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首页> 美国卫生研究院文献>Frontiers in Neurorobotics >Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot
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Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot

机译：递归神经网络中的突触可塑性用于行走机器人的多功能和自适应行为

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Walking animals, like insects, with little neural computing can effectively perform complex behaviors. For example, they can walk around their environment, escape from corners/deadlocks, and avoid or climb over obstacles. While performing all these behaviors, they can also adapt their movements to deal with an unknown situation. As a consequence, they successfully navigate through their complex environment. The versatile and adaptive abilities are the result of an integration of several ingredients embedded in their sensorimotor loop. Biological studies reveal that the ingredients include neural dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a many degrees-of-freedom (DOFs) walking robot is a challenging task. Thus, in this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural mechanisms with plasticity, exteroceptive sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent neural network consisting of two fully connected neurons. Online correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a walking robot. The turning information is transmitted as descending steering signals to the neural locomotion control which translates the signals into motor actions. As a result, the robot can walk around and adapt its turning angle for avoiding obstacles in different situations. The adaptation also enables the robot to effectively escape from sharp corners or deadlocks. Using backbone joint control embedded in the the locomotion control allows the robot to climb over small obstacles. Consequently, it can successfully explore and navigate in complex environments. We firstly tested our approach on a physical simulation environment and then applied it to our real biomechanical walking robot AMOSII with 19 DOFs to adaptively avoid obstacles and navigate in the real world.
机译：几乎没有神经计算的步行动物（如昆虫）可以有效地执行复杂的行为。例如，他们可以在周围的环境中走动，躲避弯道/死锁，避开或越过障碍物。在执行所有这些行为时，他们还可以调整动作以应对未知情况。结果，他们成功地在复杂的环境中导航。多功能和自适应能力是其感觉运动回路中嵌入的几种成分的集成的结果。生物学研究表明，这些成分包括神经动力学，可塑性，感觉反馈和生物力学。对于许多自由度（DOF）行走机器人生成这样的通用和自适应行为是一项艰巨的任务。因此，在这项研究中，我们提出了一种生物启发的方法来解决这一任务。具体而言，该方法将神经机制与可塑性，外在感觉反馈和生物力学结合在一起。神经机制包括自适应神经感觉处理和模块化神经运动控制。感觉处理基于由两个完全连接的神经元组成的小型递归神经网络。具有突触缩放功能的基于在线相关性的学习被应用于适当地改变网络的连接。通过这样做，我们可以有效地利用网络中的神经动力学（即磁滞效应和单个吸引子）来为步行机器人生成具有短期记忆的不同转向角。转向信息作为向下的转向信号传输到神经运动控制系统，该神经运动控制系统将信号转换为运动动作。结果，机器人可以四处走动并调整其转向角度，以避免在不同情况下出现障碍物。这种适应还使机器人能够有效地摆脱尖角或死锁。使用嵌入在运动控制中的骨干关节控制，机器人可以越过小障碍物。因此，它可以在复杂的环境中成功地探索和导航。我们首先在物理模拟环境上测试了我们的方法，然后将其应用于具有19个自由度的真实生物力学步行机器人AMOSII，以自适应地避开障碍物并在现实世界中导航。

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