ソロからアンサンブルへ：構成可能なマルチエージェント物体操作のための階層フレームワーク
From Solo to Ensemble: A Hierarchical Framework for Composable Multi-Agent Human-Object Interaction
単一エージェントの物体操作スキルを物体指向の運動スキルとして蒸留し、高レベル方策で複数エージェントを協調させる階層フレームワークを提案。
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著者: Zekai Deng, Kangyi Chen, Ye Shi, Jingya Wang
分類: cs.RO
原文アブストラクト
Physics-based human-object interaction has achieved robust single-agent manipulation skills, yet extending them to multi-agent cooperative tasks remains challenging. Existing approaches typically adapt interaction policies through task-specific fine-tuning, which entangles low-level contact-rich execution with high-level coordination and limits reuse across object geometries, interaction types, and team sizes. We propose a hierarchical framework that converts a single-agent HOI policy into a reusable Object-oriented Motion Skill. Specifically, we reinterpret teacher rollouts as object-oriented action supervision by extracting short-horizon object-proxy motions from executed trajectories, and distill task-specific teachers into a low-level skill operating in an Object-oriented Action Space. For downstream tasks, the distilled skill is frozen as a reusable executor, while a high-level policy coordinates multiple agents by generating region-wise object-oriented actions conditioned on the shared object, task goal, agent states, and local manipulation regions. This formulation shifts multi-agent HOI learning from direct contact-rich full-body control to compact object-level proxy-motion coordination. Experiments on diverse HOI tasks show that the distilled Object-oriented Motion Skill supports robust proxy-motion execution and enables composable policy learning across different interaction types, object geometries, and team sizes.