インターネット動画から物体操作を模倣学習する
Learning to Imitate Object Interactions from Internet Videos
インターネット動画から手と物体の4D軌道を再構成し、物理シミュレータ上で強化学習により様々な物体操作を模倣する手法を提案した。
著者: Austin Patel, Andrew Wang, Ilija Radosavovic, Jitendra Malik
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
We study the problem of imitating object interactions from Internet videos. This requires understanding the hand-object interactions in 4D, spatially in 3D and over time, which is challenging due to mutual hand-object occlusions. In this paper we make two main contributions: (1) a novel reconstruction technique RHOV (Reconstructing Hands and Objects from Videos), which reconstructs 4D trajectories of both the hand and the object using 2D image cues and temporal smoothness constraints; (2) a system for imitating object interactions in a physics simulator with reinforcement learning. We apply our reconstruction technique to 100 challenging Internet videos. We further show that we can successfully imitate a range of different object interactions in a physics simulator. Our object-centric approach is not limited to human-like end-effectors and can learn to imitate object interactions using different embodiments, like a robotic arm with a parallel jaw gripper.
