@inproceedings{lian-etal-2026-intentvla,
title = "{I}ntent{VLA}: Short-Horizon Intent Modeling for Aliased Robot Manipulation",
author = "Lian, Shijie and
Yu, Bin and
Lin, Xiaopeng and
Shen, Zhaolong and
Yang, Laurence Tianruo and
Jin, Yurun and
Liu, Haishan and
Wu, Changti and
Yuan, Hang and
Huang, Cong and
Chen, Kai",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.230/",
pages = "5292--5310",
ISBN = "979-8-89176-459-0",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T IntentVLA: Short-Horizon Intent Modeling for Aliased Robot Manipulation
%A Lian, Shijie
%A Yu, Bin
%A Lin, Xiaopeng
%A Shen, Zhaolong
%A Yang, Laurence Tianruo
%A Jin, Yurun
%A Liu, Haishan
%A Wu, Changti
%A Yuan, Hang
%A Huang, Cong
%A Chen, Kai
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F lian-etal-2026-intentvla
%X 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.
%U https://aclanthology.org/2026.emnlp-main.230/
%P 5292-5310
Markdown (Informal)
[IntentVLA: Short-Horizon Intent Modeling for Aliased Robot Manipulation](https://aclanthology.org/2026.emnlp-main.230/) (Lian et al., EMNLP 2026)
ACL
- Shijie Lian, Bin Yu, Xiaopeng Lin, Zhaolong Shen, Laurence Tianruo Yang, Yurun Jin, Haishan Liu, Changti Wu, Hang Yuan, Cong Huang, and Kai Chen. 2026. IntentVLA: Short-Horizon Intent Modeling for Aliased Robot Manipulation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 5292–5310, Budapest, Hungary. Association for Computational Linguistics.