箱の運搬を伴う移動操作のための階層的計画と制御
Hierarchical Planning and Control for Box Loco-Manipulation
物理シミュレーション上のバーチャルヒューマンが、計画・拡散モデル・強化学習を組み合わせた階層的制御により、雑然とした環境で箱を並べ替えるタスクを実現した。
著者: Zhaoming Xie, Jonathan Tseng, Sebastian Starke, Michiel van de Panne, C. Karen Liu
分類: cs.RO, cs.GR
原文アブストラクト
Humans perform everyday tasks using a combination of locomotion and manipulation skills. Building a system that can handle both skills is essential to creating virtual humans. We present a physically-simulated human capable of solving box rearrangement tasks, which requires a combination of both skills. We propose a hierarchical control architecture, where each level solves the task at a different level of abstraction, and the result is a physics-based simulated virtual human capable of rearranging boxes in a cluttered environment. The control architecture integrates a planner, diffusion models, and physics-based motion imitation of sparse motion clips using deep reinforcement learning. Boxes can vary in size, weight, shape, and placement height. Code and trained control policies are provided.
