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Since the introduction of DeepMimic [Peng et al. 2018], subsequent researchhas focused on expanding the repertoire of simulated motions across variousscenarios. In this study, we propose an alternative approach for this goal, adeep reinforcement learning method based on the simulation of asingle-rigid-body character. Using the centroidal dynamics model (CDM) toexpress the full-body character as a single rigid body (SRB) and training apolicy to track a reference motion, we can obtain a policy that is capable ofadapting to various unobserved environmental changes and controller transitionswithout requiring any additional learning. Due to the reduced dimension ofstate and action space, the learning process is sample-efficient. The finalfull-body motion is kinematically generated in a physically plausible way,based on the state of the simulated SRB character. The SRB simulation isformulated as a quadratic programming (QP) problem, and the policy outputs anaction that allows the SRB character to follow the reference motion. Wedemonstrate that our policy, efficiently trained within 30 minutes on anultraportable laptop, has the ability to cope with environments that have notbeen experienced during learning, such as running on uneven terrain or pushinga box, and transitions between learned policies, without any additionallearning.

Adaptive Tracking of a Single-Rigid-Body Character in Various Environments