An autonomous learning mobile robot using biological reward modulate STDP

Lü, Hao; Liu, Junxiu; Luo, Yuling; Hua, Yifan; Qiu, Senhui; Huang, Yongchuang

doi:10.1016/j.neucom.2021.06.027

Cited by 19 publications

(10 citation statements)

References 31 publications

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“…Some differential equation models illustrate these neural dynamics with high biological plausibility but prohibitive computational cost such as Hodgin and Huxley or Izhikevich models (Izhikevich, 2004 ; Valadez-Godínez et al, 2020 ). Nonetheless, others with a lesser plausibility can compute the membrane potential with less effort degrading the accuracy, but are still useful as a good model approximation, due that spikes generation with the same characteristics as biological neurons might not be necessary for circuit implementations, such as the Leaky Integrate and Fire (LIF) (Lu et al, 2021 ) model, given by:…”

Section: Methodsmentioning

confidence: 99%

“…On the other hand, if no spikes arrive at the neuron, the current decays. This phenomenon is described by LIF conductance-based model (Hao et al, 2020 ; Lu et al, 2021 ), composed of Equations (4 and 5):…”

Section: Methodsmentioning

confidence: 99%

“…A similar task is studied numerically by Bing et al ( 2019b ), controlling a snake's movement instead. Lu et al ( 2021 ) achieve obstacle avoidance for an Ackerman-type mobile robot, using two neurons and two synapses, implemented on a digital development board. Bing et al ( 2019a ) achieve obstacle avoidance and goal-reaching for a differential robot, implementing separate neural control structures for each task.…”

Section: Introductionmentioning

confidence: 99%

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R-STDP Spiking Neural Network Architecture for Motion Control on a Changing Friction Joint Robotic Arm

et al. 2022

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Neuromorphic computing is a recent class of brain-inspired high-performance computer platforms and algorithms involving biologically-inspired models adopting hardware implementation in integrated circuits. The neuromorphic computing applications have provoked the rise of highly connected neurons and synapses in analog circuit systems that can be used to solve today's challenging machine learning problems. In conjunction with biologically plausible learning rules, such as the Hebbian learning and memristive devices, biologically-inspired spiking neural networks are considered the next-generation neuromorphic hardware construction blocks that will enable the deployment of new analog in situ learning capable and energetic efficient brain-like devices. These features are envisioned for modern mobile robotic implementations, currently challenging to overcome the pervasive von Neumann computer architecture. This study proposes a new neural architecture using the spike-time-dependent plasticity learning method and step-forward encoding algorithm for a self tuning neural control of motion in a joint robotic arm subjected to dynamic modifications. Simulations were conducted to demonstrate the proposed neural architecture's feasibility as the network successfully compensates for changing dynamics at each simulation run.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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R-STDP Spiking Neural Network Architecture for Motion Control on a Changing Friction Joint Robotic Arm

et al. 2022

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“…The difference between these neuron models is the level of detail in describing the behavior of biological neurons (Liu et al, 2019a , b ). Since the purpose of using neurons in this article is to verify the effectiveness of the proposed algorithm, two types of simple neuron models are chosen (Lu et al, 2021 ). The first type is the IF neuron model, which is used to encode external environment information of mobile robots into spike trains.…”

Section: Methodsmentioning

confidence: 99%

Bio-Inspired Autonomous Learning Algorithm With Application to Mobile Robot Obstacle Avoidance

et al. 2022

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Spiking Neural Networks (SNNs) are often considered the third generation of Artificial Neural Networks (ANNs), owing to their high information processing capability and the accurate simulation of biological neural network behaviors. Though the research for SNNs has been quite active in recent years, there are still some challenges to applying SNNs to various potential applications, especially for robot control. In this study, a biologically inspired autonomous learning algorithm based on reward modulated spike-timing-dependent plasticity is proposed, where a novel rewarding generation mechanism is used to generate the reward signals for both learning and decision-making processes. The proposed learning algorithm is evaluated by a mobile robot obstacle avoidance task and experimental results show that the mobile robot with the proposed algorithm exhibits a good learning ability. The robot can successfully avoid obstacles in the environment after some learning trials. This provides an alternative method to design and apply the bio-inspired robot with autonomous learning capability in the typical robotic task scenario.

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“…Meanwhile, recent studies [21,22] have shown that spiking neural networks (SNNs) [23] are beneficial for navigational control applications. SNNs process information in an eventbased manner using spikes, which holds the potential for fast and energy-efficient computing and makes them suitable for hardware implementation [24,25].…”

Section: Introductionmentioning

confidence: 99%

A Spiking Neural Network for Autonomous Search and Contour Tracking Inspired by C. elegans Chemotaxis and the Lévy Walk

2022

KSII TIIS

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Caenorhabditis elegans exhibits sophisticated chemotaxis behavior through two parallel strategies, klinokinesis and klinotaxis, executed entirely by a small nervous circuit. It is therefore suitable for inspiring fast and energy-efficient solutions for autonomous navigation. As a random search strategy, the Lévy walk is optimal for diverse animals when foraging without external chemical cues. In this study, by combining these biological strategies for the first time, we propose a spiking neural network model for search and contour tracking of specific concentrations of environmental variables. Specifically, we first design a klinotaxis module using spiking neurons. This module works in conjunction with a klinokinesis module, allowing rapid searches for the concentration setpoint and subsequent contour tracking with small deviations. Second, we build a random exploration module. It generates a Lévy walk in the absence of concentration gradients, increasing the chance of encountering gradients. Third, considering local extrema traps, we develop a termination module combined with an escape module to initiate or terminate the escape in a timely manner. Experimental results demonstrate that the proposed model integrating these modules can switch strategies autonomously according to the information from a single sensor and control steering through output spikes, enabling the model worm to efficiently navigate across various scenarios.

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An autonomous learning mobile robot using biological reward modulate STDP

Cited by 19 publications

References 31 publications

R-STDP Spiking Neural Network Architecture for Motion Control on a Changing Friction Joint Robotic Arm

R-STDP Spiking Neural Network Architecture for Motion Control on a Changing Friction Joint Robotic Arm

Bio-Inspired Autonomous Learning Algorithm With Application to Mobile Robot Obstacle Avoidance

A Spiking Neural Network for Autonomous Search and Contour Tracking Inspired by C. elegans Chemotaxis and the Lévy Walk

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