Efficient multitask learning with an embodied predictive model for door opening and entry with whole-body control

Hiroshi Ito*, Kenjiro Yamamoto, Hiroki Mori, Tetsuya Ogata

*この研究の対応する著者

研究成果: Article査読

1 被引用数 (Scopus)

抄録

Robots need robust models to effectively perform tasks that humans do on a daily basis. These models often require substantial developmental costs to maintain because they need to be adjusted and adapted over time. Deep reinforcement learning is a powerful approach for acquiring complex real-world models because there is no need for a human to design the model manually. Furthermore, a robot can establish new motions and optimal trajectories that may not have been considered by a human. However, the cost of learning is an issue because it requires a huge amount of trial and error in the real world. Here, we report a method for realizing complicated tasks in the real world with low design and teaching costs based on the principle of prediction error minimization. We devised a module integration method by introducing a mechanism that switches modules based on the prediction error of multiple modules. The robot generates appropriate motions according to the door’s position, color, and pattern with a low teaching cost. We also show that by calculating the prediction error of each module in real time, it is possible to execute a sequence of tasks (opening door outward and passing through) by linking multiple modules and responding to sudden changes in the situation and operating procedures. The experimental results show that the method is effective at enabling a robot to operate autonomously in the real world in response to changes in the environment.

本文言語English
論文番号eaax8177
ジャーナルScience Robotics
7
65
DOI
出版ステータスPublished - 2022 4月 1

ASJC Scopus subject areas

  • 機械工学
  • コンピュータ サイエンスの応用
  • 制御と最適化
  • 人工知能

フィンガープリント

「Efficient multitask learning with an embodied predictive model for door opening and entry with whole-body control」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル