EquiMus: Energy-Equivalent Dynamic Modeling and Simulation of Musculoskeletal Robots Driven by Linear Elastic Actuators

Abstract

Dynamic modeling and control are critical to unlocking soft robots’ potential, yet remain challenging due to complex constitutive behaviors and real-world operating conditions. Bio-inspired musculoskeletal robots, which integrate rigid skeletons with soft actuators, combine the advantages of heavy load-bearing capacity and inherent flexibility. Although actuation dynamics has been studied through experimental methods and surrogate models, accurate and effective modeling and simulation still pose a significant challenge when soft actuators are applied at a large scale, especially in hybrid rigid-soft robots with continuously distributed mass, kinematic loops and diverse motion modes. To address these challenges, we propose EquiMus, an energy-equivalent dynamic modeling and MuJoCo-based simulation for musculoskeletal rigid--soft hybrid robots with linear elastic actuators. The equivalence and effectiveness are proven in detail and examined through simulations and real experiments on a bionic robotic leg. EquiMus further demonstrates utility for downstream tasks, including controller design and learning-based control.