Add files using upload-large-folder tool

README.md

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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
+@inproceedings{TODO_ia_bench,
+  title     = {TODO: paper title},
+  author    = {TODO: authors},
+  booktitle = {TODO: venue},
+  year      = {TODO},
+}
+```

data/oxe_lerobot/validation/metadata.jsonl

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eval_ia_bench.py

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+"""Evaluate predicted interacted-object boxes against IA-bench ground truth.
+
+Reports the paper's selective-prediction metrics for the predicted *start*
+(`initial_object_box`) and *target* (`target_object_box`) boxes:
+
+    acc@IoU    fraction of samples with IoU(pred, gt) >= --iou-threshold
+    AUROC      ranking quality of the confidence score (higher better)
+    AURC       area under risk-coverage curve (lower better)
+    E-AURC     excess AURC vs the optimal selector (lower better)
+    cov@90     coverage retained while keeping >=90% accuracy
+    cov@95     coverage retained while keeping >=95% accuracy
+    R@90/R@95  recall at those accuracy targets
+
+Standalone: needs only numpy (+ pandas for .parquet predictions, and
+huggingface_hub if you pass --gt-repo). No dependency on the annotation repo.
+
+Ground truth is read from the IA-bench metadata.jsonl files (boxes only, no
+video decode needed):
+
+    # local build / clone:
+    python eval_ia_bench.py --predictions preds.jsonl --gt-dir /path/to/IA-bench
+    # straight from the Hub:
+    python eval_ia_bench.py --predictions preds.jsonl --gt-repo irl-kit/IA-bench
+
+Predictions file (.jsonl or .parquet), one row per (trajectory, subtask):
+    {"dataset": "bridge_lerobot", "trajectory_name": "0/41905", "subtask_index": 0,
+     "initial_object_box": [x1,y1,x2,y2], "target_object_box": [x1,y1,x2,y2],
+     "score": 0.87}
+`dataset` may be omitted when --dataset names a single config. A per-box
+`initial_score`/`target_score` overrides `score` for that box. GT samples with
+no prediction count as incorrect at the lowest score (full-benchmark coverage).
+"""
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+import numpy as np
+
+# ---------------------------------------------------------------------------
+# Metrics (vendored verbatim from the paper's scoring code)
+# ---------------------------------------------------------------------------
+
+
+def iou(a, b) -> float:
+    a = np.asarray(a, dtype=float)
+    b = np.asarray(b, dtype=float)
+    ix1, iy1 = max(a[0], b[0]), max(a[1], b[1])
+    ix2, iy2 = min(a[2], b[2]), min(a[3], b[3])
+    inter = max(0.0, ix2 - ix1) * max(0.0, iy2 - iy1)
+    area_a = max(0.0, a[2] - a[0]) * max(0.0, a[3] - a[1])
+    area_b = max(0.0, b[2] - b[0]) * max(0.0, b[3] - b[1])
+    union = area_a + area_b - inter
+    return inter / union if union > 0 else 0.0
+
+
+def _risk_coverage(pred_scores, correct_flags):
+    pred_scores = np.asarray(pred_scores, dtype=float)
+    correct_flags = np.asarray(correct_flags, dtype=bool)
+    valid = ~np.isnan(pred_scores)
+    pred_scores, correct_flags = pred_scores[valid], correct_flags[valid]
+    n = len(pred_scores)
+    if n == 0:
+        return None, None, 0
+    order = np.argsort(pred_scores)[::-1]
+    cum_correct = np.cumsum(correct_flags[order].astype(float))
+    counts = np.arange(1, n + 1, dtype=float)
+    risk = 1.0 - cum_correct / counts
+    return risk, counts, int(np.sum(correct_flags))
+
+
+def compute_aurc(pred_scores, correct_flags) -> float:
+    risk, _counts, _ = _risk_coverage(pred_scores, correct_flags)
+    return float(np.mean(risk)) if risk is not None else np.nan
+
+
+def compute_eaurc(pred_scores, correct_flags) -> float:
+    risk, counts, n_correct = _risk_coverage(pred_scores, correct_flags)
+    if risk is None:
+        return np.nan
+    n = len(counts)
+    aurc = float(np.mean(risk))
+    opt = np.zeros(n, dtype=float)
+    opt[:n_correct] = 1.0
+    aurc_opt = float(np.mean(1.0 - np.cumsum(opt) / counts))
+    return aurc - aurc_opt
+
+
+def compute_auroc(pred_scores, correct_flags) -> float:
+    pred_scores = np.asarray(pred_scores, dtype=float)
+    correct_flags = np.asarray(correct_flags, dtype=bool)
+    valid = ~np.isnan(pred_scores)
+    pred_scores, correct_flags = pred_scores[valid], correct_flags[valid]
+    n_pos = int(np.sum(correct_flags))
+    n_neg = len(correct_flags) - n_pos
+    if n_pos == 0 or n_neg == 0:
+        return np.nan
+    order = np.argsort(pred_scores)[::-1]
+    sorted_labels = correct_flags[order]
+    tpr = np.concatenate([[0.0], np.cumsum(sorted_labels) / n_pos])
+    fpr = np.concatenate([[0.0], np.cumsum(~sorted_labels) / n_neg])
+    trapz = getattr(np, "trapezoid", None) or np.trapz
+    return float(trapz(tpr, fpr))
+
+
+def compute_recall_at_target_accuracy(pred_scores, correct_flags, target_accuracy=0.9):
+    pred_scores = np.asarray(pred_scores, dtype=float)
+    correct_flags = np.asarray(correct_flags, dtype=bool)
+    valid = ~np.isnan(pred_scores)
+    pred_scores, correct_flags = pred_scores[valid], correct_flags[valid]
+    total_correct = int(np.sum(correct_flags))
+    empty = {"recall": 0.0, "coverage": 0.0, "threshold": np.nan, "kept_accuracy": np.nan}
+    if len(pred_scores) == 0 or total_correct == 0:
+        return empty
+    order = np.argsort(pred_scores)[::-1]
+    sorted_scores = pred_scores[order]
+    cum_correct = np.cumsum(correct_flags[order].astype(int))
+    counts = np.arange(1, len(sorted_scores) + 1)
+    acc = cum_correct / counts
+    recall = cum_correct / total_correct
+    valid_pos = acc >= target_accuracy
+    if not np.any(valid_pos):
+        return empty
+    best = int(np.argmax(np.where(valid_pos, recall, -np.inf)))
+    return {
+        "recall": float(recall[best]),
+        "coverage": float(counts[best] / len(sorted_scores)),
+        "threshold": float(sorted_scores[best]),
+        "kept_accuracy": float(acc[best]),
+    }
+
+
+# ---------------------------------------------------------------------------
+# Data loading
+# ---------------------------------------------------------------------------
+
+
+def _read_jsonl(path: Path) -> list[dict]:
+    with Path(path).open() as f:
+        return [json.loads(line) for line in f if line.strip()]
+
+
+def load_predictions(path: Path) -> list[dict]:
+    path = Path(path)
+    if path.suffix == ".parquet":
+        import pandas as pd
+
+        return pd.read_parquet(path).to_dict("records")
+    return _read_jsonl(path)
+
+
+def load_gt(gt_dir: Path | None, gt_repo: str | None, datasets, splits) -> dict:
+    """Return {(dataset, trajectory_name, subtask_index): row} from metadata.jsonl."""
+    if gt_repo:
+        from huggingface_hub import snapshot_download
+
+        gt_dir = Path(snapshot_download(
+            repo_id=gt_repo, repo_type="dataset", allow_patterns=["data/*/*/metadata.jsonl"]
+        ))
+    if gt_dir is None:
+        raise SystemExit("provide --gt-dir or --gt-repo")
+    gt_dir = Path(gt_dir)
+
+    gt = {}
+    for meta in sorted(gt_dir.glob("data/*/*/metadata.jsonl")):
+        ds, split = meta.parts[-3], meta.parts[-2]
+        if datasets and ds not in datasets:
+            continue
+        if splits and split not in splits:
+            continue
+        for row in _read_jsonl(meta):
+            gt[(ds, row["trajectory_name"], int(row["subtask_index"]))] = row
+    if not gt:
+        raise SystemExit(f"no GT metadata found under {gt_dir}/data/*/*/metadata.jsonl")
+    return gt
+
+
+# ---------------------------------------------------------------------------
+# Evaluation
+# ---------------------------------------------------------------------------
+
+BOX_FIELDS = {"initial": "initial_object_box", "target": "target_object_box"}
+
+
+def _pred_index(preds, default_dataset):
+    idx = {}
+    for p in preds:
+        ds = p.get("dataset") or default_dataset
+        if ds is None:
+            raise SystemExit("predictions need a 'dataset' field (or pass --dataset <config>)")
+        idx[(ds, p["trajectory_name"], int(p["subtask_index"]))] = p
+    return idx
+
+
+def evaluate(gt, preds, iou_threshold, default_dataset):
+    pred_idx = _pred_index(preds, default_dataset)
+    # group keys by dataset (+ an 'all' bucket)
+    buckets: dict[str, list] = {}
+    for key in gt:
+        buckets.setdefault(key[0], []).append(key)
+        buckets.setdefault("all", []).append(key)
+
+    results = {}
+    for box_name, gt_field in BOX_FIELDS.items():
+        for bucket, keys in buckets.items():
+            scores, correct = [], []
+            n_pred = 0
+            for key in keys:
+                gt_box = gt[key].get(gt_field)
+                p = pred_idx.get(key)
+                if p is None or p.get(gt_field) is None or gt_box is None:
+                    scores.append(-np.inf)   # unpredicted → ranked last, counts as wrong
+                    correct.append(False)
+                    continue
+                n_pred += 1
+                score = p.get(f"{box_name}_score", p.get("score", 1.0))
+                scores.append(float(score) if score is not None else -np.inf)
+                correct.append(iou(p[gt_field], gt_box) >= iou_threshold)
+            scores = np.asarray(scores, dtype=float)
+            correct = np.asarray(correct, dtype=bool)
+            r90 = compute_recall_at_target_accuracy(scores, correct, 0.90)
+            r95 = compute_recall_at_target_accuracy(scores, correct, 0.95)
+            results[(box_name, bucket)] = {
+                "n": len(keys),
+                "n_pred": n_pred,
+                "acc": float(np.mean(correct)) if len(correct) else np.nan,
+                "AUROC": compute_auroc(scores, correct),
+                "AURC": compute_aurc(scores, correct),
+                "E-AURC": compute_eaurc(scores, correct),
+                "cov@90": r90["coverage"],
+                "cov@95": r95["coverage"],
+                "R@90": r90["recall"],
+                "R@95": r95["recall"],
+            }
+    return results
+
+
+def _fmt(v):
+    if isinstance(v, float):
+        return "nan" if np.isnan(v) else f"{v:.3f}"
+    return str(v)
+
+
+def print_table(results, box_name):
+    cols = ["n", "n_pred", "acc", "AUROC", "AURC", "E-AURC", "cov@90", "cov@95", "R@90", "R@95"]
+    rows = sorted(k[1] for k in results if k[0] == box_name and k[1] != "all")
+    rows.append("all")
+    print(f"\n=== {box_name} box (IoU correctness) ===")
+    print(f"{'dataset':16s} " + " ".join(f"{c:>7s}" for c in cols))
+    for bucket in rows:
+        m = results[(box_name, bucket)]
+        print(f"{bucket:16s} " + " ".join(f"{_fmt(m[c]):>7s}" for c in cols))
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--predictions", required=True, type=Path)
+    ap.add_argument("--gt-dir", type=Path, default=None, help="IA-bench root (local build/clone)")
+    ap.add_argument("--gt-repo", type=str, default=None, help="HF dataset id, e.g. irl-kit/IA-bench")
+    ap.add_argument("--dataset", nargs="*", default=None,
+                    help="restrict to these dataset configs (also default 'dataset' for preds)")
+    ap.add_argument("--split", nargs="*", default=None, help="restrict to these splits")
+    ap.add_argument("--iou-threshold", type=float, default=0.4)
+    ap.add_argument("--out", type=Path, default=None, help="optional JSON dump of all metrics")
+    args = ap.parse_args()
+
+    gt = load_gt(args.gt_dir, args.gt_repo, set(args.dataset or []), set(args.split or []))
+    preds = load_predictions(args.predictions)
+    default_dataset = args.dataset[0] if args.dataset and len(args.dataset) == 1 else None
+    results = evaluate(gt, preds, args.iou_threshold, default_dataset)
+
+    print(f"GT samples: {len(gt)} | predictions: {len(preds)} | IoU>={args.iou_threshold}")
+    print_table(results, "initial")
+    print_table(results, "target")
+
+    if args.out:
+        dump = {f"{box}/{bucket}": m for (box, bucket), m in results.items()}
+        args.out.write_text(json.dumps(dump, indent=2))
+        print(f"\nwrote {args.out}")
+
+
+if __name__ == "__main__":
+    main()