Abstract:Video world models are increasingly used in robotic manipulation, yet existing benchmarks mostly evaluate them under valid, feasible, and safe instructions. We introduce RoboTrustBench, a benchmark for evaluating the trustworthiness of video world models under four scenarios: Normal, Constraint-Sensitive, Counterfactual, and Adversarial. Built from real-world DROID episodes, RoboTrustBench contains 1,207 expert-validated instruction-image pairs and a six-dimensional evaluation protocol with 13 fine-grained criteria. Evaluating seven representative video world models with human and MLLM assessment, we find that current models often generate visually coherent videos, but struggle with constraint reasoning, counterfactual grounding, physical interaction, and unsafe-instruction suppression. These results show that visual quality and surface-level instruction following are insufficient for trustworthy robotic video world modeling.
| Comments: | Project: this https URL |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL); Robotics (cs.RO) |
| Cite as: | arXiv:2606.01600 [cs.CV] |
| (or arXiv:2606.01600v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2606.01600 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Bin Zhu [view email]
[v1]
Mon, 1 Jun 2026 02:56:09 UTC (9,378 KB)