@inproceedings{44f665be8a1f4aa283a2f9a9bf5fe933,
title = "Exploring Proprioceptive Feedback in the Evolution of Modular Robots",
abstract = "We investigate an evolvable robot system where the body provides proprioceptive sensory signals to the controller (brain) about the positions of the joints. The key aspect we consider is whether all joints should be sensed or if sensing fewer joints would be better. We research this matter based on a test suite of twenty-two robots with various shapes and sizes and implement a system where the controller and the sensory signal system evolve together. Experiments with this system show that the evolved solutions use signals only from a fraction of the joints (25–51\%) and perform better than the baseline, where all signals are used. This effect was observed across the majority of the test suite.",
keywords = "Efficiency, Evolutionary Robotics, Proprioception",
author = "\{Hosseinkhani Kargar\}, Babak and Karine Miras and Eiben, \{A. E.\}",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 18th International Conference on Parallel Problem Solving from Nature, PPSN 2024 ; Conference date: 14-09-2024 Through 18-09-2024",
year = "2024",
doi = "10.1007/978-3-031-70071-2\_25",
language = "English",
isbn = "9783031700705",
volume = "3",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "405--418",
editor = "Michael Affenzeller and Winkler, \{Stephan M.\} and Kononova, \{Anna V.\} and Thomas B{\"a}ck and Heike Trautmann and Tea Tu{\v s}ar and Penousal Machado",
booktitle = "Parallel Problem Solving from Nature – PPSN XVIII",
address = "Germany",
}