Neuroevolution of Feedback Control for Object Manipulation by 3D Agents


It has been shown that manipulation of objects by 3D virtual creatures can play an important role in the evolution of complex, embodied sensorimotor behaviours. In this work we examine the capacity of virtual creatures that use evolutionary and control architectures already shown to be capable of sensor-differential gradient-following locomotion (tropotaxis) to adapt to solve a physical problem involving the manipulation of 3D objects in their environments. Specifically, the creatures task is to guide a physically-modelled cube through their environments in order to achieve maximum covered distance of the object. Agents were evolved in the manipulation environment from random initial genotypes and from genotypes previously optimised for performance in a different task. Performance was evaluated both before and after evolutionary adaptation. We show that the architecture achieves embodied feedback control in the block movement task. We observed some overlap between the earlier and later environments but also that success in the first environment does not preclude or entail success in the second. We found that species evolving from scratch do no better or worse than those optimised for a different environment, and that sensory feedback is necessary for correct approach and control behaviours in agents, although close control is less dependent on sensory input than distance approach.

Publication DOI:
Additional Information: © 2016 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Event Title: ALIFE 2016, The Fifteenth International Conference on the Synthesis and Simulation of Living Systems
Event Type: Other
Event Dates: 2016-07-04 - 2016-07-06
Last Modified: 14 Feb 2024 08:02
Date Deposited: 12 Sep 2022 16:46
Full Text Link:
Related URLs: https://machine ... /alife2016a.pdf (Author URL)
https://direct. ... 16/28/144/99447 (Publisher URL)
PURE Output Type: Conference contribution
Published Date: 2016-07-01
Authors: Stanton, Adam
Channon, Alastair



Version: Published Version

License: Creative Commons Attribution

| Preview

Export / Share Citation


Additional statistics for this record