Diffusion Seed Trajectories for Faster Robot Motion Planning
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Solution Overview
Problem
Existing robot motion planning methods, both sampling-based and optimization-based, face inefficiencies in high-dimensional configuration spaces, leading to variable planning times and sensitivity to initial path quality, with sampling-based methods requiring postprocessing and optimization-based methods being sensitive to initial path quality.
Innovation Solution
A neural network architecture comprising an observation encoder and a noise prediction network generates multiple high-quality seed trajectories in parallel, utilizing a joint training procedure to enhance efficiency and speed in motion planning tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Shape
If optimization-based methods are used to generate smooth paths directly, then path smoothness is improved, but sensitivity to initial path quality increases
Solution Approach 1:
The diffusion model generates high-quality seed trajectories in advance through probabilistic sampling before optimization begins. These pre-generated seeds are already smooth and collision-free, reducing the optimization process's sensitivity to initial path quality while maintaining the ability to produce smooth final paths.
2Reliability
If sampling-based methods are used to find collision-free paths, then collision avoidance is improved, but planning time increases significantly with configuration space complexity
Solution Approach 1:
The patent replaces traditional sampling-based mechanical search with a diffusion-based probabilistic model. The diffusion model learns to generate trajectories directly from environmental observations, substituting the iterative sampling and collision checking process with a neural network that produces collision-free paths in a single forward pass, dramatically reducing planning time while maintaining safety.
3Device complexity
If multiple seed trajectories are generated sequentially, then computational resources are reduced, but motion planning efficiency decreases
Solution Approach 1:
The diffusion model generates multiple seed trajectories by sampling from a probabilistic distribution in the trajectory space dimension. By leveraging the stochastic nature of diffusion and running multiple samples in parallel, the system efficiently explores multiple promising paths simultaneously without requiring excessive computational resources, improving overall motion planning efficiency.
Data Source
AI summary
Embodiments of the present disclosure relate to a neural network architecture for generating high-quality, multi-modal trajectories as seed trajectories for optimization-based motion planners. The neural network architecture utilizes an observation encoder configured to encode environmental observations, and a noise prediction network configured to perform denoising based on the observations. The neural network architecture is configured to generate multiple seed trajectories in parallel by simultaneously running several instances of the noise prediction network. In contrast to conventional techniques, this architecture produces multiple high-quality seed trajectories at once, significantly enhancing the efficiency and speed of motion planning tasks.