CoBL-Diffusion: Diffusion-Based Conditional Robot Planning in Dynamic Environments Using Control Barrier and Lyapunov Functions

TL;DR

CoBL-Diffusion introduces a diffusion-based robot planning method that integrates safety and stability constraints using Control Barrier and Lyapunov functions, effectively navigating dynamic environments with low collision rates.

Contribution

This paper presents a novel diffusion-based planning framework that incorporates safety and stability constraints for dynamic multi-agent environments, extending prior static environment approaches.

Findings

Generates smooth, goal-reaching trajectories in dynamic settings.

Maintains low collision rates with moving obstacles.

Effective in both synthetic and real-world pedestrian scenarios.

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 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.