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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
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相关实验视频

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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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走向基于事件的自主传感运动控制与监督步态学习和避开障碍的机器人导航.

Shahin Hashemkhani1, Vijay Shankaran Vivekanand1, Samarth Chopra1

  • 1Department of Electrical and Computer Engineering (ECE), Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, United States.

Frontiers in neuroscience
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概括

本研究介绍了一种生物灵感的自主微型机器人的框架,可以在具有挑战性的环境中使用基于事件的传感运动控制和层次系统进行自适应导航. 机器人学习步态,以避免在资源有限的情况下遇到障碍.

关键词:
中央模式发生器结合的神经网络 结合的神经网络动态状态机器的动态状态机器多次时间尺度的反.传感器运动控制器峰值时间依赖的可塑性

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科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 神经科学是一个神经科学.
  • 人工智能的人工智能

背景情况:

  • 微型机器人需要自主导航,以应对灾害并进入危险区域.
  • 资源限制 (计算,存储,电力) 限制了边缘机器人的无监督操作.
  • 基于事件的感应运动控制提供了自适应导航和实时决策.

研究的目的:

  • 为自主迷你机器人导航提供一种新的生物灵感的等级控制框架.
  • 在机器人控制中解决资源限制和环境变化的局限性.
  • 为了实现自适应步态学习和实时避开障碍.

主要方法:

  • 使用可调节的多层神经网络与硬件友好的中央模式生成器 (CPG) 进行运动定时.
  • 实现了一个动态状态机器 (DSM) 进行分层自主操作和适应性.
  • 采用了一种非线性神经元模型,用于节奏运动控制模式的混合反.
  • 应用监督的尖端时间依赖可塑性 (STDP) 进行自主步行学习 (走路,爬行).

主要成果:

  • 在Petoi机器人平台上展示了自主步行学习和状态转换.
  • 展示了DSM管理的实时避障能力.
  • 在不平坦的地形上实现了适应性导航,并对环境波动作出反应.
  • 介绍了框架架构和网络方程的全面分析.

结论:

  • 拟议的生物灵感框架使微型机器人在严重的资源限制下能够进行强大,适应和自主导航.
  • 综合了CPG和DSM的等级控制系统,有效地管理了复杂的行为,如步态学习和避开障碍.
  • 这种方法推进了边缘机器人技术,用于在不可预测的环境中需要智能,自给自足的移动代理的应用.