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Towards robot vision using deep neural networks in evolutionary robotics

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Abstract

In evolutionary robotics, robot controllers are often evolved in simulation, as using the physical robot for fitness evaluation can take a prohibitively long time. Simulators provide a quick way to evaluate controller fitness. A simulator is tasked with providing appropriate sensor information to the controller. If the robot has an on-board camera, an entire virtual visual environment is needed to simulate the camera’s signal. In the past, these visual environments have been constructed by hand, requiring the use of hand-crafted models, textures and lighting, which is a tedious and time-consuming process. This paper proposes a deep neural network-based architecture for simulating visual environments. The neural networks are trained exclusively from images captured from the robot, creating a 3-dimensional visual environment without using hand-crafted models, textures or lighting. It does not rely on any external domain specific datasets, as all training data is captured in the physical environment. Robot controllers were evolved in simulation to discern between objects with different colours and shapes, and they successfully completed the same task in the real world.

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Data availability

Training data and trained neural network models are available at: https://github.com/nate1021/Towards-Robot-Vision

Code availability

Full Code repository is available at: https://github.com/nate1021/Towards-Robot-Vision

Notes

  1. Training computer—AMD Ryzen Threadripper; GeForce GTX 1080 Ti; 16GB RAM.

  2. TensorFlow (tensorflow.org), PyTorch (pytorch.org).

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Acknowledgements

This work is based on research supported wholly by the National Research Foundation (NRF) of South Africa (Grant No. 117625). Opinions, findings and conclusions or recommendations expressed in this paper are those of the authors, not the NRF.

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Authors and Affiliations

  1. Nelson Mandela University, University Way, Summerstrand, Port Elizabeth, 6019, South Africa

    Nathan Watt & Mathys C. du Plessis

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  1. Nathan Watt
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  2. Mathys C. du Plessis
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Contributions

NW—Wrote paper, wrote program, and ran experiments. MCdP—Supervised project, and edited paper.

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Correspondence to Nathan Watt.

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Watt, N., du Plessis, M.C. Towards robot vision using deep neural networks in evolutionary robotics. Evol. Intel. 15, 81–97 (2022). https://doi.org/10.1007/s12065-020-00490-w

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