SteadyTray: 残差強化学習による人型ロボットのトレイ運搬における物体バランス学習
SteadyTray: Learning Object Balancing Tasks in Humanoid Tray Transport via Residual Reinforcement Learning
人型ロボットが歩行中にトレイ上の物体を安定して運ぶための階層型強化学習フレームワークを提案し、歩行制御と物体安定化を分離することで高い成功率と実機でのゼロショット転移を実現した。
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著者: Anlun Huang, Zhenyu Wu, Soofiyan Atar, Yuheng Zhi, Michael Yip
分類: cs.RO
原文アブストラクト
Stabilizing unsecured payloads against the inherent oscillations of dynamic bipedal locomotion remains a critical engineering bottleneck for humanoids in unstructured environments. To solve this, we introduce ReST-RL, a hierarchical reinforcement learning architecture that explicitly decouples locomotion from payload stabilization, evaluated via the SteadyTray benchmark. Rather than relying on monolithic end-to-end learning, our framework integrates a robust base locomotion policy with a dynamic residual module engineered to actively cancel gait-induced perturbations at the end-effector. This architectural separation ensures steady tray transport without degrading the underlying bipedal stability. In simulation, the residual design significantly outperforms end-to-end baselines in gait smoothness and orientation accuracy, achieving a 96.9% success rate in variable velocity tracking and 74.5% robustness against external force disturbances. Successfully deployed on the Unitree G1 humanoid hardware, this modular approach demonstrates highly reliable zero-shot sim-to-real generalization across various objects and external force disturbances.
