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license: apache-2.0
task_categories:
- robotics
tags:
- robotics
- interacted-object
- benchmark
configs:
- config_name: all
data_files:
- split: test
path: "data/*/test/*"
- split: validation
path: "data/*/validation/*"
- config_name: agibot_world
data_files:
- split: test
path: "data/agibot_world/test/*"
- split: validation
path: "data/agibot_world/validation/*"
- config_name: bridge_lerobot
data_files:
- split: test
path: "data/bridge_lerobot/test/*"
- split: validation
path: "data/bridge_lerobot/validation/*"
- config_name: droid_lerobot
data_files:
- split: test
path: "data/droid_lerobot/test/*"
- split: validation
path: "data/droid_lerobot/validation/*"
- config_name: galaxea
data_files:
- split: validation
path: "data/galaxea/validation/*"
- config_name: oxe_lerobot
data_files:
- split: test
path: "data/oxe_lerobot/test/*"
- split: validation
path: "data/oxe_lerobot/validation/*"
- config_name: robocoin
data_files:
- split: validation
path: "data/robocoin/validation/*"
---
# IA-bench (Interacted-Object Benchmark)
Human ground-truth annotations of the interacted object for robot manipulation
subtasks. Each sample is one subtask: the full subtask video clip, the gripper
proprioception aligned 1:1 to those frames, the language instruction, and two boxes:
`initial_object_box` (object on the first frame) and `target_object_box`
(object on the last frame). Boxes are pixel `[x1, y1, x2, y2]`.
## Configs
```python
from datasets import load_dataset
ds = load_dataset("irl-kit/IA-bench", "bridge_lerobot", split="validation")
ds = load_dataset("irl-kit/IA-bench", "all", split="test")
```
| dataset | test | validation |
|---|---|---|
| agibot_world | 264 | 187 |
| bridge_lerobot | 321 | 134 |
| droid_lerobot | 269 | 138 |
| galaxea | - | 62 |
| oxe_lerobot | 424 | 14 |
| robocoin | - | 67 |
## Per-frame fields
- `video`: the full subtask clip (mp4, native resolution, all frames).
- `native_fps` / `effective_fps`: source fps and the clip fps (equal unless a
frame cap is applied). `frame_indices` gives original frame indices
(timestamp = index / native_fps); proprio is aligned 1:1 with the frames.
- `proprio` (JSON, decode with `json.loads`): unified, raw (un-normalized)
per-frame proprioception. Consistent field names across datasets:
`gripper_state` (open/close, inverted to a common convention),
`eef_state` (6/7-dim end-effector pose), `joint_pos` (arm joint positions).
Bimanual datasets prefix fields with `left_`/`right_`. `proprio_dims` gives
the per-field feature dimension; `proprio_keys` lists the present fields.
Availability/dim varies by embodiment (e.g. OXE exposes gripper only;
AgiBot eef is xyz-only).
## Evaluation
`eval_ia_bench.py` scores predicted start (`initial_object_box`) and target
(`target_object_box`) boxes against the GT and reports the paper metrics:
`acc@IoU`, `AUROC`, `AURC`, `E-AURC`, `cov@90`, `cov@95`, `R@90`, `R@95`
(per dataset + overall). A prediction row needs `dataset`, `trajectory_name`,
`subtask_index`, the two boxes, and a confidence `score`.
```bash
python eval_ia_bench.py --predictions preds.jsonl --gt-repo irl-kit/IA-bench
```
## Citation
If you use IA-bench, please cite:
```bibtex
@misc{blank2026sparcreliablespatialannotations,
title={SPARC: Reliable Spatial Annotations from Robot Demonstrations at Scale},
author={Nils Blank and Paul Mattes and Maximilian Xiling Li and Jakub Suliga and Thomas Roth and Moritz Reuss and Pankhuri Vanjani and Rudolf Lioutikov},
year={2026},
eprint={2606.13497},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2606.13497},
}
```
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