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Towards semi-episodic learning for robot damage recovery

Konstantinos Chatzilygeroudis 1 Antoine Cully 2 Jean-Baptiste Mouret 1 
1 LARSEN - Lifelong Autonomy and interaction skills for Robots in a Sensing ENvironment
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
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 lifetime autonomy and adaptivity. Preliminary experiments on a toy simulation and a 6-legged robot locomotion task show promising results.
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Submitted on : Tuesday, October 4, 2016 - 4:17:45 PM
Last modification on : Friday, January 21, 2022 - 3:09:40 AM
Long-term archiving on: : Friday, February 3, 2017 - 4:08:28 PM


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  • HAL Id : hal-01376288, version 1
  • ARXIV : 1610.01407



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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