Object-Centric Kinodynamic Planning for Nonprehensile Robot Rearrangement Manipulation
<|reference_start|>Object-Centric Kinodynamic Planning for Nonprehensile Robot Rearrangement Manipulation: Nonprehensile actions such as pushing are crucial for addressing multi-object rearrangement problems. To date, existing nonprehensile solutions are all robot-centric, i.e., the manipulation actions are generated with robot-relevant intent and their outcomes are passively evaluated afterwards. Such pipelines are very different from human strategies and are typically inefficient. To this end, this work proposes a novel object-centric planning paradigm and develops the first object-centric planner for general nonprehensile rearrangement problems. By assuming that each object can actively move without being driven by robot interactions, the object-centric planner focuses on planning desired object motions, which are realized via robot actions generated online via a closed-loop pushing strategy. Through extensive experiments and in comparison with state-of-the-art baselines in both simulation and on a physical robot, we show that our object-centric paradigm can generate more intuitive and task-effective robot actions with significantly improved efficiency. In addition, we propose a benchmarking protocol to standardize and facilitate future research in nonprehensile rearrangement.<|reference_end|>
@article{ren2024object-centric,
    title={Object-Centric Kinodynamic Planning for Nonprehensile Robot
  Rearrangement Manipulation},
    author={Kejia Ren, Gaotian Wang, Andrew S. Morgan, Lydia E. Kavraki, and Kaiyu
  Hang},
    journal={arXiv preprint arXiv:2410.00261},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.00261},
    primaryClass={cs.RO}
}