| title | IntentVLA: Short-Horizon Intent Modeling for Aliased Robot Manipulation |
|---|---|
| authors | Shijie Lian, Bin Yu, Xiaopeng Lin, Zhaolong Shen, Laurence Tianruo Yang, Yurun Jin, Haishan Liu, Changti Wu, Hang Yuan, Cong Huang, Kai Chen |
| date | 2026-05-14 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.14712 |
| affiliation | DeepCybo |
| upvotes | 14 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Robot imitation data are often multimodal: similar visual-language observations may be followed by different action chunks because human demonstrators act with different short-horizon intents, task phases, or recent context. Existing frame-conditioned VLA policies infer each chunk from the current observation and instruction alone, so under partial observability they may resample different intents across adjacent replanning steps, leading to inter-chunk conflict and unstable execution. We introduce IntentVLA, a history-conditioned VLA framework that encodes recent visual observations into a compact short-horizon intent representation and uses it to condition chunk generation. We further introduce AliasBench, a 12-task ambiguity-aware benchmark on RoboTwin2 with matched training data and evaluation environments that isolate short-horizon observation aliasing. Across AliasBench, SimplerEnv, LIBERO, and RoboCasa, IntentVLA improves rollout stability and outperforms strong VLA baselines |
cite
@misc{lian2026intentvla,
title={IntentVLA: Short-Horizon Intent Modeling for Aliased Robot Manipulation},
author={Shijie Lian and Bin Yu and Xiaopeng Lin and Zhaolong Shen and Laurence Tianruo Yang and Yurun Jin and Haishan Liu and Changti Wu and Hang Yuan and Cong Huang and Kai Chen},
year={2026},
eprint={2605.14712},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.14712}
}