Robo2VLM: Improving Visual Question Answering using Large-Scale Robot Manipulation Data

Vision-Language Models (VLMs) acquire real-world knowledge and general reasoning ability through Internet-scale image-text corpora. They can augment robotic systems with scene understanding and task planning, and assist visuomotor policies that are trained on robot trajectory data. We explore the reverse paradigm -- using rich, real, multi-modal robot trajectory data to enhance and evaluate VLMs.