DensePhysNet：多段動的相互作用による密な物理物体表現の学習
DensePhysNet: Learning Dense Physical Object Representations via Multi-step Dynamic Interactions
物体を滑らせたり衝突させたりする動的相互作用を能動的に行い、その視覚観測から摩擦や質量などの物理特性を反映したピクセル単位の密な表現を学習する手法を提案した。
著者: Zhenjia Xu, Jiajun Wu, Andy Zeng, Joshua B. Tenenbaum, Shuran Song
分類: cs.RO, cs.AI, cs.CV, cs.LG
原文アブストラクト
We study the problem of learning physical object representations for robot manipulation. Understanding object physics is critical for successful object manipulation, but also challenging because physical object properties can rarely be inferred from the object's static appearance. In this paper, we propose DensePhysNet, a system that actively executes a sequence of dynamic interactions (e.g., sliding and colliding), and uses a deep predictive model over its visual observations to learn dense, pixel-wise representations that reflect the physical properties of observed objects. Our experiments in both simulation and real settings demonstrate that the learned representations carry rich physical information, and can directly be used to decode physical object properties such as friction and mass. The use of dense representation enables DensePhysNet to generalize well to novel scenes with more objects than in training. With knowledge of object physics, the learned representation also leads to more accurate and efficient manipulation in downstream tasks than the state-of-the-art.
