CoBL-Diffusion: Diffusion-Based Conditional Robot Planning in Dynamic Environments Using Control Barrier and Lyapunov Functions
<|reference_start|>CoBL-Diffusion: Diffusion-Based Conditional Robot Planning in Dynamic Environments Using Control Barrier and Lyapunov Functions: 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.<|reference_end|>
@article{mizuta2024cobl-diffusion:,
    title={CoBL-Diffusion: Diffusion-Based Conditional Robot Planning in Dynamic
  Environments Using Control Barrier and Lyapunov Functions},
    author={Kazuki Mizuta, Karen Leung},
    journal={arXiv preprint arXiv:2406.05309},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.05309},
    primaryClass={cs.RO}
}