Quadruped Agile Locomotion: Sparsely Gated Mixture of Experts with Visual Input
Overview
This research investigates applying sparsely gated mixture-of-experts (MoE) architectures to vision-based quadruped locomotion. By comparing MoE policies against standard MLPs under matched active parameter counts, the study demonstrates an 8% increase in training reward on a Unitree Go2 robot. Results suggest that borrowing scalability techniques from large language models can enhance network capacity for dynamic tasks like climbing and jumping.
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- arXiv Robotics — research abstractsSignalQuadruped Agile Locomotion: Sparsely Gated Mixture of Experts with Visual Input
This research investigates applying sparsely gated mixture-of-experts (MoE) architectures to vision-based quadruped locomotion. By comparing MoE policies against standard MLPs under matched active parameter counts, the study demonstrates an 8% increase in training reward on a Unitree Go2 robot. Results suggest that borrowing scalability techniques from large language models can enhance network capacity for dynamic tasks like climbing and jumping.
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