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Communication Dans Un Congrès Année : 2016

Towards semi-episodic learning for robot damage recovery

Résumé

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.
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Dates et versions

hal-01376288 , version 1 (04-10-2016)

Identifiants

Citer

Konstantinos Chatzilygeroudis, Antoine Cully, Jean-Baptiste Mouret. Towards semi-episodic learning for robot damage recovery. Workshop on AI for Long-Term Autonomy at the IEEE International Conference on Robotics and Automation (ICRA), Lars Kunze; Nick Hawes; Tom Duckett; Gabe Sibley, May 2016, Stockholm, Sweden. ⟨hal-01376288⟩
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