MATCH POLICY: A Simple Pipeline from Point Cloud Registration to Manipulation Policies

Autor:	Huang, Haojie, Liu, Haotian, Wang, Dian, Walters, Robin, Platt, Robert
Rok vydání:	2024
Předmět:	Computer Science - Robotics Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: project url: https://haojhuang.github.io/match_page/
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2409.15517 Zobrazit plný text záznamu View this record from Arxiv