時間表現アラインメント：後継特徴がロボット指示追従における創発的な構成性を可能にする
Temporal Representation Alignment: Successor Features Enable Emergent Compositionality in Robot Instruction Following
現在と将来の状態表現を時間的に整合させる損失で学習することで、明示的なサブタスク計画や強化学習なしに、基本タスクの組み合わせからなる複合タスクをこなせる構成性がロボット操作タスクで向上することを示した論文。
著者: Vivek Myers, Bill Chunyuan Zheng, Anca Dragan, Kuan Fang, Sergey Levine
分類: cs.RO, cs.LG
原文アブストラクト
Effective task representations should facilitate compositionality, such that after learning a variety of basic tasks, an agent can perform compound tasks consisting of multiple steps simply by composing the representations of the constituent steps together. While this is conceptually simple and appealing, it is not clear how to automatically learn representations that enable this sort of compositionality. We show that learning to associate the representations of current and future states with a temporal alignment loss can improve compositional generalization, even in the absence of any explicit subtask planning or reinforcement learning. We evaluate our approach across diverse robotic manipulation tasks as well as in simulation, showing substantial improvements for tasks specified with either language or goal images.
