PROBE: Manipulation-Grounded Visual Question Answering with VLM Agents
Moving beyond static images: how new VLM agents learn to manipulate objects to answer complex spatial questions.
Vision-language Models (VLMs) excel at 2D grounding, spatial reasoning and agentic tool-based planning in static scenes. However, consider asking a home robot "Is my medication still in the cabinet?" The answer may be physically hidden behind a row of containers that must first b...
ArXiv CS.CV
Source
Generated insight by OnAirToday AI
PROBE bridges the gap between static image understanding and physical interaction by requiring VLM agents to manipulate their environment to uncover hidden information. This advancement is critical for developing autonomous household robots that can effectively navigate and solve problems in cluttered, real-world spaces.
Comments
Keep comments relevant and respectful. No links, advertising, self-promotion, or spam.