language-grounding

A survey paper introducing a functional role taxonomy for language grounding in embodied agents, distinguishing five roles and auditing evidence to assess whether language's contribution is genuinely supported.

CLAP proposes a method to convert pretrained vision-language models (VLMs) into vision-language-action models (VLAs) by prepending natural-language action descriptions to action token sequences, preserving semantic capabilities without architectural changes. It achieves 90.8% on LIBERO and improves robustness.

Dr. Fei-Fei Li discusses the challenges robots face in understanding and executing everyday household tasks, highlighting the difficulty of grounding natural language instructions like 'open the drawer while avoiding the vase' into robot actions.

OpenAI researchers demonstrate that cooperative AI agents can develop their own grounded and compositional language through reinforcement learning in simple worlds. The agents learn to communicate by being rewarded for achieving goals that require coordination, creating shared symbolic languages to coordinate behavior.