让扩散模型生成轨迹时自动遵守约束条件，避免撞墙或达不成目标。
Aligning Diffusion Model with Problem Constraints for Trajectory Optimization
- 用混合损失函数显式惩罚轨迹违反约束的情况。
- 在扩散过程中动态调整权重，使预测偏差贴近真实统计规律。
- 适合需要实时调整轨迹的机器人控制场景，尤其支持在线更新。
扩散模型近年来在轨迹优化中展现出强大生成能力，可产出高质量且多样化的解。然而，纯数据驱动的训练方式若未显式融入约束信息，常导致关键约束（如到达目标、避障、符合系统动力学）被违反。为此，本文提出一种新方法，将扩散模型与特定问题约束显式对齐，借鉴动态数据驱动应用系统（DDDAS）框架。通过引入混合损失函数，在训练中显式度量并惩罚约束违反行为；进一步通过分析约束违反随扩散步骤的变化规律，设计重加权策略，使各步骤的预测偏差与真实统计分布一致。在桌面操作和双车避障任务上的实验表明，该约束对齐扩散模型显著降低约束违反率，同时保持轨迹质量。该方法适用于集成至DDDAS框架，实现环境数据更新后的高效在线轨迹自适应。
原文摘要 · Abstract (English)
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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