Classical motion planning for robotic manipulation includes a set of general algorithms that aim to minimize a scene-specific cost of executing a given plan. This approach offers remarkable adaptability, as they can be directly used off-the-shelf for any new scene without needing specific training datasets. However, without a prior understanding of what diverse valid trajectories are and without specially designed cost functions for a given scene, the overall solutions tend to have low success rates. While deep-learning-based algorithms tremendously improve success rates, they are much harder to adopt without specialized training datasets. We propose EDMP, an Ensemble-of-costs-guided Diffusion for Motion Planning that aims to combine the strengths of classical and deep-learning-based motion planning. Our diffusion-based network is trained on a set of diverse kinematically valid trajectories. Like classical planning, for any new scene at the time of inference, we compute scene-specific costs such as "collision cost" and guide the diffusion to generate valid trajectories that satisfy the scene-specific constraints. Further, instead of a single cost function that may be insufficient in capturing diversity across scenes, we use an ensemble of costs to guide the diffusion process, significantly improving the success rate compared to classical planners. EDMP performs comparably with SOTA deep-learning-based methods while retaining the generalization capabilities primarily associated with classical planners.

我们提出了EDMP，即基于代价引导的扩散运动规划集合，旨在结合经典规划与基于深度学习的运动规划的优势，通过在多样的有效运动轨迹集合上进行训练的扩散网络，计算特定场景的代价，如“碰撞代价”，并引导扩散生成满足场景约束条件的有效轨迹，同时使用代价集合而非单一代价函数来指导扩散过程，显著提高了成功率，并保留了与经典规划器主要相关的泛化能力。

EDMP：预算引导扩散的运动规划集成