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The recently introduced Intelligent Trial and Error algorithm (IT\&E) enablesrobots to creatively adapt to damage in a matter of minutes by combining anoff-line evolutionary algorithm and an on-line learning algorithm based onBayesian Optimization. We extend the IT\&E algorithm to allow for robots tolearn to compensate for damages while executing their task(s). This leads to asemi-episodic learning scheme that increases the robot's lifetime autonomy andadaptivity. Preliminary experiments on a toy simulation and a 6-legged robotlocomotion task show promising results.

Towards semi-episodic learning for robot damage recovery