浮力支援脚ロボットのシミュレーション・実機転移のための残差物理学習とシステム同定
Residual Physics Learning and System Identification for Sim-to-real Transfer of Policies on Buoyancy Assisted Legged Robots
浮力支援軽量脚ロボットBALLUの実機制御に向け、アクチュエータの非線形ダイナミクスをシステム同定し、強化学習で残差物理をモデル化するEnvMimicを提案。シミュレータ精度を高め、歩行・旋回方策の実機転移を実現した。
著者: Nitish Sontakke, Hosik Chae, Sangjoon Lee, Tianle Huang, Dennis W. Hong, Sehoon Ha
分類: cs.RO, cs.AI
原文アブストラクト
The light and soft characteristics of Buoyancy Assisted Lightweight Legged Unit (BALLU) robots have a great potential to provide intrinsically safe interactions in environments involving humans, unlike many heavy and rigid robots. However, their unique and sensitive dynamics impose challenges to obtaining robust control policies in the real world. In this work, we demonstrate robust sim-to-real transfer of control policies on the BALLU robots via system identification and our novel residual physics learning method, Environment Mimic (EnvMimic). First, we model the nonlinear dynamics of the actuators by collecting hardware data and optimizing the simulation parameters. Rather than relying on standard supervised learning formulations, we utilize deep reinforcement learning to train an external force policy to match real-world trajectories, which enables us to model residual physics with greater fidelity. We analyze the improved simulation fidelity by comparing the simulation trajectories against the real-world ones. We finally demonstrate that the improved simulator allows us to learn better walking and turning policies that can be successfully deployed on the hardware of BALLU.
