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Learning Directed Locomotion in Modular Robots with Evolvable Morphologies
We generalize the well-studied problem of gait learning in modular robot...
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Learning to Walk in the Real World with Minimal Human Effort
Reliable and stable locomotion has been one of the most fundamental chal...
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Learning Agile Robotic Locomotion Skills by Imitating Animals
Reproducing the diverse and agile locomotion skills of animals has been ...
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Learning Generalizable Locomotion Skills with Hierarchical Reinforcement Learning
Learning to locomote to arbitrary goals on hardware remains a challengin...
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PPMC Training Algorithm: A Robot Independent Rough Terrain Deep Learning Based Path Planner and Motion Controller
Robots can now learn how to make decisions and control themselves, gener...
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PPMC RL Training Algorithm: Rough Terrain Intelligent Robots through Reinforcement Learning
Robots can now learn how to make decisions and control themselves, gener...
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Zero-Shot Terrain Generalization for Visual Locomotion Policies
Legged robots have unparalleled mobility on unstructured terrains. Howev...
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Learning Locomotion Skills in Evolvable Robots
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.
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