身体性を活かした生成的な物体モデルの能動学習
Embodied Active Learning of Generative Sensor-Object Models
ロボットが未知の物体を将来見つけられるよう、潜在的な不確かさに基づいて能動的にデータを収集し、画像特徴を学習する手法を提案。実機とシミュレーションで検証した。
著者: Allison Pinosky, Todd D. Murphey
分類: cs.RO
原文アブストラクト
When a robot encounters a novel object, how should it respond$\unicode{x2014}$what data should it collect$\unicode{x2014}$so that it can find the object in the future? In this work, we present a method for learning image features of an unknown number of novel objects. To do this, we use active coverage with respect to latent uncertainties of the novel descriptions. We apply ergodic stability and PAC-Bayes theory to extend statistical guarantees for VAEs to embodied agents. We demonstrate the method in hardware with a robotic arm; the pipeline is also implemented in a simulated environment. Algorithms and simulation are available open source, see http://sites.google.com/u.northwestern.edu/embodied-learning-hardware .
