从实时适应到机器人生态系统中的社会学习
Alex Szorkovszky1,2, Frank Veenstra1,2, Kyrre Glette1,2
1RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway.
Frontiers in robotics and AI
|October 20, 2023
概括
这项研究引入了机器人的社会学习,使他们能够快速适应环境变化. 机器人同步运动以学习新的步态,培养多样性,并可能创造适应性的通用机器人.
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
- 进化计算是一种进化计算.
背景情况:
- 进化机器人擅长创造适应的形态和控制器.
- 机器人的短期环境适应最有效地通过学习来实现.
- 现有的机器人学习方法通常涉及从头开始或使用直接祖先的数据.
研究的目的:
- 为机器人步行模式提出和演示一种新的社会学习方法.
- 为了利用传感运动同步进行机器人间学习.
- 探索机器人群体中新兴通用主义行为的潜力.
主要方法:
- 利用其他机器人的运动模式作为去中心化控制器的输入,例如中央模式生成器 (CPG).
- 采用传感运动同步驱动CPG进入新的,多样化的极限周期.
- 实施一个准-Hebbian反方案来稳定学习的自主控制器.
主要成果:
- 证明其他机器人的运动模式可以在去中心化控制器中诱导新的步态模式.
- 展示了通过准赫比反机制锁定稳定的自主控制器的能力.
- 提供了机器人系统中社会学习的框架.
结论:
- 通过传感运动同步的社会学习为机器人提供了有效的适应机制.
- 这种方法鼓励运动的多样性,并可能导致泛型任务解决者的出现.
- 拟议的方法对在异质环境中演变的机器人群体有影响.
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