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Bootstrapped Neuro-Simulation for complex robots.

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Title: Bootstrapped Neuro-Simulation for complex robots.
Authors: Woodford, Grant W.1 (AUTHOR) grant.woodford@mandela.ac.za, du Plessis, Mathys C.1 (AUTHOR) mc.duplessis@mandela.ac.za
Source: Robotics & Autonomous Systems. Feb2021, Vol. 136, pN.PAG-N.PAG. 1p.
Subjects: Artificial neural networks, Robots, Machine learning
Abstract: Robotic simulators are often used to speed up the Evolutionary Robotics (ER) process. Most simulation approaches are based on physics modelling. However, physics-based simulators can become complex to develop and require prior knowledge of the robotic system. Robotics simulators can be constructed using Machine Learning techniques, such as Artificial Neural Networks (ANNs). ANN-based simulator development usually requires a lengthy behavioural data collection period before the simulator can be trained and used to evaluate controllers during the ER process. The Bootstrapped Neuro-Simulation (BNS) approach can be used to simultaneously collect behavioural data, train an ANN-based simulator and evolve controllers for a particular robotic problem. This paper investigates proposed improvements to the BNS approach and demonstrates the viability of the approach by optimising gait controllers for a Hexapod and Snake robot platform. • Bootstrapped Neuro-Simulation (BNS) is shown to evolve closed-loop controllers. • Constructs robotic simulators using Artificial Neural Networks and Machine Learning techniques. • Investigates improvements to the BNS approach. • Demonstrates viability of the BNS approach on Hexapod and Snake robot platforms. [ABSTRACT FROM AUTHOR]
Copyright of Robotics & Autonomous Systems is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 148046211
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PubType: Academic Journal
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  Data: Robotic simulators are often used to speed up the Evolutionary Robotics (ER) process. Most simulation approaches are based on physics modelling. However, physics-based simulators can become complex to develop and require prior knowledge of the robotic system. Robotics simulators can be constructed using Machine Learning techniques, such as Artificial Neural Networks (ANNs). ANN-based simulator development usually requires a lengthy behavioural data collection period before the simulator can be trained and used to evaluate controllers during the ER process. The Bootstrapped Neuro-Simulation (BNS) approach can be used to simultaneously collect behavioural data, train an ANN-based simulator and evolve controllers for a particular robotic problem. This paper investigates proposed improvements to the BNS approach and demonstrates the viability of the approach by optimising gait controllers for a Hexapod and Snake robot platform. • Bootstrapped Neuro-Simulation (BNS) is shown to evolve closed-loop controllers. • Constructs robotic simulators using Artificial Neural Networks and Machine Learning techniques. • Investigates improvements to the BNS approach. • Demonstrates viability of the BNS approach on Hexapod and Snake robot platforms. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Robotics & Autonomous Systems is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.robot.2020.103708
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        Text: English
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      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Robots
        Type: general
      – SubjectFull: Machine learning
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      – TitleFull: Bootstrapped Neuro-Simulation for complex robots.
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              Text: Feb2021
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              Y: 2021
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