Abstract
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.
Original language | English |
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Title of host publication | Proceedings of the IEEE Symposium Series on Computational Intelligence, SSCI 2020 |
Place of Publication | Piscataway, NJ, USA |
Publisher | IEEE |
Pages | 2117-2124 |
ISBN (Electronic) | 978-1-7281-2547-3 |
DOIs | |
Publication status | Published - 2020 |
Event | 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 - Virtual, Canberra, Australia Duration: 1 Dec 2020 → 4 Dec 2020 |
Conference
Conference | 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 |
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Country/Territory | Australia |
City | Virtual, Canberra |
Period | 1/12/20 → 4/12/20 |
Keywords
- Adaptive Control
- Directed Locomotion
- Evolutionary Robotics
- Reality Gap