目標パラメータ化による再標的可能な視覚運動スキルの学習
Learning Deep Parameterized Skills from Demonstration for Re-targetable Visuomotor Control
目標をパラメータ化したニューラルネット方策として視覚運動スキルを学習し、未見の目標にもゼロショットで汎化できることをボタン押しやペグ挿入タスクで示した論文。
著者: Jonathan Chang, Nishanth Kumar, Sean Hastings, Aaron Gokaslan, Diego Romeres, Devesh Jha, Daniel Nikovski, George Konidaris, Stefanie Tellex
分類: cs.RO, cs.LG
原文アブストラクト
Robots need to learn skills that can not only generalize across similar problems but also be directed to a specific goal. Previous methods either train a new skill for every different goal or do not infer the specific target in the presence of multiple goals from visual data. We introduce an end-to-end method that represents targetable visuomotor skills as a goal-parameterized neural network policy. By training on an informative subset of available goals with the associated target parameters, we are able to learn a policy that can zero-shot generalize to previously unseen goals. We evaluate our method in a representative 2D simulation of a button-grid and on both button-pressing and peg-insertion tasks on two different physical arms. We demonstrate that our model trained on 33% of the possible goals is able to generalize to more than 90% of the targets in the scene for both simulation and robot experiments. We also successfully learn a mapping from target pixel coordinates to a robot policy to complete a specified goal.
