No RL, No Simulation: Learning to Navigate without Navigating

Meera Hahn, Devendra Singh Chaplot, Shubham Tulsiani, Mustafa Mukadam, James Matthew Rehg, Abhinav Gupta

Keywords: Navigation, Simulation, Graph Neural Networks, Embodied AI

Abstract: Most prior methods for learning navigation policies require access to simulation environments, as they need online policy interaction and rely on ground-truth maps for rewards. However, building simulators is expensive (requires manual effort for each and every scene) and creates challenges in transferring learned policies to robotic platforms in the real-world, due to the sim-to-real domain gap. In this paper, we pose a simple question: Do we really need active interaction, ground-truth maps or even reinforcement-learning (RL) in order to solve the image-goal navigation task? We propose a self-supervised approach to learn to navigate from only passive videos of roaming. Our approach, No RL, No Simulator (NRNS), is simple and scalable, yet highly effective. NRNS outperforms RL-based formulations by a significant margin. We present NRNS as a strong baseline for any future image-based navigation tasks that use RL or Simulation.

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TL;DR: We present a navigation method which does not rely on RL or simulation. It uses simple self-supervised distance learning from passive house tour videos along with greedy decision-making to outperform RL/IL algorithms on image-goal navigation.

Code: https://github.com/meera1hahn/NRNS/