用查询引导扩散策略，提升机器人装配的通用性与精度。
Query-Centric Diffusion Policy for Generalizable Robotic Assembly
- 以物体、接触点和技能信息构建查询，连接高层规划与底层控制。
- 在仿真与真实场景中，插入和拧螺丝任务成功率提升超50%。
- 适合需要高鲁棒性装配的工业机器人研究与应用。
机器人装配任务因部件间相互作用复杂且对接触环境噪声敏感，成为构建通用机器人的重要挑战。传统方法采用分层设计：高层多部件推理与底层精确控制，但高层指令与底层执行常存在不匹配问题。为此，本文提出查询中心扩散策略（QDP），通过包含物体、接触点和技能信息的查询，实现高层规划与底层控制的协同。QDP利用点云观测识别任务相关组件，并以此引导低层策略，增强鲁棒性。我们在FurnitureBench上进行了仿真与真实世界中的全面实验，结果显示，在长时序任务中，技能精度和成功率均显著提升。在具有挑战性的插入与拧螺丝任务中，相较于无结构化查询的基线，成功率达到提升超过50%。
原文摘要 · Abstract (English)
The robotic assembly task poses a key challenge in building generalist robots due to the intrinsic complexity of part interactions and the sensitivity to noise perturbations in contact-rich settings. The assembly agent is typically designed in a hierarchical manner: high-level multi-part reasoning and low-level precise control. However, implementing such a hierarchical policy is challenging in practice due to the mismatch between high-level skill queries and low-level execution. To address this, we propose the Query-centric Diffusion Policy (QDP), a hierarchical framework that bridges high-level planning and low-level control by utilizing queries comprising objects, contact points, and skill information. QDP introduces a query-centric mechanism that identifies task-relevant components and uses them to guide low-level policies, leveraging point cloud observations to improve the policy's robustness. We conduct comprehensive experiments on the FurnitureBench in both simulation and real-world settings, demonstrating improved performance in skill precision and long-horizon success rate. In the challenging insertion and screwing tasks, QDP improves the skill-wise success rate by over 50% compared to baselines without structured queries.
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