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Real-World Reinforcement Learning with MPC Scaffolding for Dexterous Manipulation
Real-world reinforcement learning (RL) offers a promising route to dexterous manipulation policies that can adapt directly from physical interaction, but learning is hindered by inefficient early exploration and costly failures. We propose a framework that uses sampling-based model predictive contro
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