
The author argues that extensive data augmentation in simulation can outperform traditional real-world demonstrations for robotic manipulation tasks, showcasing the potential of simulated human demonstrations.


coremsg

CyberDemo-Leveraging-Simulation-Data-for-Dexterous-Manipulation


CyberDemo: Leveraging Simulation Data for Dexterous Manipulation


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Simulation data augmentation enhances real-world dexterous manipulation performance.


cyberdemo-leveraging-simulation-data-for-dexterous-manipulation


This paper introduces a novel approach to improve deep reinforcement learning (DRL) for robot locomotion by incorporating latent action priors learned from a single gait cycle demonstration, leading to faster learning, more realistic gaits, and improved transferability to new tasks.


learning-efficient-and-realistic-robot-gaits-with-latent-action-priors-extracted-from-single-gait-cycle-demonstrations


Learning Efficient and Realistic Robot Gaits with Latent Action Priors Extracted from Single Gait Cycle Demonstrations
