QueST: 連続制御学習のための自己教師ありスキル抽象化
QueST: Self-Supervised Skill Abstractions for Learning Continuous Control
潜在変数モデルで行動系列を圧縮し、因果的帰納バイアスを導入することで、マルチタスクや少数ショット学習に強い低レベルスキル表現を学習する手法を提案。
著者: Atharva Mete, Haotian Xue, Albert Wilcox, Yongxin Chen, Animesh Garg
分類: cs.RO
原文アブストラクト
Generalization capabilities, or rather a lack thereof, is one of the most important unsolved problems in the field of robot learning, and while several large scale efforts have set out to tackle this problem, unsolved it remains. In this paper, we hypothesize that learning temporal action abstractions using latent variable models (LVMs), which learn to map data to a compressed latent space and back, is a promising direction towards low-level skills that can readily be used for new tasks. Although several works have attempted to show this, they have generally been limited by architectures that do not faithfully capture shareable representations. To address this we present Quantized Skill Transformer (QueST), which learns a larger and more flexible latent encoding that is more capable of modeling the breadth of low-level skills necessary for a variety of tasks. To make use of this extra flexibility, QueST imparts causal inductive bias from the action sequence data into the latent space, leading to more semantically useful and transferable representations. We compare to state-of-the-art imitation learning and LVM baselines and see that QueST's architecture leads to strong performance on several multitask and few-shot learning benchmarks. Further results and videos are available at https://quest-model.github.io/
