Logic Learning from Demonstrations for Multi-step Manipulation Tasks in Dynamic Environments
<|reference_start|>Logic Learning from Demonstrations for Multi-step Manipulation Tasks in Dynamic Environments: Learning from Demonstration (LfD) stands as an efficient framework for imparting human-like skills to robots. Nevertheless, designing an LfD framework capable of seamlessly imitating, generalizing, and reacting to disturbances for long-horizon manipulation tasks in dynamic environments remains a challenge. To tackle this challenge, we present Logic Dynamic Movement Primitives (Logic-DMP), which combines Task and Motion Planning (TAMP) with an optimal control formulation of DMP, allowing us to incorporate motion-level via-point specifications and to handle task-level variations or disturbances in dynamic environments. We conduct a comparative analysis of our proposed approach against several baselines, evaluating its generalization ability and reactivity across three long-horizon manipulation tasks. Our experiment demonstrates the fast generalization and reactivity of Logic-DMP for handling task-level variants and disturbances in long-horizon manipulation tasks.<|reference_end|>
@article{zhang2024logic,
    title={Logic Learning from Demonstrations for Multi-step Manipulation Tasks in
  Dynamic Environments},
    author={Yan Zhang, Teng Xue, Amirreza Razmjoo, Sylvain Calinon},
    journal={arXiv preprint arXiv:2404.16138},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.16138},
    primaryClass={cs.RO}
}