人間のデモ映像から幾何学的スキルを推論する手法
Visual Geometric Skill Inference by Watching Human Demonstration
人間のデモ動画から幾何特徴間の関連制約をグラフベースのカーネル回帰と逆強化学習で直接推論し、従来の特徴選択や追跡を不要にする手法を提案した。
著者: Jun Jin, Laura Petrich, Zichen Zhang, Masood Dehghan, Martin Jagersand
分類: cs.RO
原文アブストラクト
We study the problem of learning manipulation skills from human demonstration video by inferring the association relationships between geometric features. Motivation for this work stems from the observation that humans perform eye-hand coordination tasks by using geometric primitives to define a task while a geometric control error drives the task through execution. We propose a graph based kernel regression method to directly infer the underlying association constraints from human demonstration video using Incremental Maximum Entropy Inverse Reinforcement Learning (InMaxEnt IRL). The learned skill inference provides human readable task definition and outputs control errors that can be directly plugged into traditional controllers. Our method removes the need for tedious feature selection and robust feature trackers required in traditional approaches (e.g. feature-based visual servoing). Experiments show our method infers correct geometric associations even with only one human demonstration video and can generalize well under variance.
