Towards semi-episodic learning for robot damage recovery
  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. 
Konstantinos Chatzilygeroudis (LORIA, LARSEN), Antoine Cully,   Jean-Baptiste Mouret (LORIA, LARSEN)
cs.RO cs.AI cs.NE