Abstract
Reinforcement Learning- (RL-)based motion planning has recently shown the potential to outperform traditional approaches from autonomous navigation to robot manipulation. In this work, we focus on a motion planning task for an evasive target in a partially observable multi-agent adversarial pursuit-evasion games (PEG). These pursuit-evasion problems are relevant to various applications, such as search and rescue operations and surveillance robots, where robots must effectively plan their actions to gather intelligence or accomplish mission tasks while avoiding detection or capture themselves. We propose a hierarchical architecture that integrates a high-level diffusion model to plan global paths responsive to environment data while a low-level RL algorithm reasons about evasive versus global path-following behavior. Our approach outperforms baselines by 51.2% by leveraging the diffusion model to guide the RL algorithm for more efficient exploration and improves the explanability and predictability.
Abstract (translated)
强化学习（RL）为基础的运动规划已经在最近的研究中证明了在自主导航和机器人操作等传统方法之上超越的可能性。在本文中，我们关注一个在部分可观测多代理器对抗追击游戏（PEG）中的避碰目标的运动规划任务。这些追击- evasion 问题与各种应用有关，例如搜救和救援任务以及监视机器人，在这些应用中，机器人必须有效地规划其行动以收集情报或完成任务，同时避免被检测或捕捉。我们提出了一个分层架构，将高层扩散模型与低层强化学习算法相结合，在响应环境数据的同时规划全局路径。我们的方法通过利用扩散模型引导RL算法进行更有效的探索，提高了可解释性和可预测性。与基线相比，我们的方法提高了51.2%的性能。
URL
https://arxiv.org/abs/2403.10794
