A hierarchical deep reinforcement learning algorithm for typing with a dual-arm humanoid robot

Jacky Baltes, Hanjaya Mandala, Saeed Saeedvand*

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

摘要

Recently, the field of robotics development and control has been advancing rapidly. Even though humans effortlessly manipulate everyday objects, enabling robots to interact with human-made objects in real-world environments remains a challenge despite years of dedicated research. For example, typing on a keyboard requires adapting to various external conditions, such as the size and position of the keyboard, and demands high accuracy from a robot to be able to use it properly. This paper introduces a novel hierarchical reinforcement learning algorithm based on the Deep Deterministic Policy Gradient (DDPG) algorithm to address the dual-arm robot typing problem. In this regard, the proposed algorithm employs a Convolutional Auto-Encoder (CAE) to deal with the associated complexities of continuous state and action spaces at the first stage, and then a DDPG algorithm serves as a strategy controller for the typing problem. Using a dual-arm humanoid robot, we have extensively evaluated our proposed algorithm in simulation and real-world experiments. The results showcase the high efficiency of our approach, boasting an average success rate of 96.14% in simulations and 92.2% in real-world settings. Furthermore, we demonstrate that our proposed algorithm outperforms DDPG and Deep Q-Learning, two frequently employed algorithms in robotic applications.

原文英語
文章編號e7
期刊Knowledge Engineering Review
39
DOIs
出版狀態已發佈 - 2024 11月 20

ASJC Scopus subject areas

  • 軟體
  • 人工智慧

指紋

深入研究「A hierarchical deep reinforcement learning algorithm for typing with a dual-arm humanoid robot」主題。共同形成了獨特的指紋。

引用此