Aligning Diffusion Model with Problem Constraints for Trajectory Optimization
This addresses constraint violations in diffusion-based trajectory optimization for robotics and autonomous systems, representing an incremental improvement over existing methods.
The paper tackles the problem of diffusion models for trajectory optimization violating critical constraints like goal-reaching and collision avoidance by proposing a constraint-aligned approach with a hybrid loss function and re-weighting strategy. The result is significantly reduced constraint violations on tabletop manipulation and two-car reach-avoid problems while maintaining solution quality.
Diffusion models have recently emerged as effective generative frameworks for trajectory optimization, capable of producing high-quality and diverse solutions. However, training these models in a purely data-driven manner without explicit incorporation of constraint information often leads to violations of critical constraints, such as goal-reaching, collision avoidance, and adherence to system dynamics. To address this limitation, we propose a novel approach that aligns diffusion models explicitly with problem-specific constraints, drawing insights from the Dynamic Data-driven Application Systems (DDDAS) framework. Our approach introduces a hybrid loss function that explicitly measures and penalizes constraint violations during training. Furthermore, by statistically analyzing how constraint violations evolve throughout the diffusion steps, we develop a re-weighting strategy that aligns predicted violations to ground truth statistics at each diffusion step. Evaluated on a tabletop manipulation and a two-car reach-avoid problem, our constraint-aligned diffusion model significantly reduces constraint violations compared to traditional diffusion models, while maintaining the quality of trajectory solutions. This approach is well-suited for integration into the DDDAS framework for efficient online trajectory adaptation as new environmental data becomes available.