对具有参数不确定性的领域进行神经机器人强化学习
Camilo Amaya1, Axel von Arnim1
1Department of Neuromorphic Computing, Fortiss-Research Institute, Munich, Bavaria, Germany.
Frontiers in neurorobotics
|November 15, 2023
概括
神经形态硬件和尖端增强学习使机器人臂能有效控制. 这种新的方法,在Peg-in-hole任务中进行了演示,通过弥合模拟与现实之间的差距,提高了现实应用的稳定性.
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
- 神经科学是一个神经科学.
背景情况:
- 神经形态硬件为机器人控制提供了低能耗和延迟.
- 大脑启发的学习策略对于推进机器人能力至关重要.
- 在模拟中整合神经形态系统是改善机器人控制的关键.
研究的目的:
- 使用神经形态硬件实现用于机器人手臂控制的增强学习.
- 通过随机模拟参数来解决机器人控制中的模拟与真实差距.
- 为了展示第一个神经形态实现与硬件在循环中的Peg-in-hole任务.
主要方法:
- 使用神经机器人平台 (NRP) 模拟框架.
- 实现了尖增强学习,并为入洞任务提供力-扭矩反.
- 集成了神经形态的Loihi芯片用于硬件在循环中的控制,并利用脚本加快训练.
主要成果:
- 通过使用神经形态硬件在模拟入洞任务中成功控制了机器人手臂.
- 通过在随机模拟环境中进行培训,制定了强有力的政策.
- 尽管存在现实世界的参数变化,但实现了有效的控制,弥合了模拟与现实之间的差距.
结论:
- 神经形态硬件和尖端增强学习为强大的机器人控制提供了可行的解决方案.
- 这项研究表明神经形态机器人技术取得了重大进展,特别是在需要精确操纵的任务中.
- 这项工作为在复杂环境中更具适应性和效率的机器人系统铺平了道路.
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