潜在ゴールモデルによる未知環境ナビゲーションの高速探索
Rapid Exploration for Open-World Navigation with Latent Goal Models
画像ゴールまでの距離と行動を潜在変数モデルで学習し、トポロジカルな画像メモリと組み合わせて、未知の障害物や天候の中でも80m先のゴールを20分以内に探索・到達する移動ロボットシステムを実現した。
著者: Dhruv Shah, Benjamin Eysenbach, Gregory Kahn, Nicholas Rhinehart, Sergey Levine
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
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. Please check out the project website for videos of our experiments and information about the real-world dataset used at https://sites.google.com/view/recon-robot.
