CoBL-Diffusion: Diffusion-Based Conditional Robot Planning in Dynamic Environments Using Control Barrier and Lyapunov Functions
Year: 2024
Type: conference-paper
Abstract: 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 CoBL-Diffusion 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.
Authors: Kazuki Mizuta, Karen Leung
Institution: University of Washington
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4405779038