---
pretty_name: AstroBench
license: apache-2.0
language:
- en
tags:
- robotics
- embodied-ai
- space-robotics
- benchmark
- 3d
- arxiv:2610.31064
configs:
- config_name: objects_objaverse8414
  default: true
  data_files:
  - split: catalog
    path: data/objects_objaverse8414.jsonl
- config_name: objects_core20
  data_files:
  - split: catalog
    path: data/objects_core20.jsonl
- config_name: tasks
  data_files:
  - split: catalog
    path: data/tasks.jsonl
- config_name: prompts_core20
  data_files:
  - split: catalog
    path: data/prompts_core20.jsonl
- config_name: prompts_objaverse8414
  data_files:
  - split: catalog
    path: data/prompts_objaverse8414.jsonl
- config_name: scenes
  data_files:
  - split: catalog
    path: data/scenes.jsonl
- config_name: workcells
  data_files:
  - split: catalog
    path: data/workcells.jsonl
---

# 🚀 AstroBench

### Free-Floating Capture and Space Robotic Manipulation

[📄 Paper](https://arxiv.org/abs/2610.31064) · [💻 GitHub](https://github.com/AstroUpLab/AstroBench) · [🌐 Project](https://astroup.com/astrobench/) · [📖 Task catalog](https://github.com/AstroUpLab/AstroBench/blob/main/docs/tasks.md)

AstroBench studies how robots capture drifting and rotating objects and manipulate space hardware. In free-floating capture, a contact can move the target away or change its rotation, making approach timing and contact geometry central to the task.

This Hugging Face release brings together an **8,414-object expansion inventory**, **five cabin scene configurations**, and **two editable space workcells** with **nine manipulation tasks and 29 task–target combinations**. Object and task catalogs can be explored in the dataset viewer or loaded with 🤗 Datasets. The MELFI and RRM geometry, tools, mounting hardware, collision meshes and editable sources are available in the asset package.

| MELFI sample storage | RRM servicing panel |
| :---: | :---: |
| ![MELFI: two narrow boxes and one wide box mounted along the wall](media/melfi.png) | ![RRM: wall-mounted servicing panel and floor-mounted arm](media/rrm.png) |

## 📦 Load the catalogs

```bash
pip install datasets huggingface_hub
```

```python
from datasets import load_dataset

repo_id = "brickasdasda/AstroBench"

objects = load_dataset(repo_id, "objects_objaverse8414", split="catalog")
tasks = load_dataset(repo_id, "tasks", split="catalog")
prompts = load_dataset(repo_id, "prompts_objaverse8414", split="catalog")

print(objects[0]["name"], objects[0]["source_url"])
print(prompts[0]["text"])
```

The default configuration is `objects_objaverse8414`. Each configuration uses a `catalog` split containing published definitions and records.

| Configuration | Rows | Contents |
| --- | ---: | --- |
| `objects_objaverse8414` | 8,414 | Selected Objaverse object names, category labels, source IDs, attribution and license metadata |
| `objects_core20` | 20 | Core object classes and canonical prompt names |
| `tasks` | 9 | Workcell task definitions, target lists, initial states, measurements and scoring parameters |
| `prompts_core20` | 109 | Four capture tracks × 20 core objects, plus 29 workcell instructions |
| `prompts_objaverse8414` | 33,685 | Four capture tracks × 8,414 expansion objects, plus 29 workcell instructions |
| `scenes` | 5 | Cabin identifiers, placement configurations and source references |
| `workcells` | 2 | MELFI/RRM parts, frames, layout, asset identities and archive members |

The expansion inventory preserves each object's source link and original attribution. Its 8,414 rows identify individual objects; category labels are recorded separately. Use the source IDs and links to retrieve the corresponding Objaverse models. Scene records similarly link their cabin assets to the original source.

## 🛠️ Space hardware and tasks

**MELFI V6 — sample storage.** Two narrow boxes and one wide box form a tray mounted along the cabin wall. The model includes separate lids, latch parts, retention bands and sample cards. Each of the three boxes supports five tasks:

| Task ID | Instruction template |
| --- | --- |
| `card_transfer_to_mouth` | Move the card to the opening of the {target_name} and hold it aligned. |
| `card_insert` | Insert the card fully into the {target_name}, release it, and leave it retained inside. |
| `card_extract` | Remove the card from the {target_name} and hold it clear of the opening. |
| `lid_open` | Open the lid of the {target_name} and leave it open. |
| `lid_close` | Close and latch the lid of the {target_name}, then withdraw the gripper. |

**RRM TB1 — servicing caps and captive screws.** The panel is mounted vertically on a cabin wall, with the arm's floor mounting frame directly in front of it. The geometry includes the FDV1 cap, a 3 × 3 SMA cap array, two captive fasteners, cap adapters and tool docks.

| Task ID | Instruction template |
| --- | --- |
| `fdv1_remove_stow` | Unscrew and remove the {target_name}, retain it in its adapter, dock the adapter in its stowage holder, and withdraw the driver. |
| `sma_remove_stow` | Unscrew and remove the {target_name}, retain it in its adapter, dock the adapter in its stowage holder, and withdraw the driver. |
| `captive_loosen` | Loosen the {target_name} without releasing it from the panel and hold it in the loosened position. |
| `captive_tighten` | Tighten the {target_name} until it is seated, keep it captive, and withdraw the driver. |

The prompt tables resolve `{target_name}` into each exact box, cap or screw name. Capture instructions use the same wording across the `static`, `drift`, `tumble` and `fast_tumble` tracks. The tables preserve the published registry version and text hashes.

### Download and open the workcells

[Download the complete workcell package](https://huggingface.co/datasets/brickasdasda/AstroBench/resolve/main/assets/astrobench-workcells.zip)

```python
from pathlib import Path
from zipfile import ZipFile
from huggingface_hub import hf_hub_download

archive = hf_hub_download(
    repo_id="brickasdasda/AstroBench",
    repo_type="dataset",
    filename="assets/astrobench-workcells.zip",
)
with ZipFile(archive) as z:
    z.extractall(Path("astrobench-assets"))
```

The archive contains the full `workcells/` directory, including both workcells and their shared source files. Open `workcells/melfi_v6/melfi_v6.blend` or `workcells/rrm_tb1/rrm_tb1.blend` in Blender, or import the corresponding `.glb` in a compatible renderer. Parts, collision data, dimensions and transforms are described in the adjacent JSON files. The package uses metres and radians.

The accompanying [workcell guide](https://github.com/AstroUpLab/AstroBench/blob/main/docs/workcells.md) describes task resets, target selection, world placement and the arm mounting frames. Use the [GitHub package](https://github.com/AstroUpLab/AstroBench) for episode planning, task scoring and result aggregation.

### 🎬 Mechanism demonstrations

[FDV1 removal and stowage](https://huggingface.co/datasets/brickasdasda/AstroBench/resolve/main/media/fdv1.mp4) · [SMA cap removal and stowage](https://huggingface.co/datasets/brickasdasda/AstroBench/resolve/main/media/sma.mp4) · [Captive screw](https://huggingface.co/datasets/brickasdasda/AstroBench/resolve/main/media/captive.mp4)

These archived demonstrations show the mechanisms in their original tabletop arrangement. The overview images and packaged layout describe the current wall-mounted arrangement. [Demonstration metadata](media/demonstrations.json) records the individual videos and layout review.

## 📐 Data fields and provenance

- Object records preserve stable `object_id` values; Objaverse records also retain `source_uid`, `source_url`, author attribution and the original license label.
- Prompt records contain `task_id`, `target_id`, `track`, `text`, and hashes for the instruction and source registry.
- Task and workcell records expose identifiers and commonly used scalar fields directly. Heterogeneous specifications are stored as canonical JSON in fields ending in `_json`; read them with `json.loads()`.
- Workcell archive member paths locate the GLB, Blender, parts, collision and manifest files. The original manifests retain source and asset checksums.

This release is derived from [AstroUpLab/AstroBench at commit 66a8e1f](https://github.com/AstroUpLab/AstroBench/tree/66a8e1f146e1379e57dbe4c9887775c7039a213f). See [provenance.json](provenance.json) for the source revision and file checksums.

## 📜 License and sources

AstroBench's original task definitions, code and reconstructed workcell geometry are released under [Apache-2.0](LICENSE). Third-party object assets retain the individual licenses recorded in the object catalog. Their author attribution and source links are preserved. [NOTICE](NOTICE) and the workcell package's own `NOTICE` describe the NASA/ESA hardware references and asset provenance.

## 📚 Citation

If you use AstroBench in your research, please cite:

```bibtex
@misc{wang2026astrobench,
  title         = {{AstroBench}: Benchmarking Free-Floating Capture and Space Robotic Manipulation},
  author        = {Zizhe Wang and Jia Deng and Xin Lu and Zehao Jia and Yi Huang and Yao Zhu},
  year          = {2026},
  eprint        = {2610.31064},
  archivePrefix = {arXiv},
  primaryClass  = {cs.RO},
  doi           = {10.48550/arXiv.2610.31064},
  url           = {https://arxiv.org/abs/2610.31064}
}
```