用扩散模型生成安全轨迹，结合模型预测控制提升机器人规划安全性与效率
D-SafeMPC: Diffusion-Driven Safe Model Predictive Control with Discrete-Time Control Barrier Functions
- 扩散过程受控制屏障函数引导，每步迭代用MPC优化轨迹
- 在4种障碍场景中成功率超基线，实机测试验证有效性
- 适合需要高安全性的机器人路径规划任务
扩散模型在机器人规划中的应用受限于无法内生保证安全或动力学约束，常产生物理上不可行的输出。混合方法虽采用模型预测控制（MPC）解决此问题，但因扩散模型提供的初始轨迹质量差，导致MPC难以收敛至安全可行解。为此，我们提出D-SafeMPC，通过控制屏障函数（CBFs）和控制李雅普诺夫函数（CLFs）引导逆扩散过程，并引入迭代投影机制，在每个去噪步骤中由MPC精修轨迹。该方法将采样导向安全、目标导向区域，提供可靠的MPC热启动。在Franka机械臂上的四类仿真场景（含静态与三类动态障碍）及真实机器人实验中，D-SafeMPC显著提升了安全性、任务成功率与规划效率，优于现有先进方法。为促进复现，代码与配置已公开于https://github.com/erdiphd/D-SafeMPC。
原文摘要 · Abstract (English)
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
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