LDP：用于高效机器人导航与避障的局部扩散规划器
LDP: A Local Diffusion Planner for Efficient Robot Navigation and Collision Avoidance
AI总结：
本文提出局部扩散规划器LDP，通过融合全局—局部洞察的多智能体多样化数据生成机制训练扩散策略，并以轻量级方式引入全局观测，从而提升机器人在复杂动态场景中的避障与导航鲁棒性。
摘要：
条件扩散模型已被证明是学习机器人策略的高效工具，这得益于其在精确建模策略条件分布方面的先进性。真实世界场景具有动态障碍物和迷宫式结构，其复杂性质凸显了将机器人局部导航决策作为条件分布问题的复杂性。然而，将扩散模型用于机器人局部导航并非易事，并面临若干尚未充分探索的挑战：（1）数据紧迫性。局部导航中复杂的条件分布需要训练数据包含多样化真实世界场景中的多样化策略；（2）短视观测。由于感知场景的多样性，基于机器人局部视角的扩散决策对于完成整个任务可能并非最优，因为它们往往缺乏前瞻性。在某些需要绕行的场景中，机器人可能会被困住。为解决这些问题，我们的方法首先探索一种多样化数据生成机制，该机制通过融合全局—局部洞察的目标选择，涵盖表现出不同偏好的多个智能体。然后，基于这些多样化训练数据，得到一个扩散智能体，能够在多样化场景中实现出色的避障。随后，我们通过以轻量级方式纳入全局观测来增强局部扩散规划器（Local Diffusion Planner，简称LDP）。这一增强拓宽了LDP的观测范围，有效降低了陷入局部最优的风险，并促进更鲁棒的导航决策。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
