MG-VQA: Manipulation Grounded Visual Question Answering with VLMs
Vision-language models (VLMs) have shown promising spatial reasoning capabilities from static visual inputs, where the evidence needed to answer a question is available in the provided views. However, in cluttered environments, answer-relevant evidence may be occluded rather than absent: an object may lie beneath a pile, be covered by another object, or have identifying information facing away from the camera. Answering such questions requires physical interaction to reveal the hidden evidence. We formalize this setting as Manipulation-Grounded Visual Question Answering (MG-VQA), where an agent answers a question about an initially cluttered scene by using manipulation as an intermediate ...
| Authors | Vineet Bhat, Mikaela Angelina Uy, Siyi Chen et al. |
|---|---|
| Primary category | cs.CV |
| Published | 17 August 2026 |
| Last updated at source | 12 October 2026 |
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