让扩散模型与约束优化协同工作，提升机器人规划安全性
Joint Model-based Model-free Diffusion for Planning with Constraints
- 将生成与优化模块统一为联合采样问题，通过交互势函数增强兼容性
- 在离线强化学习和机械臂操作中显著提升任务性能，且不牺牲安全约束
- 无需额外训练，支持非可微目标，适合需要高安全性的机器人系统
无模型扩散规划器在机器人运动规划中表现优异，但实际系统常需结合基于模型的优化模块以满足安全等约束。直接集成二者存在兼容性问题，因扩散模型的多模态输出可能与优化模块产生对抗。为此，本文提出联合模型-无模型扩散框架（JM2D），将模块集成建模为联合采样问题，通过交互势函数最大化兼容性，无需额外训练。利用重要性采样，仅根据交互势评估引导模块输出，从而处理由非凸优化模块带来的非可微目标。在离线强化学习和机器人操作任务中验证表明，相比传统安全过滤器，JM2D显著提升任务性能且不降低安全性。进一步证明条件生成是JM2D的特例，并通过对比最先进梯度与投影型扩散规划器阐明关键设计选择。更多信息见：https://jm2d-corl25.github.io/
原文摘要 · Abstract (English)
Model-free diffusion planners have shown great promise for robot motion planning, but practical robotic systems often require combining them with model-based optimization modules to enforce constraints, such as safety. Naively integrating these modules presents compatibility challenges when diffusion's multi-modal outputs behave adversarially to optimization-based modules. To address this, we introduce Joint Model-based Model-free Diffusion (JM2D), a novel generative modeling framework. JM2D formulates module integration as a joint sampling problem to maximize compatibility via an interaction potential, without additional training. Using importance sampling, JM2D guides modules outputs based only on evaluations of the interaction potential, thus handling non-differentiable objectives commonly arising from non-convex optimization modules. We evaluate JM2D via application to aligning diffusion planners with safety modules on offline RL and robot manipulation. JM2D significantly improves task performance compared to conventional safety filters without sacrificing safety. Further, we show that conditional generation is a special case of JM2D and elucidate key design choices by comparing with SOTA gradient-based and projection-based diffusion planners. More details at: https://jm2d-corl25.github.io/.
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