用预测地图和强化学习提升机器人在室内环境的探索效率。
MapExRL: Human-Inspired Indoor Exploration with Predicted Environment Context and Reinforcement Learning
- 用前缘点作为动作空间，实现更长视野的规划决策。
- 在真实室内数据集上比最强基线提升18.8%的探索效率。
- 适合需要高效自主探索的智能机器人研究者。
机器人路径规划在未知环境中极具挑战性，需对未见空间进行推理并预判未来观测。高效探索要求在预算约束下选择信息增益最大的路径。尽管自主探索技术不断进步，现有算法在结构化环境中仍难以匹敌人类表现，尤其在存在可预测线索但未被充分利用的情况下。基于用户研究洞察，我们提出MapExRL，通过学习策略与全局地图预测，提升结构化室内环境中的探索效率。不同于多数基于运动基元的动作空间，本方法采用前缘点，实现更高效的模型学习与更长视野推理。框架从已观测地图生成全局地图预测，结合预测不确定性、传感器覆盖估计、前缘距离及剩余预算距离，评估前缘的长期战略价值。通过融合多种前缘评分方法与额外上下文，策略在每阶段做出更明智决策。我们在真实室内地图数据集上评估，相较于最强基线提升最高达18.8%，较传统前缘算法收益更显著。
原文摘要 · Abstract (English)
Path planning for robotic exploration is challenging, requiring reasoning over unknown spaces and anticipating future observations. Efficient exploration requires selecting budget-constrained paths that maximize information gain. Despite advances in autonomous exploration, existing algorithms still fall short of human performance, particularly in structured environments where predictive cues exist but are underutilized. Guided by insights from our user study, we introduce MapExRL, which improves robot exploration efficiency in structured indoor environments by enabling longer-horizon planning through a learned policy and global map predictions. Unlike many learning-based exploration methods that use motion primitives as the action space, our approach leverages frontiers for more efficient model learning and longer horizon reasoning. Our framework generates global map predictions from the observed map, which our policy utilizes, along with the prediction uncertainty, estimated sensor coverage, frontier distance, and remaining distance budget, to assess the strategic long-term value of frontiers. By leveraging multiple frontier scoring methods and additional context, our policy makes more informed decisions at each stage of the exploration. We evaluate our framework on a real-world indoor map dataset, achieving up to an 18.8% improvement over the strongest state-of-the-art baseline, with even greater gains compared to conventional frontier-based algorithms. Website: https://mapexrl.github.io
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