arXiv AI
Learning Energy-Efficient Air--Ground Actuation for Hybrid Robots on Stair-Like Terrain
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LoComposition: Terrain-Adaptive Energy-Efficient Quadruped Locomotion without Gait Priors
arXiv:2606. 15896v1 Announce Type: cross Abstract: Learning-based quadrupedal locomotion typically relies on complex reward formulations that entangle task specification, operational limits, gait preference, and terrain adaptation within a single optimization objective.
arXiv AI
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Learning to Walk With Less: A Dyna-Style Approach to Quadrupedal Locomotion
arXiv:2509. 06296v2 Announce Type: replace-cross Abstract: Traditional on-policy reinforcement learning (RL) controllers for quadrupedal locomotion often suffer from low data efficiency, requiring millions of interactions with simulated environments to achieve stable control.
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Training and Simulation of Quadrupedal Robot in Adaptive Stair Climbing and Descending for Indoor Firefighting: An End-to-End Reinforcement Learning Approach
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PGTT: Phase-Guided Terrain Traversal for Perceptive Legged Locomotion
arXiv:2510. 18348v2 Announce Type: replace-cross Abstract: State-of-the-art perceptive Reinforcement Learning controllers for legged robots typically either (i) impose oscillator-or IK-based gait priors that constrain the action space, bias policy optimization, and limit adaptability across robot morphologies, or (ii) operate "blind," making them unable to anticipate hind-leg terrain and brittle to observation noise.
arXiv Machine Learning
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Learning All-Terrain Locomotion for a Planetary Rover with Actively Articulated Suspension
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arXiv AI
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Agile perceptive multi-skill locomotion for quadrupedal robots in the wild
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