LLMs

Lafite-RL is a framework that leverages Large Language Models to provide natural language feedback for guiding reinforcement learning in robotic tasks. Tested on RLBench, it improves learning efficiency and success rates without requiring costly human supervision.

Explainable Q-Map improves the transparency of RL agents by combining reward decomposition with abstract action spaces, enabling clear, high-level explanations based on task-relevant object properties. We demonstrate visual and textual explanations in robotic scenarios and show how they can be used with LLMs for reasoning and interactive querying.