Datasets:

brickasdasda
/

AstroBench

---
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}
}
```

	---
	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}
	}
	```