Learning Ergonomic Control in Human–Robot Symbiotic Walking

This article presents an imitation learning strategy for extracting ergonomically safe control policies in physical human–robot interaction scenarios. The presented approach seeks to proactively reduce the risk of injuries and musculoskeletal disorders by anticipating the ergonomic effects of a robot's actions on a human partner, e.g., how the ankle angle of a prosthesis affects future knee torques of the user. To this end, we extend ensemble Bayesian interaction primitives to enable the prediction of latent biomechanical variables. This methodology yields a reactive control strategy, which we evaluate in an assisted walking task with a robotic lower limb prosthesis. Building upon the learned interaction primitives, we also present a model-predictive control (MPC) strategy that actively steers the human–robot interaction toward ergonomic and safe movement regimes. We compare the introduced control strategies and highlight the framework's ability to generate ergonomic, biomechanically safe assistive prosthetic control. A rich analysis of constrained MPC shows a 20× reduction in the effects of large perturbations on prosthetic control system. We empirically demonstrate a 16% reduction in vertical knee reaction forces in real-world jumping experiments utilizing our control methodology and examine other optimal control strategies in simulated walking experiments.