Towards semi-episodic learning for robot damage recovery
This work addresses robot damage recovery for improved autonomy, but it appears incremental as it builds directly on an existing algorithm.
The paper tackles the problem of enabling robots to adapt to damage while performing tasks by extending the Intelligent Trial and Error algorithm to a semi-episodic learning scheme, resulting in increased lifetime autonomy and adaptivity with promising preliminary results in simulations and a 6-legged robot locomotion task.
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.