混合扩散模型：结合开环流程与视觉运动扩散策略
Hybrid Imitation Learning: Teleoperation Augmentation Primitives that Policies Learn to Trigger
AI总结：
混合扩散模型结合开环流程与视觉运动扩散策略，通过遥控增强原语实现高效任务执行，验证于现实挑战任务。
摘要：
尽管基于视觉运动的策略通过模仿学习在复杂操作任务中表现出色，但它们通常难以达到传统控制方法相同的精度和速度。在本工作中，我们引入了混合扩散模型，将开环流程与视觉运动扩散策略相结合。我们开发了遥控增强原语（TAPs），允许操作员在演示过程中无缝执行预定义的流程，例如锁定特定轴、移动到栖息点 waypoints 或触发任务特定的流程。我们的混合扩散方法在推理过程中学习触发此类 TAPs。我们在具有挑战性的现实任务上验证了该方法：瓶吸液、开放式容器液体转移和容器拆卸。所有实验视频均可在项目网站上获取：https://hybriddiffusion.github.io/
英文摘要：
What an operator can demonstrate bounds what imitation learning can learn. Teleoperation interfaces map the human body to the robot, so motions that are hard for a human, such as holding an exact orientation, returning to the same viewpoint, or turning a wrist joint several full revolutions, are difficult to demonstrate on most teleoperation interfaces, even when they are trivial for the robot. We introduce Teleoperation Augmentation Primitives (TAPs): axis locks, perching waypoints, pose anchors, and embodiment-specific routines that the operator triggers during a demonstration by speech, an AR menu, or simply with a controller button. TAPs are themselves recorded in the demonstration and can therefore also be learned and invoked by the policy itself. In simulation, where benchmarks provide human demonstrations, we show that augmenting them with TAPs after the fact yields better policies on a wristcamera-only peg insertion (0.372 to 0.531 success) and on a mug-cleanup task with a learned "remember this pose" anchor (0.203 to 0.323). On a real robot, three tasks show the same pattern: primitives that help the operator do not hurt the policy, and a policy triggering an unscrewing routine succeeds where a plain policy cannot resolve the multi-turn motion from images alone (67% vs. 38% success). We close with an industrial proof-of-concept case leveraging this idea. Project website: https://hybrid-imitation.github.io/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
