視覚観察から行動可能な表現を学習する
Learning Actionable Representations from Visual Observations
複数フレームを同時に埋め込むことで位置と速度をより正確に符号化する自己教師あり表現学習を提案し、その埋め込みのみを入力としてPPOで連続制御方策を学習できることを示した。
著者: Debidatta Dwibedi, Jonathan Tompson, Corey Lynch, Pierre Sermanet
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
In this work we explore a new approach for robots to teach themselves about the world simply by observing it. In particular we investigate the effectiveness of learning task-agnostic representations for continuous control tasks. We extend Time-Contrastive Networks (TCN) that learn from visual observations by embedding multiple frames jointly in the embedding space as opposed to a single frame. We show that by doing so, we are now able to encode both position and velocity attributes significantly more accurately. We test the usefulness of this self-supervised approach in a reinforcement learning setting. We show that the representations learned by agents observing themselves take random actions, or other agents perform tasks successfully, can enable the learning of continuous control policies using algorithms like Proximal Policy Optimization (PPO) using only the learned embeddings as input. We also demonstrate significant improvements on the real-world Pouring dataset with a relative error reduction of 39.4% for motion attributes and 11.1% for static attributes compared to the single-frame baseline. Video results are available at https://sites.google.com/view/actionablerepresentations .
