Least action principles and their application to constrained and task-level problems in robotics and biomechanics

International audienceLeast action principles provide an insightful starting point from which problems involving constraints and task-level objectives can be addressed. In this paper, the principle of least action is first treated with regard to holonomic constraints in multibody systems. A variant of this, the principle of least curvature or straightest path, is then investigated in the context of geodesic paths on constrained motion manifolds. Subsequently, task space descriptions are addressed and the operational space approach is interpreted in terms of least action. Task-level control is then applied to the problem of cost minimization. Finally, task-level optimization is formulated with respect to extremizing an objective criterion, where the criterion is interpreted as the action of the system. Examples are presented which illustrate these approaches

Diffusion Inertial Poser: Human Motion Reconstruction from Arbitrary Sparse IMU Configurations

Motion capture from a limited number of inertial measurement units (IMUs) has important applications in health, human performance, and virtual reality. Real-world limitations and application-specific goals dictate different IMU configurations (i.e., number of IMUs and chosen attachment body segments), trading off accuracy and practicality. Although recent works were successful in accurately reconstructing whole-body motion from six IMUs, these systems only work with a specific IMU configuration. Here we propose a single diffusion generative model, Diffusion Inertial Poser (DiffIP), which reconstructs human motion in real-time from arbitrary IMU configurations. We show that DiffIP has the benefit of flexibility with respect to the IMU configuration while being as accurate as the state-of-the-art for the commonly used six IMU configuration. Our system enables selecting an optimal configuration for different applications without retraining the model. For example, when only four IMUs are available, DiffIP found that the configuration that minimizes errors in joint kinematics instruments the thighs and forearms. However, global translation reconstruction is better when instrumenting the feet instead of the thighs. Although our approach is agnostic to the underlying model, we built DiffIP based on physiologically realistic musculoskeletal models to enable use in biomedical research and health applications

Unmanned Multiple Exploratory Probe System (MEPS) for Mars observation. Volume 2: Calculations and derivations

This volume of the final report on the unmanned Multiple Exploratory Probe System (MEPS) details all calculations, derivations, and computer programs that support the information presented in the first volume

Unmanned Multiple Exploratory Probe System (MEPS) for Mars observation. Volume 1: Trade analysis and design

This report presents the unmanned Multiple Exploratory Probe Systems (MEPS), a space vehicle designed to observe the planet Mars in preparation for manned missions. The options considered for each major element are presented as a trade analysis, and the final vehicle design is defined

Optical inhibition of motor nerve and muscle activity

Introduction: There is no therapeutic approach that provides precise and rapidly reversible inhibition of motor nerve and muscle activity for treatment of spastic hypertonia. Methods: We used optogenetics to demonstrate precise and rapidly reversible light-mediated inhibition of motor nerve and muscle activity in vivo in transgenic Thy1::eNpHR2.0 mice. Results: We found optical inhibition of motor nerve and muscle activity to be effective at all muscle force amplitudes and determined that muscle activity can be modulated by changing light pulse duration and light power density. Conclusions: This demonstration of optical inhibition of motor nerves is an important advancement toward novel optogenetics-based therapies for spastic hypertonia.Stanford University (Stanford Bio-X Interdisciplinary Initiatives Award)Stanford University (National Institutes of Health Graduate Training Program in Biotechnology grant)W. M. Keck Foundation (grant)National Institutes of Health (U.S.) (NIH grant R01NS080954