| title | VABench: Measuring Embodied Spatial Intelligence through Visual Demonstrations, Active Perception, and Metric Control |
|---|---|
| authors | Zhongbo Zhang, Jiayi Jin, Yifan Wang, Zaibin Zhang, Haiwen Diao, Lijun Wang, Huchuan Lu |
| date | 2026-09-17 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2609.19554 |
| affiliation | DaLian University of Technology |
| upvotes | 20 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Spatial intelligence requires more than describing object locations. Under incomplete observation, models must identify and acquire missing evidence, interpret it in a common spatial frame, and act on it. We introduce VA-Bench to evaluate the complete observe-reason-act-revise loop. General-purpose MLLMs learn procedural context from RGB-only demonstrations, actively select camera viewpoints, issue metric Cartesian commands, and revise them from execution feedback. Models receive no privileged object poses, oracle trajectories, or learned action heads. A fixed model-agnostic controller executes only model-specified targets. VA-Bench contains 14 base task families (11 single-arm and three dual-arm), seven held-out geometry/layout variants, and a long-horizon five-object composition track. We evaluate 12 primary model conditions in three independent runs over the same 20 physically verified seeds per base task, reporting terminal success, nine trajectory-level behavioral diagnostics, and subtask progress. First, the best-performing model scores 100.0% on target localization and 78.9% on spatial relations in the annotated run. Its three-run macro-average task success is only 53.93+/-3.17%. Second, active camera control significantly improves task success over passive multi-view observation. In one matched comparison, success rises from 27.86% to 57.50%. Third, held-out geometric transfer can reduce task success by over 30 percentage points. No model completes a strict long-horizon episode, despite substantial partial progress. VA-Bench thus tests whether general-purpose MLLMs can turn visual demonstrations and actively acquired evidence into successful embodied action. |
cite
@misc{zhang2026vabench,
title={VABench: Measuring Embodied Spatial Intelligence through Visual Demonstrations, Active Perception, and Metric Control},
author={Zhongbo Zhang and Jiayi Jin and Yifan Wang and Zaibin Zhang and Haiwen Diao and Lijun Wang and Huchuan Lu},
year={2026},
eprint={2609.19554},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2609.19554}
}