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Pré-Publication, Document De Travail Année : 2024

Flow Matching Imitation Learning for Multi-Support Manipulation

Résumé

Humanoid robots could benefit from using their upper bodies for support contacts, enhancing their workspace, stability, and ability to perform contact-rich and pushing tasks. In this paper, we propose a unified approach that combines an optimization-based multi-contact whole-body controller with Flow Matching, a recently introduced method capable of generating multi-modal trajectory distributions for imitation learning. In simulation, we show that Flow Matching is more appropriate for robotics than Diffusion and traditional behavior cloning. On a real full-size humanoid robot (Talos), we demonstrate that our approach can learn a whole-body non-prehensile box-pushing task and that the robot can close dishwasher drawers by adding contacts with its free hand when needed for balance. We also introduce a shared autonomy mode for assisted teleoperation, providing automatic contact placement for tasks not covered in the demonstrations. Full experimental videos are available at: https://hucebot.github.io/flow_multisupport_website/
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Dates et versions

hal-04650144 , version 1 (16-07-2024)

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Quentin Rouxel, Andrea Ferrari, Serena Ivaldi, Jean-Baptiste Mouret. Flow Matching Imitation Learning for Multi-Support Manipulation. 2024. ⟨hal-04650144⟩

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