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The scalability of robotic learning is fundamentally bottlenecked by the significant cost and labor of real-world data collection.
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Konstantinos Bousmalis et al · 2017
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20 years of reality gap: A few thoughts about simulators in evolutionary robotics
Jean-Baptiste Mouret and Konstantinos Chatzilygeroudis · 2017
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Domain randomization for transferring deep neural networks from simulation to the real world
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Using simulation and domain adaptation to improve efficiency of deep robotic grasping
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Closing the sim-to-real loop: Adapting simulation randomization with real world experience
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Policy transfer via kinematic domain randomization and adaptation
Ioannis Exarchos, Yifeng Jiang, Wenhao Yu, and C Karen Liu · 2021
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Generalization in reinforcement learning by soft data augmentation
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SplatSim: Zero-shot sim2real transfer of rgb manipulation policies using gaussian splatting
Mohammad Nomaan Qureshi et al · 2024
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RL-GSBridge: 3D gaussian splatting based real2sim2real method for robotic manipulation learning
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