ISS政策：具有隐式场景监督的可扩展扩散策略
ISS Policy : Scalable Diffusion Policy with Implicit Scene Supervision
AI总结：
ISS策略通过隐式场景监督模块提升机器人操作的性能和鲁棒性，实现高效、泛化能力强的3D视觉-运动扩散策略。
摘要：
基于视觉的模仿学习已经使机器人操作技能取得了显著进展，但其依赖于物体外观而忽视底层3D场景结构，导致训练效率低且泛化能力差。为了解决这些挑战，我们引入了隐式场景监督（ISS）策略，这是一种基于3D视觉-运动DiT的扩散策略，能够从点云观测中预测连续动作序列。我们扩展了DiT，加入了一个新的隐式场景监督模块，该模块鼓励模型产生与场景几何演变一致的输出，从而提高策略的性能和鲁棒性。值得注意的是，ISS策略在单臂操作任务（MetaWorld）和灵巧手操作（Adroit）上均取得了最先进的性能。在现实世界实验中，它也展示了强大的泛化能力和鲁棒性。额外的消融研究显示，我们的方法在数据和参数方面均能有效扩展。代码和视频将被发布。
英文摘要：
Vision-based imitation learning has enabled impressive robotic manipulation skills, but action imitation alone provides limited supervision of the geometric consequences of robot behavior. To address this limitation, we introduce **Implicit Scene Supervision (ISS) Policy**, a 3D visuomotor diffusion policy with a DiT backbone that predicts continuous action sequences from point-cloud observations. ISS augments action diffusion with a supervised robot motion predictor that maps generated actions and robot-state context to end-effector motion, and then uses the predicted motion together with gripper intent to forecast future point-cloud representations. By explicitly modeling the intermediate transition from action to robot motion, ISS encourages the policy to capture how its actions affect the surrounding 3D scene. We further introduce asymmetric gradient routing to separate direct motion regression from scene-level policy supervision, together with a change-balanced objective that accounts for variations in scene-change magnitude. These auxiliary objectives provide dynamics-aware geometric supervision using only expert demonstrations, without requiring additional annotations or auxiliary modules at inference time. ISS Policy achieves state-of-the-art performance on single-arm manipulation tasks in MetaWorld and dexterous manipulation tasks in Adroit, while real-world dual-arm experiments further demonstrate its effectiveness on physical robotic manipulation. The resulting framework preserves the scalable DiT backbone and standard diffusion-policy control interface. Code and videos will be released.
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。
