LDP: A Local Diffusion Planner for Efficient Robot Navigation and Collision Avoidance
<|reference_start|>LDP: A Local Diffusion Planner for Efficient Robot Navigation and Collision Avoidance: The conditional diffusion model has been demonstrated as an efficient tool for learning robot policies, owing to its advancement to accurately model the conditional distribution of policies. The intricate nature of real-world scenarios, characterized by dynamic obstacles and maze-like structures, underscores the complexity of robot local navigation decision-making as a conditional distribution problem. Nevertheless, leveraging the diffusion model for robot local navigation is not trivial and encounters several under-explored challenges: (1) Data Urgency. The complex conditional distribution in local navigation needs training data to include diverse policy in diverse real-world scenarios; (2) Myopic Observation. Due to the diversity of the perception scenarios, diffusion decisions based on the local perspective of robots may prove suboptimal for completing the entire task, as they often lack foresight. In certain scenarios requiring detours, the robot may become trapped. To address these issues, our approach begins with an exploration of a diverse data generation mechanism that encompasses multiple agents exhibiting distinct preferences through target selection informed by integrated global-local insights. Then, based on this diverse training data, a diffusion agent is obtained, capable of excellent collision avoidance in diverse scenarios. Subsequently, we augment our Local Diffusion Planner, also known as LDP by incorporating global observations in a lightweight manner. This enhancement broadens the observational scope of LDP, effectively mitigating the risk of becoming ensnared in local optima and promoting more robust navigational decisions.<|reference_end|>
@article{yu2024ldp:,
    title={LDP: A Local Diffusion Planner for Efficient Robot Navigation and
  Collision Avoidance},
    author={Wenhao Yu, Jie Peng, Huanyu Yang, Junrui Zhang, Yifan Duan, Jianmin Ji
  and Yanyong Zhang},
    journal={arXiv preprint arXiv:2407.01950},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.01950},
    primaryClass={cs.RO cs.AI}
}