用点云配准实现高精度抓取，无需训练即可泛化到新场景。
MATCH POLICY: A Simple Pipeline from Point Cloud Registration to Manipulation Policies
- 通过点云配准将操作任务转化为几何匹配问题。
- 在RLBench上表现超越多个强基线，六项真实机器人任务成功执行。
- 无需训练，利用几何对称性实现极高的样本效率。
许多操作任务需要机器人重新排列物体间的相对位置，可描述为一组刚体部件之间的相对位姿序列。本文提出MATCH POLICY，一种简单而新颖的高精度抓取与放置任务求解管道。该方法不直接预测动作，而是将目标与存储的示范进行点云配准，将动作推断转化为点云注册任务，从而无需训练即可实现复杂操作策略。该方法专为关键帧设置下的高精度任务设计，利用任务中的几何交互和对称性，实现了极高的样本效率和对未见配置的泛化能力。我们在RLBench基准上展示了其在多种任务上的最先进性能，并在真实机器人上完成了六项任务测试。
原文摘要 · Abstract (English)
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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