README.md

---
license: apache-2.0
language:
- en
task_categories:
- visual-question-answering
tags:
- robotics
- spatial-reasoning
- benchmark
- vlm
- embodied-ai
- multiple-choice
pretty_name: RoboVista
size_categories:
- n<1K
dataset_info:
  features:
  - name: images
    sequence: image
  - name: publication_source
    dtype: string
  - name: question
    dtype: string
  - name: choices
    sequence: string
  - name: correct_answer
    dtype: string
  - name: reasoning
    dtype: string
  - name: id
    dtype: string
  - name: domain
    dtype: string
  - name: task
    dtype: string
  - name: ability_type
    dtype: string
  - name: ability_subcategory
    dtype: string
  - name: creator_name
    dtype: string
  - name: created_at
    dtype: string
  splits:
  - name: train
    num_bytes: 1168067483.0
    num_examples: 474
  download_size: 1102522161
  dataset_size: 1168067483.0
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# RoboVista: Evaluating Vision-Language Models for Diverse Robot Applications

**RSS 2026** · [Project page](https://berkeleyautomation.github.io/robovista/) · [Leaderboard](https://berkeleyautomation.github.io/robovista/leaderboard.html) · [Code](https://github.com/KeplerC/rqa)

RoboVista is a benchmark of **474 multiple-choice questions over real robot scenes** for evaluating
vision-language models on the perception and reasoning skills robots actually need. Each question
pairs one or more images from a real robot deployment (wrist cameras, overhead views, driving
scenes, surgical setups) with 2–5 answer choices, a ground-truth answer, and expert reasoning.

Questions span **six application domains** — agriculture, autonomous driving, domestic,
industrial manufacturing, surgical robotics, and scenes from open robot-learning datasets
(DROID, Bridge, AgiBot, Fractal, Dex-Net, …) — and are labeled by **ability type**: scene
understanding (geometry & spatial reasoning, sequential events, physical interaction) vs.
planning & decision-making (goal/action reasoning, motion feasibility, failure recovery).

## Dataset structure

| Field | Type | Description |
|---|---|---|
| `images` | list[Image] | 1–7 images per question |
| `publication_source` | string | Source dataset / paper for the scene |
| `question` | string | Question text |
| `choices` | list[string] | Up to 5 answer options (A–E; unused slots empty) |
| `correct_answer` | string | Ground-truth letter (`A`–`E`) |
| `reasoning` | string | Annotator's reasoning for the answer |
| `id` | string | Question id |
| `domain` | string | Application domain (see above) |
| `task` | string | Original task description from the source deployment |
| `ability_type` | string | `scene_understanding`, `high_level_decision_making`, `low_level_motion_awareness`, or `recovery_replanning_robustness` |
| `ability_subcategory` | string | Fine-grained ability label |
| `creator_name` | string | Annotator |

Single `train` split, 474 rows, images embedded (~1.1 GB).

## Usage

```python
from datasets import load_dataset

ds = load_dataset("sy-xie/robovista", split="train")
ex = ds[0]

letters = ["A", "B", "C", "D", "E"]
choices = "\n".join(
    f"{letter}: {c}" for letter, c in zip(letters, ex["choices"]) if c
)
prompt = (
    f"{ex['question']}\n\n{choices}\n\n"
    "Answer with the letter only (A, B, C, D, or E)."
)
# send ex["images"] + prompt to your VLM, compare to ex["correct_answer"]
```

The evaluation harness (server launch scripts, standard/CoT prompts, answer parsing) is in the
[code repository](https://github.com/KeplerC/rqa).

## Leaderboard

Accuracy with the standard prompt and a chain-of-thought prompt (top models shown; full results
and per-domain breakdowns on the [leaderboard page](https://berkeleyautomation.github.io/robovista/leaderboard.html)):

| Model | Weights | Standard | CoT |
|---|---|---:|---:|
| Gemini 3 Flash (preview) | API | **68.9** | 65.4 |
| Qwen3.5-397B-A17B | open | 55.2 | 52.8 |
| GPT-5-0 | API | 54.6 | 54.6 |
| Qwen3.6-27B | open | 53.5 | 54.4 |
| Gemma 4 31B | open | 51.5 | 55.4 |
| Qwen3.6-35B-A3B | open | 50.9 | 50.0 |
| Qwen3-VL-32B-Thinking | open | 49.6 | 51.3 |
| GPT-4o | API | 48.5 | — |
| GLM-4.6V | open | 48.0 | 45.0 |
| RoboBrain2.5-8B-NV | open | 45.7 | 40.4 |
| Cosmos-Reason2-32B | open | 46.1 | 42.0 |
| Cosmos3-Nano (16B) | open | 44.4 | 39.8 |
| Gemma 4 12B | open | 41.7 | 45.4 |
| Molmo2-8B | open | 41.7 | 33.0 |
| Cosmos-Reason1-7B | open | 41.3 | 39.8 |
| Cosmos-Reason2-2B | open | 38.3 | 33.5 |

Scoring notes: 460 of 474 questions are scored (a 14-question blacklist is excluded); answers are
extracted with the harness's improved parser; Qwen3/3.5/3.6 rows run in non-thinking mode.

## Citation

```bibtex
@inproceedings{robovista2026,
  title     = {RoboVista: Evaluating Vision-Language Models for Diverse Robot Applications},
  author    = {Xie, Shuangyu and Chen, Kaiyuan and Chen, Ziyang and Adebola, Simeon and
               Huang, Yixuan and Ma, Zehan and Qiu, Tianshuang and Yuan, Wentao and
               Shah, Dhruv and Sanketi, Pannag R. and Goldberg, Ken},
  booktitle = {Robotics: Science and Systems (RSS)},
  year      = {2026}
}
```