Abstract
Adapting the control systems of robots on the fly is important in robotic systems of the future. In this paper we present and investigate a three-fold adaptive system based on evolution, individual and social learning in a group of robots and report on a proof-of-concept study based on epucks. We distinguish inheritable and learnable components in the robots' makeup, specify and implement operators for evolution, learning and social learning, and test the system in an arena where the task is to learn to avoid obstacles. In particular, we make the sensory layout evolvable, the locomotion control system learnable and investigate the effects of including social learning in the 'adaptation engine'. Our simulation experiments demonstrate that the full mix of three adaptive mechanisms is practicable and that adding social learning leads to better controllers faster.
Original language | English |
---|---|
Title of host publication | GECCO 2015 - Proceedings of the 2015 Genetic and Evolutionary Computation Conference |
Place of Publication | Madrid, SP |
Publisher | Association for Computing Machinery, Inc |
Pages | 177-183 |
Number of pages | 7 |
ISBN (Electronic) | 9781450334723 |
DOIs | |
Publication status | Published - 2015 |
Event | 16th Genetic and Evolutionary Computation Conference (GECCO 2015) - Madrid, Spain Duration: 11 Jul 2015 → 15 Jul 2015 |
Conference
Conference | 16th Genetic and Evolutionary Computation Conference (GECCO 2015) |
---|---|
Country/Territory | Spain |
City | Madrid |
Period | 11/07/15 → 15/07/15 |
Keywords
- Evolutionary robotics
- Individual learning
- Neural networks
- Obstacle avoidance
- On-line evolution
- Social learning