用点云+流匹配，让机器人学会更精准的抓取动作。
Learning Robotic Manipulation Policies from Point Clouds with Conditional Flow Matching
- 用点云作为输入，结合条件流匹配建模动作分布。
- 在RLBench上达67.8%成功率，是第二名的两倍。
- 适合做视觉感知与复杂动作规划的机器人研究者。
从专家示范中学习是有限数据下训练机器人操作策略的有前景方法。然而，模仿学习算法需在输入模态、训练目标和6-自由度末端执行器姿态表示等方面做出诸多设计选择。基于扩散的方法因其能预测长时程轨迹并处理多模态动作分布而受到关注。最近提出的条件流匹配（CFM，或修正流）是扩散模型的一种更灵活泛化形式。本文研究了CFM在机器人策略学习中的应用，并重点探讨其与其他设计选择的协同关系。结果表明，当结合点云输入观测时，CFM表现最佳。此外，我们研究了在SO(3)流形上的CFM可行性，并通过简化示例评估其适用性。在RLBench上的大量实验表明，所提出的PointFlowMatch方法在8个任务上实现67.8%的平均成功率达当前最优，是次优方法的两倍。
原文摘要 · Abstract (English)
Learning from expert demonstrations is a promising approach for training robotic manipulation policies from limited data. However, imitation learning algorithms require a number of design choices ranging from the input modality, training objective, and 6-DoF end-effector pose representation. Diffusion-based methods have gained popularity as they enable predicting long-horizon trajectories and handle multimodal action distributions. Recently, Conditional Flow Matching (CFM) (or Rectified Flow) has been proposed as a more flexible generalization of diffusion models. In this paper, we investigate the application of CFM in the context of robotic policy learning and specifically study the interplay with the other design choices required to build an imitation learning algorithm. We show that CFM gives the best performance when combined with point cloud input observations. Additionally, we study the feasibility of a CFM formulation on the SO(3) manifold and evaluate its suitability with a simplified example. We perform extensive experiments on RLBench which demonstrate that our proposed PointFlowMatch approach achieves a state-of-the-art average success rate of 67.8% over eight tasks, double the performance of the next best method.
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