  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.
