専門家の観察によるマルチロボット方策の推定と学習
Inferring and Learning Multi-Robot Policies by Observing an Expert
専門家ロボットチームの未注釈観察から各ロボットの制御器を推定し、その履歴でニューラルネット方策を学習して、同じ制御器群を持つ未学習チームに同等のミッション遂行性能を与える手法を提案した。
著者: Pietro Pierpaoli, Harish Ravichandar, Nicholas Waytowich, Anqi Li, Derrik Asher, Magnus Egerstedt
分類: cs.RO
原文アブストラクト
We present a technique for learning how to solve a multi-robot mission that requires interaction with an external environment by observing an expert system executing the same mission. We define the expert system as a team of robots equipped with a library of controllers, each designed to solve a specific task, supervised by an expert policy that appropriately selects controllers based on the states of robots and environment. The objective is for an un-trained team of robots (i.e., imitator system) equipped with the same library of controllers, but agnostic to the expert policy, to execute the mission, with performances comparable to those of the expert system. From un-annotated observations of the expert system, a multi-hypothesis filtering technique is used to estimate individual controllers executed by the expert policy. Then, the history of estimated controllers and environmental states is used to train a neural network policy for the imitator system. Considering a perimeter protection scenario on a team of differential-drive robots, we show that the learned policy endows the imitator system with performances comparable to those of the expert system.
