The recently introduced Intelligent Trial and Error algorithm (IT\&E) enables robots to creatively adapt to damage in a matter of minutes by combining an off-line evolutionary algorithm and an on-line learning algorithm based on Bayesian Optimization. We extend the IT\&E algorithm to allow for robots to learn to compensate for damages while executing their task(s). This leads to a semi-episodic learning scheme that increases the robot's lifetime autonomy and adaptivity. Preliminary experiments on a toy simulation and a 6-legged robot locomotion task show promising results.
@article{arxiv.1610.01407,
title = {Towards semi-episodic learning for robot damage recovery},
author = {Konstantinos Chatzilygeroudis and Antoine Cully and Jean-Baptiste Mouret},
journal= {arXiv preprint arXiv:1610.01407},
year = {2016}
}
Comments
Workshop on AI for Long-Term Autonomy at the IEEE International Conference on Robotics and Automation (ICRA), May 2016, Stockholm, Sweden. 2016