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About this paper
Huang Huang, Balakumar Sundaralingam, Arsalan Mousavian, Adithyavairavan Murali, Ken Goldberg, Dieter Fox
DiffusionSeeder introduces a hybrid approach for rapid robot motion planning, leveraging a conditional diffusion model to generate high-quality, diverse seed trajectories for a GPU-accelerated motion optimizer. The method achieves up to a 36x speedup and a 10% higher success rate in simulation, and an 86% success rate with 26ms planning time on a real robot in cluttered, partially observed environments.
Key insights
- Combining a generative diffusion model with classical optimization effectively addresses the 'cold start' problem, enabling rapid convergence to high-quality solutions in complex motion planning.
- Diffusion models naturally generate diverse, multi-modal seed trajectories, which is crucial for increasing the success rate of optimizers in environments with multiple valid paths.
- Operating on partial observations (depth images) through a Vision Transformer (ViT) encoder allows for practical application in real-world settings where full scene geometry is often unavailable.
Summary by alphaXiv.
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