先预测物体运动再生成动作，提升机器人抓取成功率
Motion Before Action: Diffusing Object Motion as Manipulation Condition
- 用两级扩散模型先预测物体未来姿态，再据此生成机器人动作
- 在仿真和真实场景中，动作成功率显著高于原有策略
- 可无缝接入现有扩散模型控制框架，适合做机器人模仿学习
从视觉观测中推断物体运动表征能提升机器人操作性能。本文提出一种新范式：通过推理视觉观测中的物体运动来生成动作序列。我们设计MBA（Motion Before Action）模块，采用两级扩散过程分别生成物体运动与机器人动作，并以物体运动作为条件引导动作生成。MBA首先基于观测预测物体未来的位姿序列，再以此序列指导机器人动作生成。该模块为即插即用设计，可灵活集成至已有带扩散动作头的机器人操作策略中。大量仿真与真实环境实验表明，该方法在多种操作任务中显著提升了现有策略的性能。
原文摘要 · Abstract (English)
Inferring object motion representations from observations enhances the performance of robotic manipulation tasks. This paper introduces a new paradigm for robot imitation learning that generates action sequences by reasoning about object motion from visual observations. We propose MBA (Motion Before Action), a novel module that employs two cascaded diffusion processes for object motion generation and robot action generation under object motion guidance. MBA first predicts the future pose sequence of the object based on observations, then uses this sequence as a condition to guide robot action generation. Designed as a plug-and-play component, MBA can be flexibly integrated into existing robotic manipulation policies with diffusion action heads. Extensive experiments in both simulated and real-world environments demonstrate that our approach substantially improves the performance of existing policies across a wide range of manipulation tasks. Project page: https://selen-suyue.github.io/MBApage/
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