Flow Matching Imitation Learning for Multi-Support Manipulation
<|reference_start|>Flow Matching Imitation Learning for Multi-Support Manipulation: 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/<|reference_end|>
@article{rouxel2024flow,
    title={Flow Matching Imitation Learning for Multi-Support Manipulation},
    author={Quentin Rouxel (Inria), Andrea Ferrari (Inria), Serena Ivaldi (Inria),
  Jean-Baptiste Mouret (Inria)},
    journal={arXiv preprint arXiv:2407.12381},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.12381},
    primaryClass={cs.RO}
}