MATCH POLICY: A Simple Pipeline from Point Cloud Registration to Manipulation Policies
<|reference_start|>MATCH POLICY: A Simple Pipeline from Point Cloud Registration to Manipulation Policies: Many manipulation tasks require the robot to rearrange objects relative to one another. Such tasks can be described as a sequence of relative poses between parts of a set of rigid bodies. In this work, we propose MATCH POLICY, a simple but novel pipeline for solving high-precision pick and place tasks. Instead of predicting actions directly, our method registers the pick and place targets to the stored demonstrations. This transfers action inference into a point cloud registration task and enables us to realize nontrivial manipulation policies without any training. MATCH POLICY is designed to solve high-precision tasks with a key-frame setting. By leveraging the geometric interaction and the symmetries of the task, it achieves extremely high sample efficiency and generalizability to unseen configurations. We demonstrate its state-of-the-art performance across various tasks on RLBench benchmark compared with several strong baselines and test it on a real robot with six tasks.<|reference_end|>
@article{huang2024match,
    title={MATCH POLICY: A Simple Pipeline from Point Cloud Registration to
  Manipulation Policies},
    author={Haojie Huang, Haotian Liu, Dian Wang, Robin Walters, Robert Platt},
    journal={arXiv preprint arXiv:2409.15517},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15517},
    primaryClass={cs.RO cs.CV}
}