具有社会意识导航的自主机器人使用记忆辅助深度强化学习学习
Estrella Montero1, Nabih Pico2,3, Manuel S Alvarez-Alvarado4
1Department of Electrical and Computer Engineering, Sungkyunkwan University, Natural Sciences Campus, Suwon, South Korea.
Scientific reports
|March 13, 2026
概括
本研究介绍了自适应机器人安全算法 (ARSA),这是一种深度强化学习方法,可以提高机器人在拥挤的空间中的机器人导航. 通过记忆辅助决策,ARSA提高了成功率,减少了导航时间,同时确保了安全.
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
- 人与机器人的交互
背景情况:
- 在人类环境中服务机器人的成功取决于灵活的导航.
- 人类在拥挤的空间中的随机性和活力挑战了机器人导航.
- 现有的方法与动态的人类行为作斗争.
研究的目的:
- 为以人为中心的环境中服务机器人开发强大的导航系统.
- 通过深度强化学习来应对动态人类行为的挑战.
- 通过优先考虑人类互动和安全,增强机器人的决策能力.
主要方法:
- 实施了一种深度强化学习方法,称为自适应机器人安全算法 (ARSA).
- 集成的双向封闭反复单元层,用于长期环境记忆.
- 集成的动态预警区和优先考虑的人类行为在学习政策中.
主要成果:
- 在模拟中,ARSA政策提高了4%的成功率.
- 保持碰撞率低于4%,航行时间缩短了14%.
- 通过现实世界的实验验证,显示流,无碰撞的导航.
结论:
- 在动态的人类环境中,ARSA框架为机器人导航提供了卓越的效率和安全性.
- 记忆辅助的深度强化学习有效地处理环境随机性.
- ARSA使机器人能够主动和知情地做出决定,从而实现更安全的人机交互.
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