Grounded Language

Grounded language research aims to create AI systems that understand and generate language in relation to real-world situations and sensory data, bridging the gap between symbolic representations and physical grounding. Current research focuses on developing algorithms and models, such as those based on interaction-grounded learning (IGL) and reinforcement learning (RL), that enable agents to learn from diverse feedback modalities (e.g., visual, textual) and generalize to novel situations. This work is significant for advancing natural language understanding and generation, particularly in interactive and collaborative settings, with applications in areas like robotics, human-computer interaction, and personalized recommendation systems.