Model-Based Reinforcement Learning Exploits Passive Body Dynamics for High-Performance Biped Robot Locomotion

arXiv cs.RO / 4/17/2026

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Key Points

The paper studies how exploiting a biped robot’s passive body dynamics (e.g., springs) can enable walking and running via model-based deep reinforcement learning.
Two simulation models were compared—one with passive elements and one without—and the passive model’s training behavior was strongly governed by the system’s attractor dynamics.
Although the passive model’s trajectories converged quickly to limit cycles, achieving high rewards took longer than in the non-passive setup.
The resulting locomotion learned with attractor-driven training was more robust and energy-efficient, showing an advantage of stable limit-cycle behavior from body-ground interactions.
The authors argue the findings support the importance of incorporating passive physical properties for future embodied AI systems.

Abstract

Embodiment is a significant keyword in recent machine learning fields. This study focused on the passive nature of the body of a biped robot to generate walking and running locomotion using model-based deep reinforcement learning. We constructed two models in a simulator, one with passive elements (e.g., springs) and the other, which is similar to general humanoids, without passive elements. The training of the model with passive elements was highly affected by the attractor of the system. This lead that although the trajectories quickly converged to limit cycles, it took a long time to obtain large rewards. However, thanks to the attractor-driven learning, the acquired locomotion was robust and energy-efficient. The results revealed that robots with passive elements could efficiently acquire high-performance locomotion by utilizing stable limit cycles generated through dynamic interaction between the body and ground. This study demonstrates the importance of implementing passive properties in the body for future embodied AI.