基于神经辐射表示的连续视觉语言导航前瞻探索
Lookahead Exploration with Neural Radiance Representation for Continuous Vision-Language Navigation
AI总结：
本文提出预训练分层神经辐射表示模型HNR，通过生成未来环境的多层次语义特征替代RGB图像预测，并结合并行评估构建未来路径树，提升连续视觉语言导航的前瞻探索效率与准确性。
摘要：
视觉语言导航（VLN）使智能体能够在三维环境中遵循自然语言指令导航到远程位置。在每个导航步骤中，智能体从可能的候选位置中进行选择，然后执行移动。为了更好地进行导航规划，前瞻探索策略旨在通过准确预测候选位置的未来环境来有效评估智能体的下一步行动。为此，一些现有工作预测未来环境的RGB图像，但这种策略存在图像失真和计算成本高的问题。为了解决这些问题，我们提出了预训练的分层神经辐射表示模型（HNR），用于生成未来环境的多层次语义特征，这些特征比逐像素RGB重建更鲁棒且更高效。此外，利用预测的未来环境表示，我们的前瞻VLN模型能够构建可导航的未来路径树，并通过高效的并行评估选择最优路径。在VLN-CE数据集上的大量实验证实了我们方法的有效性。
英文摘要：
Vision-and-language navigation (VLN) enables the agent to navigate to a remote location following the natural language instruction in 3D environments. At each navigation step, the agent selects from possible candidate locations and then makes the move. For better navigation planning, the lookahead exploration strategy aims to effectively evaluate the agent's next action by accurately anticipating the future environment of candidate locations. To this end, some existing works predict RGB images for future environments, while this strategy suffers from image distortion and high computational cost. To address these issues, we propose the pre-trained hierarchical neural radiance representation model (HNR) to produce multi-level semantic features for future environments, which are more robust and efficient than pixel-wise RGB reconstruction. Furthermore, with the predicted future environmental representations, our lookahead VLN model is able to construct the navigable future path tree and select the optimal path via efficient parallel evaluation. Extensive experiments on the VLN-CE datasets confirm the effectiveness of our method.
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。
