Diffusion-based learning of contact plans for agile locomotion
<|reference_start|>Diffusion-based learning of contact plans for agile locomotion: Legged robots have become capable of performing highly dynamic maneuvers in the past few years. However, agile locomotion in highly constrained environments such as stepping stones is still a challenge. In this paper, we propose a combination of model-based control, search, and learning to design efficient control policies for agile locomotion on stepping stones. In our framework, we use nonlinear model predictive control (NMPC) to generate whole-body motions for a given contact plan. To efficiently search for an optimal contact plan, we propose to use Monte Carlo tree search (MCTS). While the combination of MCTS and NMPC can quickly find a feasible plan for a given environment (a few seconds), it is not yet suitable to be used as a reactive policy. Hence, we generate a dataset for optimal goal-conditioned policy for a given scene and learn it through supervised learning. In particular, we leverage the power of diffusion models in handling multi-modality in the dataset. We test our proposed framework on a scenario where our quadruped robot Solo12 successfully jumps to different goals in a highly constrained environment.<|reference_end|>
@article{dhédin2024diffusion-based,
    title={Diffusion-based learning of contact plans for agile locomotion},
    author={Victor Dh'edin, Adithya Kumar Chinnakkonda Ravi, Armand Jordana,
  Huaijiang Zhu, Avadesh Meduri, Ludovic Righetti, Bernhard Sch"olkopf, Majid
  Khadiv},
    journal={arXiv preprint arXiv:2403.03639},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.03639},
    primaryClass={cs.RO}
}