Rapid Exploration for Open-World Navigation with Latent Goal Models

Dhruv Shah, Benjamin Eysenbach, Nicholas Rhinehart, Sergey Levine

Research output: Contribution to journalConference articlepeer-review

15 Scopus citations

Abstract

We describe a robotic learning system for autonomous exploration and navigation in diverse, open-world environments. At the core of our method is a learned latent variable model of distances and actions, along with a non-parametric topological memory of images. We use an information bottleneck to regularize the learned policy, giving us (i) a compact visual representation of goals, (ii) improved generalization capabilities, and (iii) a mechanism for sampling feasible goals for exploration. Trained on a large offline dataset of prior experience, the model acquires a representation of visual goals that is robust to task-irrelevant distractors. We demonstrate our method on a mobile ground robot in open-world exploration scenarios. Given an image of a goal that is up to 80 meters away, our method leverages its representation to explore and discover the goal in under 20 minutes, even amidst previously-unseen obstacles and weather conditions.

Original languageEnglish (US)
Pages (from-to)674-684
Number of pages11
JournalProceedings of Machine Learning Research
Volume164
StatePublished - 2021
Externally publishedYes
Event5th Conference on Robot Learning, CoRL 2021 - London, United Kingdom
Duration: Nov 8 2021Nov 11 2021

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability

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