LaMo: Language Models Boost Robot Learning With Limited Data
LaMo combines language models with offline reinforcement learning for robots. It improves motion control with limited data & performs well in sparse-reward tasks.
This is a Plain English Papers summary of a research paper called Language Models Boost Robot Learning with Limited Training Data. If you like these kinds of analysis, you should join AImodels.fyi or follow us on Twitter. Overview New framework called LaMo combines language models with offline reinforcement learning Uses pre-trained language models to improve motion control with limited data Features four key components: sequential pre-training, LoRA fine-tuning, MLP transformation, and language prediction loss Performs well in sparse-reward tasks and matches performance of value-bas...