DrEureka

A simulation-to-real-world transfer strategy guided by large language models designed for acquiring robot skills.

CommonProductProductivityArtificial IntelligenceMachine Learning

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Automated Reward Function Construction: Generates appropriate reward functions based on the target task.
Domain Randomization Distribution: Auto-generates domain randomization parameters for real-world transfer support.
Strategy Testing Under Simulation Conditions: Tests strategies under various simulation conditions
constructing reward-aware physical priors.
Real-World Deployment: Trains strategies for deployment in the real world using synthesized rewards and domain randomization parameters.
Robustness: The DrEureka strategy performs excellently in the real world
maintaining balance even under terrain changes and disturbances.
Safety: By integrating safety instructions
the reward function design has been improved to generate rewards safe for real-world deployment.
Reward-Perception Physically-Prior: The initial strategy is crucial for DrEureka's success

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5581

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45.86%

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1.6

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DrEureka

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