The Future of Robot Control? Bridging Environments and Language with Rendering Functions and Vision-Language Models

Coupling text and vision models may be a game changer in robot control. Image created with DALL-E.

In a world where humans and machines increasingly share tasks, there is a growing desire to command our smart technologies in a way that feels natural and intuitive. Imagine telling your household robot, “Please grab the box and place it gently on the shelf,” and it understands with the precision of a trained human. A recent study titled “Bridging Environments and Language with Rendering Functions and Vision-Language Models” by Théo Cachet (NAVER LABS Europe and Sorbonne University), Christopher R. Dance (NAVER LABS Europe), and Olivier Sigaud (Sorbonne University) takes us a step closer to that dream. Their work sets the stage for an era of language-conditioned agents — AI-driven systems that execute commands described in everyday text.

The loose coupleing between Language and Vision

Whenever we chat with large language models like ChatGPT or generate pictures with advanced text-to-image systems, we enjoy the effortless feeling of “talking” to AI. And yet, commanding a sophisticated virtual agent or a real-world robot with natural language remains a tricky problem. Why? Because to ground language (or anchor words to specific entities and actions in an environment) requires the AI to truly see…