将生成式策略基于物理：面向机器人控制的优化引导扩散
Grounding Generative Policies in Physics: Optimization-Guided Diffusion for Robot Control
AI总结：
提出推理时优化框架，通过将扩散引导建模为约束优化问题，在采样中施加物理可行性约束，无需重训练即可提升机器人任务成功率。
摘要：
扩散模型能有效从高维多模态分布中采样，但其输出可能违反部署约束。对于任务空间机器人策略，生成抓取、路径点或轨迹可能在分布上有效但不可行，违反可达性、避碰或闭环可执行性要求。这种具身差距限制了机器人的零样本部署，即使任务空间行为本身是可迁移的。我们提出一个推理时优化框架，通过将扩散引导公式化为约束优化问题，将行为生成与物理可行性耦合。我们的关键见解是用优化校正替换反向过程中的采样扰动，允许在采样过程中施加硬约束或软惩罚，无需重新训练扩散模型，同时保持样本接近学习先验。我们在具有可达性和避碰约束的灵巧抓取合成，以及具有控制器级可跟踪性约束的动态操作上评估该方法。在各种设置和机器人具身上，优化引导去噪匹配投影和梯度引导基线的可行性，同时更好地保持抓取质量，并提高控制器级可执行性和任务成功率，在灵巧抓取上任务成功率比最佳基线提高最多20个百分点，在视觉运动操作上提高23个百分点。
英文摘要：
Diffusion models sample effectively from high-dimensional, multimodal distributions, but their outputs may violate deployment constraints. For task-space robot policies, generated grasps, waypoints, or trajectories can be distributionally valid yet infeasible, violating reachability, collision-avoidance, or closed-loop executability requirements. This embodiment gap limits zero-shot deployment across robots, even when the task-space behavior itself is transferable. We propose an inference-time optimization framework that couples the behavior generation to physical feasibility by formulating diffusion guidance as a constrained optimization problem. Our key insight is to replace the sampling perturbation in the backward process with an optimized correction, allowing hard constraints or soft penalties to be imposed during sampling without the need to retrain the diffusion model, while keeping samples close to the learned prior. We evaluate the method on dexterous grasp synthesis with reachability and collision-avoidance constraints, and dynamic manipulation with controller-level trackability constraints. Across settings and robot embodiments, optimization-guided denoising matches the feasibility of projection- and gradient-guidance baselines while better preserving grasp quality, and improving controller-level executability and task success, with task success improving by up to 20pp. on dexterous grasping and 23pp. on visuomotor manipulation over the best baseline.
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。
