pretty_name: AVM-Eval
size_categories:
- 10K<n<100K
tags:
- robotics
- world-models
- evaluation
- arxiv:2610.32222
AVM-Eval
AVM-Eval (Action and Video Mismatch Benchmark) evaluates how reliably inverse dynamics models and reward models assess action-following fidelity on real and generated robot videos. It is introduced in ACWM-RM: A Reference-Free Reward Model from Complementary Mismatch Preferences.
Dataset Overview
AVM-Eval contains approximately 67.2k evaluation pairs:
| Pair type | Size | Description |
|---|---|---|
| Action mismatch | 53.9k | The same recorded video paired with original and perturbed action sequences, covering numerical and semantic perturbations. |
| Video mismatch | 13.3k | Two generated videos conditioned on the same initial frame and action sequence, covering intra-model and cross-model comparisons. Preferences are determined using nDTW-based action-following scores. |
Source data comes from AgiBotWorld, RoboTwin, RoboDojo, and Genie-Sim. Generated videos are produced by DreamDojo-2B, DreamDojo-14B, GE-Sim-2B, and OSCAR-2B. The benchmark uses pairwise accuracy to evaluate action-following assessment, including generalization to unseen tasks, embodiments, and scenes.
Files
action_mismatch/ # Action-mismatch evaluation pairs in Parquet format
video_mismatch/ # Video-mismatch evaluation pairs in Parquet format
media.tar # Media archive
See the paper for construction details and the evaluation protocol.
Citation
If you use AVM-Eval in your research, please cite:
@misc{hong2026acwmrm,
title = {{ACWM-RM}: A Reference-Free Reward Model from Complementary Mismatch Preferences},
author = {Shibo Hong and Siyu Ren and FengJiao Chen and Zhongyuan Peng and Wei Shi and Yixin Cao},
year = {2026},
eprint = {2610.32222},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2610.32222}
}