在边缘使用自我监督的认知地图学习器的无地图移动机器人导航
Ioannis Polykretis1, Andreea Danielescu1
1Accenture Labs, San Francisco, CA, United States.
Frontiers in robotics and AI
|June 6, 2024
概括
我们开发了一种以大脑为灵感的导航方法,用于在未知环境中的移动代理. 这种方法使用浅层架构和本地学习,将最先进的性能与边缘硬件的复杂性降低相匹配.
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
- 计算神经科学是一种神经科学.
背景情况:
- 在未知的环境中移动代理导航对于自主性至关重要.
- 深度增强学习 (DRL) 是有前途的,但在复杂性,内存和边缘硬件适应方面面临挑战.
- 当前的DRL方法通常需要复杂的奖励函数和大量的计算资源.
研究的目的:
- 为移动代理提供一种由大脑启发的,自我监督的导航方法.
- 克服对资源有限环境的复杂DRL方法的局限性.
- 为了在边缘神经形态处理器上实现高效导航.
主要方法:
- 用本地学习规则训练的浅层神经网络架构.
- 自主监督的学习结合了基于新奇的和随机的探索步行.
- 消除错误反向传播以减少内存开销.
主要成果:
- 实现了与最先进的深度Q网络 (DQN) 相同的目标达到精度和路径长度.
- 与DQN相比,利用了类似或更少的参数,操作和训练代数.
- 证明了减少内存足迹和适合边缘神经形态硬件的适用性.
结论:
- 提出的由大脑启发的方法为自主导航提供了一个有效的替代方案.
- 自主监督学习和浅层架构增强了代理人的自主性和适应性.
- 这种方法促进了资源效率高的体内神经形态剂的开发.
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