制約付きスキル発見：教師なし強化学習による四足歩行
Constrained Skill Discovery: Quadruped Locomotion with Unsupervised Reinforcement Learning
距離制約下でスキルと状態の相互情報量を最大化し、ノルムマッチング目的で多様な歩行スキルを学習。実機ANYmalでゼロショット目標到達を実現した。
著者: Vassil Atanassov, Wanming Yu, Alexander Luis Mitchell, Mark Nicholas Finean, Ioannis Havoutis
分類: cs.RO
原文アブストラクト
Representation learning and unsupervised skill discovery can allow robots to acquire diverse and reusable behaviors without the need for task-specific rewards. In this work, we use unsupervised reinforcement learning to learn a latent representation by maximizing the mutual information between skills and states subject to a distance constraint. Our method improves upon prior constrained skill discovery methods by replacing the latent transition maximization with a norm-matching objective. This not only results in a much a richer state space coverage compared to baseline methods, but allows the robot to learn more stable and easily controllable locomotive behaviors. We successfully deploy the learned policy on a real ANYmal quadruped robot and demonstrate that the robot can accurately reach arbitrary points of the Cartesian state space in a zero-shot manner, using only an intrinsic skill discovery and standard regularization rewards.
