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

• URL: http://arxiv.org/abs/2409.15517v1
• Date: Mon, 23 Sep 2024 20:09:43 GMT
• Title: MATCH POLICY: A Simple Pipeline from Point Cloud Registration to Manipulation Policies
• Authors: Haojie Huang, Haotian Liu, Dian Wang, Robin Walters, Robert Platt,
• Abstract summary: MATCH POLICY is a pipeline for solving high-precision pick and place tasks. It transfers action inference into a point cloud registration task. It achieves extremely high sample efficiency and generalizability to unseen configurations.
• Score: 25.512068008948603
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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