Aligning Diffusion Model with Problem Constraints for Trajectory Optimization

• URL: http://arxiv.org/abs/2504.00342v1
• Date: Tue, 01 Apr 2025 01:46:05 GMT
• Title: Aligning Diffusion Model with Problem Constraints for Trajectory Optimization
• Authors: Anjian Li, Ryne Beeson,
• Abstract summary: We propose a novel approach that aligns diffusion models explicitly with problem-specific constraints.<n>Our approach is well-suited for integration into the Dynamic Data-driven Application Systems (DDDAS) framework.
• Score: 0.6629765271909505
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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