学習した抽象表現による反応型高レベルタスクの自動符号化と修復
Automatic Encoding and Repair of Reactive High-Level Tasks with Learned Abstract Representations
ロボットのスキルをセンサデータから記号化してLTLで自動符号化し、反応型高レベルタスクの戦略合成と、実行不能な場合のスキル追加・修正案の自動提示を行うフレームワークを提案。
著者: Adam Pacheck, Steven James, George Konidaris, Hadas Kress-Gazit
分類: cs.RO
原文アブストラクト
We present a framework that, given a set of skills a robot can perform, abstracts sensor data into symbols that we use to automatically encode the robot's capabilities in Linear Temporal Logic. We specify reactive high-level tasks based on these capabilities, for which a strategy is automatically synthesized and executed on the robot, if the task is feasible. If a task is not feasible given the robot's capabilities, we present two methods, one enumeration-based and one synthesis-based, for automatically suggesting additional skills for the robot or modifications to existing skills that would make the task feasible. We demonstrate our framework on a Baxter robot manipulating blocks on a table, a Baxter robot manipulating plates on a table, and a Kinova arm manipulating vials, with multiple sensor modalities, including raw images.
