SHOES: 脚式ロボットのためのオンポリシー技能拡張によるSim-to-realハードウェア適応
SHOES: Sim-to-real Hardware-adaptation via On-policy Expansion of Skills for Legged Robots
靴による脚部形状変化に適応するため、シミュレーションで訓練した歩行方策を凍結し、軽量な変調モジュールのみを実機で微調整するサンプル効率の良い枠組みを提案。
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著者: Kota Tsuruhara, Tsung-Yuan Chen, Kento Kawaharazuka
分類: cs.RO
原文アブストラクト
Humans expand their locomotion capability by wearing appropriate footwear, such as hiking boots for mountains or skates for ice. Footwear changes the ground-contact interface and enables traversal of otherwise difficult terrain. In contrast, locomotion controllers are typically trained for a fixed morphology, and their performance can degrade when shoes alter foot geometry, mass, and friction. We propose SHOES, a sample-efficient framework for adapting locomotion policies to footwear-induced morphology changes. SHOES freezes a simulation-trained nominal policy and fine-tunes only lightweight, identity-initialized modulation modules on the robot, preserving the initial behavior while enabling efficient hardware adaptation. We also introduce SafeBox, a workspace-aware waypoint sampler that constrains online adaptation within a user-defined workspace. The simulation results with a shoe-equipped Unitree Go2 show that SHOES substantially recovers locomotion performance after footwear-induced morphology changes using limited additional adaptation. Under the same adaptation budget, SHOES outperforms FiLM-only, Linear Adapter-only, and residual-RL baselines. Real-world experiments on a Unitree Go2 fitted with human shoes further evaluate the effectiveness of SHOES for hardware deployment.
