OmniContact: 接触フローによるメタスキルの連鎖で汎用ヒューマノイド移動操作を実現
OmniContact: Chaining Meta-Skills via Contact Flow for Generalizable Humanoid Loco-Manipulation
ヒューマノイドの長期的な移動操作タスクを、接触フローという表現を介してメタスキルを階層的に連鎖させるフレームワークを提案し、高い成功率と自律的な失敗回復を実現した。
詳しい要約
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著者: Runyi Yu, Xiaoyi Lin, Ji Ma, Yinhuai Wang, Koukou Luo, Jiahao Ji, Huayi Wang, Wenjia Wang, Runhan Zhang, Ping Tan, Ting Wu, Ruoli Dai, Qifeng Chen, Lei Han
分類: cs.RO
原文アブストラクト
Learning long-horizon humanoid loco-manipulation poses a dual challenge: it requires not only the robust execution of meta-skills but also their seamless, closed-loop chaining equipped with autonomous recovery. Existing approaches remain limited: explicit humanoid-object interaction representations offer precision but are notoriously difficult for high-level planning, whereas implicit skill embeddings are compact but lack the interpretability required for reliable composition. We propose \ours, a hierarchical framework centered on \textbf{contact flow (CF)}, a compact representation consisting of key body trajectories and time-series binary contact signals. Leveraging this shared interface, our low-level policy \textbf{CF-Track} learns a unified library of loco-manipulation skills, while our high-level module \textbf{CF-Gen} heuristically synthesizes future contact-flow sequences. To support this setting, we additionally collect the OmniContact dataset, a MoCap-based HOI corpus for humanoid loco-manipulation (Appendix~\ref{sec:dataset}). Together, they enable robust execution, autonomous failure recovery, and flexible composition of meta-skills for long-horizon tasks. Experiments show that OmniContact achieves \(98.7\%\) success on \textit{Carry Box} and \(76.5\%\) on \textit{Push-Stack Boxes}, outperforming prior baselines by average margins of \(40.9\%\) in meta-skill and \(66.5\%\) in skill chaining. Besides, our framework naturally integrates with VLMs for semantic task decomposition, enabling complex, semantically grounded loco-manipulation behaviors, such as arranging scattered boxes into a heart shape.
