recently, we showed that RL in the form of general value functions (GVFs) could be used to accurately detect a user's control intent prior to their explicit control choices. In the present work, we explore the use of temporal-difference learning and GVFs to predict when users will switch their control influence between the different motor functions of a robot arm. Experiments were performed using a multi-function robot arm that was controlled by muscle signals from a user's body (similar to conventional artificial limb control). Our approach was able to acquire and maintain forecasts about a user's switching decisions in real time. It also provides an intuitive and reward-free way for users to correct or reinforce the decisions made by the machine learning system. We expect that when a system is certain enough about its predictions, it can begin to take over switching decisions from the user to streamline control and potentially decrease the time and effort needed to complete tasks. This preliminary study therefore suggests a way to naturally integrate human- and machine-based decision making systems.Comment: 5 pages, 4 figures, This version to appear at The 1st Multidisciplinary Conference on Reinforcement Learning and Decision Making, Princeton, NJ, USA, Oct. 25-27, 201

Converting Biomechanical Models from OpenSim to MuJoCo

OpenSim is a widely used biomechanics simulator with several anatomically accurate human musculo-skeletal models. While OpenSim provides useful tools to analyse human movement, it is not fast enough to be routinely used for emerging research directions, e.g., learning and simulating motor control through deep neural networks and Reinforcement Learning (RL). We propose a framework for converting OpenSim models to MuJoCo, the de facto simulator in machine learning research, which itself lacks accurate musculo-skeletal human models. We show that with a few simple approximations of anatomical details, an OpenSim model can be automatically converted to a MuJoCo version that runs up to 600 times faster. We also demonstrate an approach to computationally optimize MuJoCo model parameters so that forward simulations of both simulators produce similar results.Comment: Submitted to 5th International Conference on NeuroRehabilitation (ICNR2020

A novel plasticity rule can explain the development of sensorimotor intelligence

Grounding autonomous behavior in the nervous system is a fundamental challenge for neuroscience. In particular, the self-organized behavioral development provides more questions than answers. Are there special functional units for curiosity, motivation, and creativity? This paper argues that these features can be grounded in synaptic plasticity itself, without requiring any higher level constructs. We propose differential extrinsic plasticity (DEP) as a new synaptic rule for self-learning systems and apply it to a number of complex robotic systems as a test case. Without specifying any purpose or goal, seemingly purposeful and adaptive behavior is developed, displaying a certain level of sensorimotor intelligence. These surprising results require no system specific modifications of the DEP rule but arise rather from the underlying mechanism of spontaneous symmetry breaking due to the tight brain-body-environment coupling. The new synaptic rule is biologically plausible and it would be an interesting target for a neurobiolocal investigation. We also argue that this neuronal mechanism may have been a catalyst in natural evolution.Comment: 18 pages, 5 figures, 7 video