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arXiv · 2306.09532

Hierarchical Planning and Control for Box Loco-Manipulation

Zhaoming Xie·Jonathan Tseng·Sebastian Starke·Michiel van de Panne·C. Karen Liu
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Abstract

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.

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Zhaoming Xie, Jonathan Tseng, Sebastian Starke, Michiel van de Panne, C. Karen Liu. 2023-06-15. Hierarchical Planning and Control for Box Loco-Manipulation. https://arxiv.org/abs/2306.09532

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