让机器人像人一样想象未知场景，高效探索新环境。
ForesightNav: Learning Scene Imagination for Efficient Exploration
- 用预测未探索区域的占据与语义信息来指导导航决策
- 在未见环境中实现100%点目标导航完成率，物体导航SPL达67%
- 适合需要自主探索的新场景机器人系统
理解人类如何利用先验知识在未知环境中进行探索性决策，对开发具备类似能力的自主机器人至关重要。本文提出ForesightNav，一种受人类想象与推理启发的新型探索策略。该方法使机器人能够预测未探索区域的占据状态和语义细节，从而高效选择有意义的长期导航目标，显著提升在未知环境中的探索效率。我们在Structured3D数据集上验证了该方法，结果表明其能准确预测占据信息，并在未见场景几何结构预测方面表现优异。实验显示，想象力模块显著提升了探索效率，在Structured3D验证集上实现PointNav 100%完成率，ObjectNav SPL达到67%。这些成果证明了基于想象的推理对增强自主系统泛化与高效探索能力的重要作用。
原文摘要 · Abstract (English)
Understanding how humans leverage prior knowledge to navigate unseen environments while making exploratory decisions is essential for developing autonomous robots with similar abilities. In this work, we propose ForesightNav, a novel exploration strategy inspired by human imagination and reasoning. Our approach equips robotic agents with the capability to predict contextual information, such as occupancy and semantic details, for unexplored regions. These predictions enable the robot to efficiently select meaningful long-term navigation goals, significantly enhancing exploration in unseen environments. We validate our imagination-based approach using the Structured3D dataset, demonstrating accurate occupancy prediction and superior performance in anticipating unseen scene geometry. Our experiments show that the imagination module improves exploration efficiency in unseen environments, achieving a 100% completion rate for PointNav and an SPL of 67% for ObjectNav on the Structured3D Validation split. These contributions demonstrate the power of imagination-driven reasoning for autonomous systems to enhance generalizable and efficient exploration.
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