能動センシング歩行ポリシーによる物理特性の視覚推定学習
Learning to See Physical Properties with Active Sensing Motor Policies
四足ロボットが歩行中に地面をこするなど能動的に探索して物理特性を推定し、そのデータで画像から地形の摩擦などを予測する視覚システムを自己教師あり学習する手法。
著者: Gabriel B. Margolis, Xiang Fu, Yandong Ji, Pulkit Agrawal
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
Knowledge of terrain's physical properties inferred from color images can aid in making efficient robotic locomotion plans. However, unlike image classification, it is unintuitive for humans to label image patches with physical properties. Without labeled data, building a vision system that takes as input the observed terrain and predicts physical properties remains challenging. We present a method that overcomes this challenge by self-supervised labeling of images captured by robots during real-world traversal with physical property estimators trained in simulation. To ensure accurate labeling, we introduce Active Sensing Motor Policies (ASMP), which are trained to explore locomotion behaviors that increase the accuracy of estimating physical parameters. For instance, the quadruped robot learns to swipe its foot against the ground to estimate the friction coefficient accurately. We show that the visual system trained with a small amount of real-world traversal data accurately predicts physical parameters. The trained system is robust and works even with overhead images captured by a drone despite being trained on data collected by cameras attached to a quadruped robot walking on the ground.
