发表机构
Paderborn University; Illinois Institute of Technology; California Institute of Technology; Technical University of Munich（帕德博恩大学; 伊利诺伊理工学院; 加州理工学院; 慕尼黑工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对扩散模型用于机器人规划时无法保证安全和动态约束的问题，提出D-SafeMPC方法，通过控制障碍函数等引导扩散过程，结合迭代投影方案，经实验验证该方法能提升安全性、任务成功率和规划效率。
AI 中文摘要
机器人规划中扩散模型应用的关键限制在于其无法内在地执行安全或动态约束，常导致物理上不可行或不安全的输出。采用模型预测控制（MPC）解决此问题的混合方法可能不稳定，因扩散模型的不良轨迹初始化会阻碍MPC收敛到安全可行解。为克服这些挑战，我们提出D-SafeMPC，增强扩散与控制间的交互。该方法用控制障碍函数（CBF）和控制李雅普诺夫函数（CLF）引导反向扩散过程，并采用迭代投影方案，让MPC在每个去噪步骤优化轨迹。这使采样趋向安全、目标导向区域并提供可靠的MPC热启动。在Franka机械手的四种场景（一个静态障碍和三个动态障碍设置）模拟以及物理Franka机器人的仿真到真实实验中，D-SafeMPC比现有基线提高了安全性、任务成功率和规划效率。为便于重现，我们的源代码和实验配置可在给定网址的存储库中获取。
英文摘要
A key limitation on the use of diffusion models in robotic planning is their inability to inherently enforce safety or dynamical constraints, which often results in physically infeasible or unsafe outputs. Hybrid approaches that employ model predictive control (MPC) to address this problem can be unstable, as poor trajectory initializations from the diffusion model prevent the MPC from converging to a safe and feasible solution. To overcome these challenges, we propose D-SafeMPC, which enhances the interaction between diffusion and control. Our method guides the reverse diffusion process with control barrier functions (CBFs) and control Lyapunov functions (CLFs) and employs an iterative-projection scheme where an MPC refines the trajectory at each denoising step. This steers sampling toward safe, goal-directed regions and provides reliable MPC warm starts. In simulations on a Franka manipulator across four scenarios (one static-obstacle and three dynamic-obstacle settings) and in a sim-to-real experiment on a physical Franka robot, D-SafeMPC improves safety, task success rates, and planning efficiency over state-of-the-art baselines. To facilitate reproducibility, our source code and experimental configurations are available in a repository at https://github.com/erdiphd/D-SafeMPC
