  Equipping autonomous robots with the ability to navigate safely and
efficiently around humans is a crucial step toward achieving trusted robot
autonomy. However, generating robot plans while ensuring safety in dynamic
multi-agent environments remains a key challenge. Building upon recent work on
leveraging deep generative models for robot planning in static environments,
this paper proposes CoBL-Diffusion, a novel diffusion-based safe robot planner
for dynamic environments. CoBL-Diffusion uses Control Barrier and Lyapunov
functions to guide the denoising process of a diffusion model, iteratively
refining the robot control sequence to satisfy the safety and stability
constraints. We demonstrate the effectiveness of the proposed model using two
settings: a synthetic single-agent environment and a real-world pedestrian
dataset. Our results show that CoBL-Diffusion generates smooth trajectories
that enable the robot to reach goal locations while maintaining a low collision
rate with dynamic obstacles.
