ScaleNav: 言語条件付き局所ナビゲーションのためのスケーラブルなインターネット動画事前学習
ScaleNav: Scalable Internet Video Pretraining for Language-Conditioned Local Navigation
インターネット上の一人称視点動画から目標指向の視覚ダイナミクスを学習し、シミュレーションデータで行動を整合させて、言語指示に従うロボットナビゲーションを実現する世界-行動フレームワークを提案した論文。
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著者: Yihui Yang, Yuanhong Yu, Jiawei Wang, Zhongrui Yu, Jia Shi, Kun Zhan, Ruizhen Hu, Xiaowei Zhou, Yu Liu, Sida Peng
分類: cs.RO
原文アブストラクト
We study language-conditioned local navigation, where a robot must navigate to a text-specified target visible in its initial observation and stop at the described location. End-to-end navigation policies require diverse training data to generalize across scenes, yet real-robot data is costly to collect and simulated data leaves a sim-to-real gap. Internet egocentric videos offer a scalable source of real-world navigation experience. Our key insight is to learn goal-directed visual dynamics from Internet egocentric videos, and use these dynamics to support robot action generation. To this end, we propose ScaleNav, a unified world-action framework with three-stage training. We first perform instruction-conditioned video pretraining on these videos to learn how observations evolve as the camera approaches instructed targets across diverse real-world scenes. Action alignment then uses action-labeled simulated data to train the model to predict executable action chunks. Joint fine-tuning subsequently optimizes all trainable components together for navigation. ScaleNav achieves success rates of 86.5% on a single-view adaptation of Short-Horizon OVON and 94.9% on our HabitatGS-LN benchmark, outperforming the evaluated baselines on both benchmarks. We further deploy and evaluate ScaleNav on a real quadruped robot, with results supporting the model's ability to generalize to real-world navigation. Code and videos are available at https://zju3dv.github.io/ScaleNav/
