ensemble-of-costs-diffusion.github.io - EDMP

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Description: EDMP

edmp (1)

Example domain paragraphs

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 to generate a potentially valid collision-free trajectory. This approach offers remarkable adaptability, as they can be directly used 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 solu

Architecture. EDMP leverages a diffusion model alongside an ensemble of l cost functions. The diffusion model denoises a batch of trajectories τ from t = T to 0, while each cost in the ensemble guides a specific sub-batch. We calculate the gradient ∇J of each collision cost (intersection or swept volume) in the ensemble from robot and environment bounding boxes, using differentiable forward kinematics. After denoising is over, the trajectory with minimum swept volume is chosen as the solution.

We would like to thank Adam Fishman for assisting with MπNets and providing valuable insights into collision checking and benchmarking.

Links to ensemble-of-costs-diffusion.github.io (4)

• bipashasen.github.io Bipasha Sen
• skymanaditya1.github.io Aditya Agarwal
• kallol-saha.github.io Kallol Saha
• vishal-2000.github.io Vishal Reddy Mandadi