Robotic implementation of classical and operant conditioning within a single SNN architecture

Dumesnil, Etienne; Beaulieu, Philippe-Olivier; Boukadoum, Mounir

doi:10.1109/icci-cc.2016.7862055

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…A virtual robot controller simulates the thigmotaxis (the movements of one organism either towards or away from the stimulus) and boldness (the propensity to engage in risky behaviour) behaviours. The network performed visual learning tasks solved through an operant conditioning procedure [116] [136]. Similarly, another article presents learning in robots where SNN can implement several variations of learning through classical conditioning with positive or negative reinforcement.…”

Section: Speech Recognitionmentioning

confidence: 99%

Neuromorphic Computing for Interactive Robotics: A Systematic Review

2022

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Modelling functionalities of the brain in human-robot interaction contexts requires a real-time understanding of how each part of a robot (motors, sensors, emotions, etc.) works and how they interact all together to accomplish complex behavioural tasks while interacting with the environment. Human brains are very efficient as they process the information using event-based impulses also known as spikes, which make living creatures very efficient and able to outperform current mainstream robotic systems in almost every task that requires real-time interaction. In recent years, combined efforts by neuroscientists, biologists, computer scientists and engineers make it possible to design biologically realistic hardware and models that can endow the robots with the required human-like processing capability based on neuromorphic computing and Spiking Neural Network (SNN). However, while some attempts have been made, a comprehensive combination of neuromorphic computing and robotics is still missing. In this article, we present a systematic review of neuromorphic computing applications for socially interactive robotics. We first introduce the basic principles, models and architectures of neuromorphic computation. The remaining articles are classified according to the applications they focus on. Finally, we identify the potential research topics for fully integrated socially interactive neuromorphic robots.

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Section: Speech Recognitionmentioning

confidence: 99%

Neuromorphic Computing for Interactive Robotics: A Systematic Review

2022

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“…With this simple basic architecture and learning rules such as habituation and STDP, they were able solve simple OC-related tasks in a simulated environment, such as pushing blocks. In another publication by Dumesnil et al ( 2016a , b ) a reward-dependent STDP learning rule was implemented on a robot to allow for OC learning and demonstrated in a maze task. The RGB camera was used to capture the color information which represented the cue or the reward in the maze environment.…”

Section: Learning and Robotics Applicationsmentioning

confidence: 99%

A Survey of Robotics Control Based on Learning-Inspired Spiking Neural Networks

et al. 2018

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Biological intelligence processes information using impulses or spikes, which makes those living creatures able to perceive and act in the real world exceptionally well and outperform state-of-the-art robots in almost every aspect of life. To make up the deficit, emerging hardware technologies and software knowledge in the fields of neuroscience, electronics, and computer science have made it possible to design biologically realistic robots controlled by spiking neural networks (SNNs), inspired by the mechanism of brains. However, a comprehensive review on controlling robots based on SNNs is still missing. In this paper, we survey the developments of the past decade in the field of spiking neural networks for control tasks, with particular focus on the fast emerging robotics-related applications. We first highlight the primary impetuses of SNN-based robotics tasks in terms of speed, energy efficiency, and computation capabilities. We then classify those SNN-based robotic applications according to different learning rules and explicate those learning rules with their corresponding robotic applications. We also briefly present some existing platforms that offer an interaction between SNNs and robotics simulations for exploration and exploitation. Finally, we conclude our survey with a forecast of future challenges and some associated potential research topics in terms of controlling robots based on SNNs.

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Robotic implementation of classical and operant conditioning within a single SNN architecture

Cited by 2 publications

References 7 publications

Neuromorphic Computing for Interactive Robotics: A Systematic Review

Neuromorphic Computing for Interactive Robotics: A Systematic Review

A Survey of Robotics Control Based on Learning-Inspired Spiking Neural Networks

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