DiffusionSeeder: Seeding Motion Optimization with Diffusion for Rapid Motion Planning
Huang Huang, Balakumar Sundaralingam, Arsalan Mousavian,, Adithyavairavan Murali, Ken Goldberg, Dieter Fox

TL;DR
DiffusionSeeder uses a diffusion model to generate diverse, high-quality motion trajectories from scene observations, significantly accelerating and improving the success rate of robot motion planning in complex, partially observed environments.
Contribution
It introduces a diffusion-based method for seeding motion optimization, enhancing speed and success rate in complex robot manipulation tasks compared to traditional approaches.
Findings
12x average speedup in simulation
36x speedup in complex scenarios
86% success rate in real robot experiments
Abstract
Running optimization across many parallel seeds leveraging GPU compute have relaxed the need for a good initialization, but this can fail if the problem is highly non-convex as all seeds could get stuck in local minima. One such setting is collision-free motion optimization for robot manipulation, where optimization converges quickly on easy problems but struggle in obstacle dense environments (e.g., a cluttered cabinet or table). In these situations, graph-based planning algorithms are used to obtain seeds, resulting in significant slowdowns. We propose DiffusionSeeder, a diffusion based approach that generates trajectories to seed motion optimization for rapid robot motion planning. DiffusionSeeder takes the initial depth image observation of the scene and generates high quality, multi-modal trajectories that are then fine-tuned with a few iterations of motion optimization. We…
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Taxonomy
TopicsRobotic Path Planning Algorithms · Robotic Mechanisms and Dynamics · Software Testing and Debugging Techniques
