@inproceedings{anupam-etal-2026-roboreason,
title = "{R}obo{R}eason: Boosting Robotics-Relevant {VLM} Reasoning Through Scalable Data Generation",
author = "Anupam, Sagnik and
Lal, Utkarsh and
Edara, Aneesh and
Zhang, Botong and
Tang, Yao and
Gu, Jiatao and
Jayaraman, Dinesh and
Bastani, Osbert",
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.2384/",
pages = "54156--54182",
ISBN = "979-8-89176-459-0",
abstract = "Vision-Language Models (VLMs) have demonstrated impressive performance when integrated into robot manipulation pipelines, but real-world evaluation of VLM capabilities on downstream robotics tasks is both difficult and expensive to scale. We propose that spatial and temporal ordering tasks using images from stereo and wrist cameras attached to real-world robots are particularly well-suited for evaluating visual reasoning capabilities, as they assess skills directly applicable to robots. We evaluate VLM reasoning abilities on three questions: ordering shuffled frames conditioned on a language description of the task, ordering objects depthwise based on distance from the camera, and ordering objects from left-to-right in pixel coordinates. The complexity of these tasks can also be modulated by changing the size of the set to sort. Our experiments show that several state-of-the-art vision-language models struggle on ordering tasks. To fix this, we propose a scalable autonomous training data generation pipeline, and show that both supervised finetuning and reinforcement-learning on the generated data can significantly improve VLM reasoning performance both on spatiotemporal ordering tasks as well as downstream robotics and visual reasoning tasks."
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<abstract>Vision-Language Models (VLMs) have demonstrated impressive performance when integrated into robot manipulation pipelines, but real-world evaluation of VLM capabilities on downstream robotics tasks is both difficult and expensive to scale. We propose that spatial and temporal ordering tasks using images from stereo and wrist cameras attached to real-world robots are particularly well-suited for evaluating visual reasoning capabilities, as they assess skills directly applicable to robots. We evaluate VLM reasoning abilities on three questions: ordering shuffled frames conditioned on a language description of the task, ordering objects depthwise based on distance from the camera, and ordering objects from left-to-right in pixel coordinates. The complexity of these tasks can also be modulated by changing the size of the set to sort. Our experiments show that several state-of-the-art vision-language models struggle on ordering tasks. To fix this, we propose a scalable autonomous training data generation pipeline, and show that both supervised finetuning and reinforcement-learning on the generated data can significantly improve VLM reasoning performance both on spatiotemporal ordering tasks as well as downstream robotics and visual reasoning tasks.</abstract>
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%0 Conference Proceedings
%T RoboReason: Boosting Robotics-Relevant VLM Reasoning Through Scalable Data Generation
%A Anupam, Sagnik
%A Lal, Utkarsh
%A Edara, Aneesh
%A Zhang, Botong
%A Tang, Yao
%A Gu, Jiatao
%A Jayaraman, Dinesh
%A Bastani, Osbert
%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 anupam-etal-2026-roboreason
%X Vision-Language Models (VLMs) have demonstrated impressive performance when integrated into robot manipulation pipelines, but real-world evaluation of VLM capabilities on downstream robotics tasks is both difficult and expensive to scale. We propose that spatial and temporal ordering tasks using images from stereo and wrist cameras attached to real-world robots are particularly well-suited for evaluating visual reasoning capabilities, as they assess skills directly applicable to robots. We evaluate VLM reasoning abilities on three questions: ordering shuffled frames conditioned on a language description of the task, ordering objects depthwise based on distance from the camera, and ordering objects from left-to-right in pixel coordinates. The complexity of these tasks can also be modulated by changing the size of the set to sort. Our experiments show that several state-of-the-art vision-language models struggle on ordering tasks. To fix this, we propose a scalable autonomous training data generation pipeline, and show that both supervised finetuning and reinforcement-learning on the generated data can significantly improve VLM reasoning performance both on spatiotemporal ordering tasks as well as downstream robotics and visual reasoning tasks.
%U https://aclanthology.org/2026.emnlp-main.2384/
%P 54156-54182
Markdown (Informal)
[RoboReason: Boosting Robotics-Relevant VLM Reasoning Through Scalable Data Generation](https://aclanthology.org/2026.emnlp-main.2384/) (Anupam et al., EMNLP 2026)
ACL
- Sagnik Anupam, Utkarsh Lal, Aneesh Edara, Botong Zhang, Yao Tang, Jiatao Gu, Dinesh Jayaraman, and Osbert Bastani. 2026. RoboReason: Boosting Robotics-Relevant VLM Reasoning Through Scalable Data Generation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 54156–54182, Budapest, Hungary. Association for Computational Linguistics.