DMotion: 動画から教師なしで順モデルを学習するロボット視覚運動制御
DMotion: Robotic Visuomotor Control with Unsupervised Forward Model Learned from Videos
ラベル付き動作や物体情報なしに動画データのみから、制御対象の動きを分離して順モデルを学習する手法を提案し、モデル予測制御によるロボット操作への有効性を示した。
著者: Haoqi Yuan, Ruihai Wu, Andrew Zhao, Haipeng Zhang, Zihan Ding, Hao Dong
分類: cs.RO
原文アブストラクト
Learning an accurate model of the environment is essential for model-based control tasks. Existing methods in robotic visuomotor control usually learn from data with heavily labelled actions, object entities or locations, which can be demanding in many cases. To cope with this limitation, we propose a method, dubbed DMotion, that trains a forward model from video data only, via disentangling the motion of controllable agent to model the transition dynamics. An object extractor and an interaction learner are trained in an end-to-end manner without supervision. The agent's motions are explicitly represented using spatial transformation matrices containing physical meanings. In the experiments, DMotion achieves superior performance on learning an accurate forward model in a Grid World environment, as well as a more realistic robot control environment in simulation. With the accurate learned forward models, we further demonstrate their usage in model predictive control as an effective approach for robotic manipulations.
