Imitation Learning with Limited Actions via Diffusion Planners and Deep Koopman Controllers
<|reference_start|>Imitation Learning with Limited Actions via Diffusion Planners and Deep Koopman Controllers: Recent advances in diffusion-based robot policies have demonstrated significant potential in imitating multi-modal behaviors. However, these approaches typically require large quantities of demonstration data paired with corresponding robot action labels, creating a substantial data collection burden. In this work, we propose a plan-then-control framework aimed at improving the action-data efficiency of inverse dynamics controllers by leveraging observational demonstration data. Specifically, we adopt a Deep Koopman Operator framework to model the dynamical system and utilize observation-only trajectories to learn a latent action representation. This latent representation can then be effectively mapped to real high-dimensional continuous actions using a linear action decoder, requiring minimal action-labeled data. Through experiments on simulated robot manipulation tasks and a real robot experiment with multi-modal expert demonstrations, we demonstrate that our approach significantly enhances action-data efficiency and achieves high task success rates with limited action data.<|reference_end|>
@article{bi2024imitation,
    title={Imitation Learning with Limited Actions via Diffusion Planners and Deep
  Koopman Controllers},
    author={Jianxin Bi, Kelvin Lim, Kaiqi Chen, Yifei Huang, and Harold Soh},
    journal={arXiv preprint arXiv:2410.07584},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.07584},
    primaryClass={cs.RO cs.LG}
}