RoboTron-Nav: 知覚・計画・予測を統合した具現化ナビゲーションの統一フレームワーク
RoboTron-Nav: A Unified Framework for Embodied Navigation Integrating Perception, Planning, and Prediction
言語指示に基づく視覚ナビゲーションのため、知覚・計画・予測をマルチタスク学習で統合し、適応的な3D履歴サンプリングで過去の観測を効率的に活用するフレームワークを提案。CHORES-Sベンチマークで成功率81.1%を達成し、最先端性能を更新した。
著者: Yufeng Zhong, Chengjian Feng, Feng Yan, Fanfan Liu, Liming Zheng, Lin Ma
分類: cs.RO
原文アブストラクト
In language-guided visual navigation, agents locate target objects in unseen environments using natural language instructions. For reliable navigation in unfamiliar scenes, agents should possess strong perception, planning, and prediction capabilities. Additionally, when agents revisit previously explored areas during long-term navigation, they may retain irrelevant and redundant historical perceptions, leading to suboptimal results. In this work, we propose RoboTron-Nav, a unified framework that integrates perception, planning, and prediction capabilities through multitask collaborations on navigation and embodied question answering tasks, thereby enhancing navigation performances. Furthermore, RoboTron-Nav employs an adaptive 3D-aware history sampling strategy to effectively and efficiently utilize historical observations. By leveraging large language model, RoboTron-Nav comprehends diverse commands and complex visual scenes, resulting in appropriate navigation actions. RoboTron-Nav achieves an 81.1% success rate in object goal navigation on the $\mathrm{CHORES}$-$\mathbb{S}$ benchmark, setting a new state-of-the-art performance. Project page: https://yvfengzhong.github.io/RoboTron-Nav
