分布内バリア関数：分布外状態を回避する自己教師ありポリシーフィルタ
In-Distribution Barrier Functions: Self-Supervised Policy Filters that Avoid Out-of-Distribution States
オフラインの安全なデモデータから潜在空間でバリア関数を学習し、高次元視覚入力のみを使う制御器が訓練分布から外れた状態に入らないよう誘導するポリシーフィルタを提案した。
著者: Fernando Castañeda, Haruki Nishimura, Rowan McAllister, Koushil Sreenath, Adrien Gaidon
分類: cs.RO, cs.LG, cs.SY, eess.SY
原文アブストラクト
Learning-based control approaches have shown great promise in performing complex tasks directly from high-dimensional perception data for real robotic systems. Nonetheless, the learned controllers can behave unexpectedly if the trajectories of the system divert from the training data distribution, which can compromise safety. In this work, we propose a control filter that wraps any reference policy and effectively encourages the system to stay in-distribution with respect to offline-collected safe demonstrations. Our methodology is inspired by Control Barrier Functions (CBFs), which are model-based tools from the nonlinear control literature that can be used to construct minimally invasive safe policy filters. While existing methods based on CBFs require a known low-dimensional state representation, our proposed approach is directly applicable to systems that rely solely on high-dimensional visual observations by learning in a latent state-space. We demonstrate that our method is effective for two different visuomotor control tasks in simulation environments, including both top-down and egocentric view settings.
