Safe Model Predictive Diffusion with Shielding

2025-12-05

AI Summary

Generating safe and dynamically feasible trajectories for complex robotic systems—particularly in non-convex environments—remains challenging due to the difficulty of jointly satisfying safety and motion-dynamics constraints. Method: This paper proposes a training-free diffusion-based planning framework. Its core innovation is the first integration of a safety shielding mechanism directly into the denoising process of the diffusion model—bypassing post-hoc correction—and thereby ensuring end-to-end trajectory generation that intrinsically satisfies both safety and dynamical feasibility. The approach unifies a model-based diffusion architecture, explicit kinematic and dynamic modeling, and real-time safety verification. Results: Evaluated on high-dimensional nonlinear systems such as tractor-trailer models, the method achieves state-of-the-art task success rates, substantially improves safety guarantees, and completes single-shot planning in under one second.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: Safety and Robustness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

Generating safe, kinodynamically feasible, and optimal trajectories for complex robotic systems is a central challenge in robotics. This paper presents Safe Model Predictive Diffusion (Safe MPD), a training-free diffusion planner that unifies a model-based diffusion framework with a safety shield to generate trajectories that are both kinodynamically feasible and safe by construction. By enforcing feasibility and safety on all samples during the denoising process, our method avoids the common pitfalls of post-processing corrections, such as computational intractability and loss of feasibility. We validate our approach on challenging non-convex planning problems, including kinematic and acceleration-controlled tractor-trailer systems. The results show that it substantially outperforms existing safety strategies in success rate and safety, while achieving sub-second computation times.

Problem

• Ensures kinodynamically feasible and safe robotic trajectories
• Avoids post-processing pitfalls like computational intractability
• Outperforms existing safety strategies in success and speed

Innovation

• Training-free diffusion planner with safety shield
• Enforces feasibility and safety during denoising process
• Achieves sub-second computation in non-convex planning