ScaleNav: Scalable Internet Video Pretraining for Language-Conditioned Local Navigation

Key points

• 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.1
• Internet egocentric videos offer a scalable source of real-world navigation experience.1
• To this end, we propose ScaleNav, a unified world-action framework with three-stage training.1
• We further deploy and evaluate ScaleNav on a real quadruped robot, with results supporting the model's ability to generalize to real-world navigation.1

Sources (2)

• [1]ScaleNav: Scalable Internet Video Pretraining for Language-Conditioned Local Navigation
  Hugging Face Daily Papers · Oct 19, 12:00 AM

  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.

• [2]ScaleNav: Scalable Internet Video Pretraining for Language-Conditioned Local Navigation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 02:37 AM · same content

Extractive summary: sentences quoted from the sources.