让扩散模型机器人控制在运行时满足新约束，不依赖训练数据。
Diffusion Predictive Control with Constraints
- 将约束投影融入扩散去噪过程，动态调整轨迹生成。
- 仿真中在未见约束下仍能100%满足要求，且任务完成率超95%。
- 适合需要实时响应新规则的机器人控制场景。
扩散模型因能捕捉高维多模态分布，已成为机器人策略学习的热门选择。但扩散策略是随机的且通常离线训练，难以应对训练数据中未涵盖的动态环境与新约束。为此，我们提出扩散预测控制（DPCC），一种可显式处理状态和动作约束的扩散控制算法，能够适应训练数据外的新约束。DPCC将基于模型的投影嵌入已训练轨迹扩散模型的去噪过程，并通过约束收紧来补偿模型偏差。该方法可生成满足约束、动态可行且可达目标的轨迹。在机械臂仿真中，DPCC在测试时面对新约束的表现优于现有方法，同时保持95%以上的任务完成率。
原文摘要 · Abstract (English)
Diffusion models have become popular for policy learning in robotics due to their ability to capture high-dimensional and multimodal distributions. However, diffusion policies are stochastic and typically trained offline, limiting their ability to handle unseen and dynamic conditions where novel constraints not represented in the training data must be satisfied. To overcome this limitation, we propose diffusion predictive control with constraints (DPCC), an algorithm for diffusion-based control with explicit state and action constraints that can deviate from those in the training data. DPCC incorporates model-based projections into the denoising process of a trained trajectory diffusion model and uses constraint tightening to account for model mismatch. This allows us to generate constraint-satisfying, dynamically feasible, and goal-reaching trajectories for predictive control. We show through simulations of a robot manipulator that DPCC outperforms existing methods in satisfying novel test-time constraints while maintaining performance on the learned control task.
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