Robot See Robot Do: Imitating Articulated Object Manipulation with Monocular 4D Reconstruction
<|reference_start|>Robot See Robot Do: Imitating Articulated Object Manipulation with Monocular 4D Reconstruction: Humans can learn to manipulate new objects by simply watching others; providing robots with the ability to learn from such demonstrations would enable a natural interface specifying new behaviors. This work develops Robot See Robot Do (RSRD), a method for imitating articulated object manipulation from a single monocular RGB human demonstration given a single static multi-view object scan. We first propose 4D Differentiable Part Models (4D-DPM), a method for recovering 3D part motion from a monocular video with differentiable rendering. This analysis-by-synthesis approach uses part-centric feature fields in an iterative optimization which enables the use of geometric regularizers to recover 3D motions from only a single video. Given this 4D reconstruction, the robot replicates object trajectories by planning bimanual arm motions that induce the demonstrated object part motion. By representing demonstrations as part-centric trajectories, RSRD focuses on replicating the demonstration's intended behavior while considering the robot's own morphological limits, rather than attempting to reproduce the hand's motion. We evaluate 4D-DPM's 3D tracking accuracy on ground truth annotated 3D part trajectories and RSRD's physical execution performance on 9 objects across 10 trials each on a bimanual YuMi robot. Each phase of RSRD achieves an average of 87% success rate, for a total end-to-end success rate of 60% across 90 trials. Notably, this is accomplished using only feature fields distilled from large pretrained vision models -- without any task-specific training, fine-tuning, dataset collection, or annotation. Project page: https://robot-see-robot-do.github.io<|reference_end|>
@article{kerr2024robot,
    title={Robot See Robot Do: Imitating Articulated Object Manipulation with
  Monocular 4D Reconstruction},
    author={Justin Kerr, Chung Min Kim, Mingxuan Wu, Brent Yi, Qianqian Wang, Ken
  Goldberg, Angjoo Kanazawa},
    journal={arXiv preprint arXiv:2409.18121},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.18121},
    primaryClass={cs.RO cs.CV}
}