SGFT uses a simulation-trained value function to guide real-world exploration via potential-based reward shaping and short-horizon objectives, substantially improving fine-tuning sample efficiency.
What went wrong? closing the sim-to-real gap via differentiable causal discovery
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Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning
SGFT uses a simulation-trained value function to guide real-world exploration via potential-based reward shaping and short-horizon objectives, substantially improving fine-tuning sample efficiency.