<ul><li>LaMo is a new framework that combines language models with offline reinforcement learning.</li><li>It uses pre-trained language models to improve motion control with limited data.</li><li>LaMo features four key components: sequential pre-training, LoRA fine-tuning, MLP transformation, and language prediction loss.</li><li>It performs well in sparse-reward tasks and matches the performance of value-based methods, particularly with small datasets.</li></ul>

Language Models Boost Robot Learning with Limited Training Data

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