Uncertainty-Aware Ankle Exoskeleton Control
Lower limb exoskeletons show promise to assist human movement, but their utility is limited by controllers designed for discrete, predefined actions in controlled environments, restricting their real-world applicability. We present an uncertainty-aware control framework that enables ankle exoskeletons to operate safely across diverse scenarios by automatically disengaging when encountering unfamiliar movements. Our approach uses an uncertainty estimator to classify movements as similar (in-distribution) or different (out-of-distribution) relative to actions in the training set. We evaluated three architectures (model ensembles, autoencoders, and generative adversarial networks) on an offline dataset and tested the strongest performing architecture (ensemble of gait phase estimators) online. The online test demonstrated the ability of our uncertainty estimator to turn assistance on and off as the user transitioned between in-distribution and out-of-distribution tasks (F1: 89.2). This new framework provides a path for exoskeletons to safely and autonomously support human movement in unstructured, everyday environments.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Design and Biomechanical Evaluation of a Lightweight Low-Complexity Soft Bilateral Ankle Exoskeleton
Many people could benefit from exoskeleton assistance during gait, for either medical or nonmedical purposes. But exoskeletons bring added mass and structure, which in turn require compensating for. In this work, we pres…
Gait Asymmetry from Unilateral Weakness and Improvement With Ankle Assistance: a Reinforcement Learning based Simulation Study
Unilateral muscle weakness often leads to asymmetric gait, disrupting interlimb coordination and stance timing. This study presents a reinforcement learning (RL) based musculoskeletal simulation framework to (1) quantify…
Reinforcement LearningA Novel Robust Control Method Combining DNN-Based NMPC Approximation and PI Control: Application to Exoskeleton Squat Movements
Nonlinear Model Predictive Control (NMPC) is a precise controller, but its heavy computational load often prevents application in robotic systems. Some studies have attempted to approximate NMPC using deep neural network…
Deep RL- Tuned Mo del-Free Adaptive Control for Lower-Limb Exoskeletons During Sit-to-Stand Transitions
Sit-to-stand (STS) transitions impose significant joint-loading demands on elderly individuals, making them a primary target for lower-limb exoskeleton assistance. However, accurate trajectory tracking during STS is chal…
Reinforcement LearningMusculoskeletal Motion Imitation for Learning Personalized Exoskeleton Control Policy in Impaired Gait
Designing generalizable control policies for lower-limb exoskeletons remains fundamentally constrained by exhaustive data collection or iterative optimization procedures, which limit accessibility to clinical populations…
Reinforcement Learning