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Learning Locomotion Skills in Evolvable Robots

arXiv 2010.09531 ↗First posted 19 Oct 2020cs.AI cs.NE cs.ROcited by 17

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Abstract (arXiv)

The challenge of robotic reproduction -- making of new robots by recombining two existing ones -- has been recently cracked and physically evolving robot systems have come within reach. Here we address the next big hurdle: producing an adequate brain for a newborn robot. In particular, we address the task of targeted locomotion which is arguably a fundamental skill in any practical implementation. We introduce a controller architecture and a generic learning method to allow a modular robot with an arbitrary shape to learn to walk towards a target and follow this target if it moves. Our approach is validated on three robots, a spider, a gecko, and their offspring, in three real-world scenarios.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Oct 2020.

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Citing papers per year: 2021: 1; 2022: 3; 2023: 5; 2024: 6; 2025: 1; 2026: 1.

17 papers.

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