2022/10/03 by Yuni Fuchioka, Fuchioka, Yuni, Zhaoming Xie +3 · 4 citations
Biochemistry, Genetics and Molecular Biology · Engineering · #FOS: Computer and information sciences #Real-time simulation and control systems #Robotic Locomotion and Control #Robotics (cs.RO) #Zebrafish Biomedical Research Applications
paper · pdf · doi:10.48550/arxiv.2210.01247
openalex publication_date 2022/10/03 · openalex created_date 2022/10/06 · openalex updated_date 2026/07/28
Reinforcement Learning (RL) has seen many recent successes for quadruped robot control. The imitation of reference motions provides a simple and powerful prior for guiding solutions towards desired solutions without the need for meticulous reward design. While much work uses motion capture data or hand-crafted trajectories as the reference motion, relatively little work has explored the use of reference motions coming from model-based trajectory optimization. In this work, we investigate several design considerations that arise with such a framework, as demonstrated through four dynamic behaviours: trot, front hop, 180 backflip, and biped stepping. These are trained in simulation and transferred to a physical Solo 8 quadruped robot without further adaptation. In particular, we explore the space of feed-forward designs afforded by the trajectory optimizer to understand its impact on RL learning efficiency and sim-to-real transfer. These findings contribute to the long standing goal of producing robot controllers that combine the interpretability and precision of model-based optimization with the robustness that model-free RL-based controllers offer.