CARO: 接触非依存の残差観測によるゼロショット堅牢な四脚歩行
CARO: Contact-Agnostic Residual Observation for Zero-Shot Robust Quadruped Locomotion
四脚ロボットの歩行制御において、接触情報やトルクセンサーを使わずに、固定ベースの力学モデルから得られる残差観測を利用して、外乱や動的変化に対する堅牢性を向上させる手法を提案した。
著者: Zihan Yang, Shixuan Han, Kexin Guo, Xiang Yu
分類: cs.RO
原文アブストラクト
We propose CARO, a contact-agnostic residual observation framework for policy adaptation. CARO embeds a fixed-base Euler--Lagrange model into the reinforcement learning control loop and constructs a torque-level residual observation without requiring torque sensors, explicit contact estimation, or vision-based measurements of the floating-base position and linear velocity. A disturbance observer extracts a structured signal representing dynamics mismatch, while the policy learns to exploit this feedback for online adaptation. CARO is trained under the same terrain, command, and domain-randomization conditions as the nominal policy, without specialized disturbance curricula or additional adaptation supervision. Nevertheless, it achieves substantially improved zero-shot robustness in simulation and sim-to-real transfer tasks involving out-of-distribution payloads, center-of-mass shifts, terrain geometries, abrupt dynamics changes, and elevated-platform landings.