専門家の実演からリアルタイム修正を導く修正型共有自律
Informing Real-time Corrections in Corrective Shared Autonomy Through Expert Demonstrations
学習による実演からロボットの基本動作と修正すべき変数を自動抽出し、修正型共有自律を効率化する手法を提案し、清掃タスクで有効性を検証した。
著者: Michael Hagenow, Emmanuel Senft, Robert Radwin, Michael Gleicher, Bilge Mutlu, Michael Zinn
分類: cs.RO
原文アブストラクト
Corrective Shared Autonomy is a method where human corrections are layered on top of an otherwise autonomous robot behavior. Specifically, a Corrective Shared Autonomy system leverages an external controller to allow corrections across a range of task variables (e.g., spinning speed of a tool, applied force, path) to address the specific needs of a task. However, this inherent flexibility makes the choice of what corrections to allow at any given instant difficult to determine. This choice of corrections includes determining appropriate robot state variables, scaling for these variables, and a way to allow a user to specify the corrections in an intuitive manner. This paper enables efficient Corrective Shared Autonomy by providing an automated solution based on Learning from Demonstration to both extract the nominal behavior and address these core problems. Our evaluation shows that this solution enables users to successfully complete a surface cleaning task, identifies different strategies users employed in applying corrections, and points to future improvements for our solution.
