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            <h1 class="title is-1 publication-title">Robo2VLM: <br/>
              Improving Visual Question Answering using Large-Scale Robot Manipulation Data</h1>
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                <a href="https://keplerc.github.io/" target="_blank">Kaiyuan Chen</a><sup>*,1</sup>,</span>
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                  <a href="https://ehehee.github.io/" target="_blank">Shuangyu Xie</a><sup>*,1</sup>,</span>
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                    <a target="_blank">Zehan Ma</a><sup>1</sup>,</span>
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                    <a href="https://research.google/people/pannagrsanketi/" target="_blank">Pannag Sanketi</a><sup>2</sup>,</span>
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                    <a href="https://goldberg.berkeley.edu/" target="_blank">Ken Goldberg</a><sup>1</sup>,</span>
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                    <span class="author-block"><sup>1</sup>UC Berkeley, <sup>2</sup>Google DeepMind</span>
                    <span class="eql-cntrb"><small><br><sup>*</sup>Indicates Equal Contribution</small></span>
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                      🏆 NeurIPS 2025 Spotlight
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       <strong>Robo2VLM dataset overview.</strong>
         <!-- The middle colorbar traces a typical manipulation episode—from pre-grasp through immobilization, contact, detach, and into post-grasp. Surrounding panels give example questions for each VQA category. Dashed arrows connect every category to the phase(s) in which its questions are sampled. Icons beneath each panel list the key sensing modalities (RGB, stereo depth, wrist/side cameras, gripper state, end-effector pose, language instructions) needed to derive ground-truth answers. -->
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            Vision-Language Models (VLMs) acquire real-world knowledge and general reasoning ability through Internet-scale image-text corpora. They can augment robotic systems with scene understanding and task planning, and assist visuomotor policies that are trained on robot trajectory data. We explore the reverse paradigm — using rich, real, multi-modal robot trajectory data to enhance and evaluate VLMs. In this paper, we present Robo2VLM, a Visual Question Answering (VQA) dataset generation framework for VLMs. Given a human tele-operated robot trajectory, Robo2VLM derives ground-truth from non-visual and non-descriptive sensory modalities, such as end-effector pose, gripper aperture, and force sensing. Based on these modalities, it segments the robot trajectory into a sequence of manipulation phases. At each phase, Robo2VLM uses scene and interaction understanding to identify 3D properties of the robot, task goal, and the target object. The properties are used to generate representative VQA queries – images with textural multiple-choice questions – based on spatial, goal-conditioned, and interaction reasoning question templates. We curate Robo2VLM-1, a large-scale in-the-wild dataset with 684,710 questions covering 463 distinct scenes and 3,396 robotic manipulation tasks from 176k real robot trajectories. Results suggest that Robo2VLM-1 can benchmark and improve VLM capabilities in spatial and interaction reasoning.          </p>
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              Robo2VLM generates multi-modal real-world robot trajectories through (1) manipulation phase classification, (2) keyframe selection guided by scene and interaction cues, and (3) structured VQA question prototype.
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            Explore the Robo2VLM-1 dataset interactively using the Hugging Face dataset viewer below.
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          <h2 class="title is-3">Robo2VLM-1 Dataset Statistics</h2>
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            <p>
              Distribution and key statistics of Robo2VLM-1 dataset. (Left) Robo2VLM-1covers diverse scenes with the most frequent scenes in office (33.6%), lab (25.3%), and kitchen (16.9\%). (Middle) Robo2VLM-1 covers tasks including common manipulation actions include pick (21.5%), put (20.6%), and move (9.9%). (Right) The table summarizes key dataset statistics including question characteristics, answer choices, and image resolutions.            </p>
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          <h2 class="title is-3">Benchmarking VLM Capabilities</h2>
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              <img src="static/images/benchmark-stats.png" alt="Directional Weight Score" class="blend-img-background center-image" style="max-width: 100%; height: auto;" />
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            <p>Performance Comparison of Multimodal Foundation Models on OpenX-VQA Benchmark Categories (%). Upper part: zero-shot. Lower part: with CoT prompting.
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          <h2 class="title is-3">Finetuning Results</h2>
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              <img src="static/images/finetune_breakdown.png" alt="Directional Weight Score" class="blend-img-background center-image" style="max-width: 100%; height: auto;" />
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              Fine-tuning LLaVA 1.6 with increasing training data of robo2vlm-1 from 10k to 50k VQA items. Accuracy improvements almost all categories compared to no fine-tuning.        </div>
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      <pre><code>
  @inproceedings{chenrobo2vlm,
    title={Robo2VLM: Improving Visual Question Answering using Large-Scale Robot Manipulation Data},
    author={Chen, Kaiyuan and Xie, Shuangyu and Ma, Zehan and Sanketi, Pannag R and Goldberg, Ken},
    booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems Datasets and Benchmarks Track}
  }

  @misc{chen2025robo2vlmvisualquestionanswering,
    title={Robo2VLM: Visual Question Answering from Large-Scale In-the-Wild Robot Manipulation Datasets},
    author={Kaiyuan Chen and Shuangyu Xie and Zehan Ma and Pannag Sanketi and Ken Goldberg},
    year={2025},
    eprint={2505.15517},
    archivePrefix={arXiv},
    primaryClass={cs.RO},
    url={https://arxiv.org/abs/2505.15517},
  }</code></pre>
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</section>
<!--End BibTex citation -->


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