This study is motivated by evolutionary robot systems where robot bodies and brains evolve simultaneously. In such systems robot 'birth' must be followed by 'infant learning' by a learning method that works for various morphologies evolution may produce. Here we address the task of directed locomotion in modular robots with controllers based on Central Pattern Generators. We present a bio-inspired adaptive feedback mechanism that uses a forward model and an inverse model that can be learned on-the-fly. We compare two versions (a simple and a sophisticated one) of this concept to a traditional (open-loop) controller using Bayesian optimization as a learning algorithm. The experimental results show that the sophisticated version outperforms the simple one and the traditional controller. It leads to a better performance and more robust controllers that better cope with noise.
|Title of host publication
|2020 IEEE Symposium Series on Computational Intelligence (SSCI)
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - 5 Jan 2021
|2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 - Virtual, Canberra, Australia
Duration: 1 Dec 2020 → 4 Dec 2020
|2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020
|1/12/20 → 4/12/20
- Adaptive Control
- Directed Locomotion
- Evolutionary Robotics
- Reality Gap