Work

Towards semi-episodic learning for robot damage recovery

Year: 2016

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1610.01407

Authors: Chatzilygeroudis, Konstantinos, Cully, Antoine, Mouret, Jean-Baptiste

Institutions: Laboratoire Lorrain de Recherche en Informatique et ses Applications, LARSEN: Autonomie et interaction au long cours pour des robots en environnements potentiellement sensorisés, Imperial College London

Language: English

Topic: Evolutionary Algorithms and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2528360617

Cites: 13

Cited by: 3

Related to: 10