基于深度强化学习的平衡表合作机器人
Yewon Kim1, Dae-Won Kim2, Bo-Yeong Kang3
1Department of Artificial Intelligence, Kyungpook National University, Daegu 41566, Republic of Korea.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
本研究介绍了一种使用深度强化学习 (DRL) 来与人类平衡桌面的合作机器人. DRL机器人学习人类行为,在现实世界测试中达到90%的精度.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 传统的强化学习 (RL) 往往侧重于单个代理任务.
- 许多现实世界的任务,如平衡桌子,需要人机合作,以确保安全和效率.
- 现有的研究缺乏强大的方法,使机器人能够在协作任务中动态适应人类的行为.
研究的目的:
- 开发一种基于深度强化学习 (DRL) 的技术,使机器人能够与人类合作平衡桌子.
- 使机器人能够识别和响应人类在共享任务中的行为.
- 在人机协作场景中增强机器人的自主性和安全性.
主要方法:
- 使用深度Q网络 (DQN),DRL算法,用于机器人控制.
- 整合了一个摄像头系统,使机器人能够感知桌子的状态和人类的互动.
- 通过模拟和现实世界 (H / W) 实验训练机器人,重点是合作式桌子平衡.
主要成果:
- 合作机器人在20次训练中以优化的超参数显示了90%的平均最佳政策融合率.
- 在硬件实验中,基于DQN的机器人实现了90%的操作精度.
- 该系统成功地学会了与人类合作平衡桌子,适应他们的动作.
结论:
- 拟议的DRL技术有效地使机器人能够与人类一起执行协作式平衡.
- 基于DQN的方法在识别人类行为和执行反平衡行动方面表现出高性能和精度.
- 这项研究证实了DRL在动态物理任务中开发复杂的人机协作的潜力.
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