Contact-conditioned learning of locomotion policies
<|reference_start|>Contact-conditioned learning of locomotion policies: Locomotion is realized through making and breaking contact. State-of-the-art constrained nonlinear model predictive controllers (NMPC) generate whole-body trajectories for a given contact sequence. However, these approaches are computationally expensive at run-time. Hence it is desirable to offload some of this computation to an offline phase. In this paper, we hypothesize that conditioning a learned policy on the locations and timings of contact is a suitable representation for learning a single policy that can generate multiple gaits (contact sequences). In this way, we can build a single generalist policy to realize different gaited and non-gaited locomotion skills and the transitions among them. Our extensive simulation results demonstrate the validity of our hypothesis for learning multiple gaits for a biped robot.<|reference_end|>
@article{ciebielski2024contact-conditioned,
    title={Contact-conditioned learning of locomotion policies},
    author={Michal Ciebielski, Majid Khadiv},
    journal={arXiv preprint arXiv:2408.00776},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.00776},
    primaryClass={cs.RO}
}