用扩散模型生成探索路径，让机器人一次推理就规划出高效路线。
DARE: Diffusion Policy for Autonomous Robot Exploration
- 基于注意力编码器与扩散策略，从专家示范中学习探索模式。
- 在仿真与真实场景中表现媲美顶尖探索算法，且泛化能力强。
- 适合需要高效自主探索的机器人应用，如未知环境巡检。
自主机器人探索需高效感知并构建未知环境地图。相较于仅基于当前信念优化路径的传统方法，基于学习的方法可通过历史经验推理未知区域，展现更优潜力。本文提出DARE，一种基于扩散模型的新生成式方法，利用专家示范训练，可一次性推理生成探索路径。DARE基于注意力编码器与扩散策略模型，并引入真实最优示范用于训练，使规划器能基于部分信念识别未知区域潜在结构，并在路径规划中加以考虑。实验表明，DARE在仿真与真实场景中均达到与主流探索规划器相当的性能，具备良好泛化能力。
原文摘要 · Abstract (English)
Autonomous robot exploration requires a robot to efficiently explore and map unknown environments. Compared to conventional methods that can only optimize paths based on the current robot belief, learning-based methods show the potential to achieve improved performance by drawing on past experiences to reason about unknown areas. In this paper, we propose DARE, a novel generative approach that leverages diffusion models trained on expert demonstrations, which can explicitly generate an exploration path through one-time inference. We build DARE upon an attention-based encoder and a diffusion policy model, and introduce ground truth optimal demonstrations for training to learn better patterns for exploration. The trained planner can reason about the partial belief to recognize the potential structure in unknown areas and consider these areas during path planning. Our experiments demonstrate that DARE achieves on-par performance with both conventional and learning-based state-of-the-art exploration planners, as well as good generalizability in both simulations and real-life scenarios.
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