長期的自律性に向けて：ロボット学習の視点から
Towards Long-term Autonomy: A Perspective from Robot Learning
変化する環境で長期自律動作するにはオンサイト・オンザフライ学習が重要だと論じ、その前提となるデータと実運用展開について考察した。
著者: Zhi Yan, Li Sun, Tomas Krajnik, Tom Duckett, Nicola Bellotto
分類: cs.RO, cs.AI
原文アブストラクト
In the future, service robots are expected to be able to operate autonomously for long periods of time without human intervention. Many work striving for this goal have been emerging with the development of robotics, both hardware and software. Today we believe that an important underpinning of long-term robot autonomy is the ability of robots to learn on site and on-the-fly, especially when they are deployed in changing environments or need to traverse different environments. In this paper, we examine the problem of long-term autonomy from the perspective of robot learning, especially in an online way, and discuss in tandem its premise "data" and the subsequent "deployment".
