"Towards semi-episodic learning for robot damage recovery"
Konstantinos Chatzilygeroudis, Antoine Cully and Jean-Baptiste Mouret
Paper submitted to "Artificial Intelligence for Long-Term Autonomy" (AILTA) workshop in ICRA 2016.
Abstract:
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 life-time autonomy and adaptivity. Preliminary experiments on a toy simulation and a 6-legged robot locomotion task show promising results.
This video shows a 6-legged robot performing locomotion tasks despite the left middle leg being removed using our technique.
This work was supported by the ERC project “
ResiBots” (grant agreement No 637972), funded by the European Research Council.
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