README.md

---
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}, 
}
```