Datasets:

wzlllong
/

value-model-benchmark-training-data

Each of the six dataset roots has annotations/subtasks.json (schema v2). Its tasks list contains local task indices, task names, instructions, and S-numbered definitions. Each subtask has subtask_id, a qualified definition_id (domain/task_key/Sn), and definition_en; original label IDs and wording are retained where needed. reference records the RoboValue source publication and page references. Definitions alone do not create temporal labels.

Dataset root	Tasks / definitions	Available temporal labels
`training-data/simulation/full-shot`	15 / 58	None
`training-data/simulation/one-shot`	15 / 58	58 segments and 8,080 frame labels
`training-data/real-world/full-shot`	20 / 97	None
`training-data/real-world/one-shot`	20 / 97	None
`evaluation/simulation/test-30`	4 / 19	54 existing segments in 12 ED/MS episodes
`evaluation/real-world/test-30`	4 / 21	153 existing segments across 30 episodes

annotations/subtask_segments.parquet is available in Simulation One-Shot and both public test roots. Simulation One-Shot additionally provides annotations/frame_labels.parquet; its original columns and values are retained. Definitions, temporal labels, and test metric memberships are read separately from LeRobot model observations.

Read test definitions and intervals

The following example downloads just the definition and interval files, then finds the annotated subtask at frame 100 of real-world episode 0. Change prefix to the simulation test root to read its labels. An uncovered frame returns no label. Use the fixed revision shown below for downloading and loading.

import json
from pathlib import Path
import pyarrow.parquet as pq
from huggingface_hub import snapshot_download

repo_id = "wzlllong/value-model-benchmark-training-data"
revision = "3f21eb1beeedaf1b1d5aaacda4a033174521d8c3"
prefix = "evaluation/real-world/test-30"
snapshot = snapshot_download(
    repo_id=repo_id, repo_type="dataset", revision=revision,
    allow_patterns=[f"{prefix}/annotations/subtasks.json",
                    f"{prefix}/annotations/subtask_segments.parquet"],
    local_dir="./robovalue-subtasks",
)
root = Path(snapshot) / prefix
annotations = json.loads((root / "annotations/subtasks.json").read_text())
definitions = {s["definition_id"]: s for t in annotations["tasks"]
               for s in t["subtasks"]}
segments = pq.read_table(root / "annotations/subtask_segments.parquet",
    filters=[("episode_index", "=", 0), ("start_frame", "<=", 100),
             ("end_frame_exclusive", ">", 100)])
for row in segments.to_pylist():
    definition = definitions[row["definition_id"]]
    print(row["subtask_id"], definition["definition_en"])

Test intervals are local, zero-based [start_frame, end_frame_exclusive) ranges. The table retains source_segment_index, source_subtask_id, original episode/task indices, and local/global endpoints. It is not a metric-query list: select eligible episodes from the unchanged annotations/metric_membership.json. Test source annotations and simulation object/action/arm identities remain in their original files.

Read Simulation One-Shot frame labels

After the imports and revision above, run:

prefix = "training-data/simulation/one-shot"
snapshot = snapshot_download(
    repo_id=repo_id, repo_type="dataset", revision=revision,
    allow_patterns=[f"{prefix}/annotations/subtasks.json",
                    f"{prefix}/annotations/frame_labels.parquet"],
    local_dir="./robovalue-subtasks",
)
root = Path(snapshot) / prefix
annotations = json.loads((root / "annotations/subtasks.json").read_text())
labels = pq.read_table(root / "annotations/frame_labels.parquet",
    filters=[("episode_index", "=", 0), ("frame_index", "=", 100)])
for row in labels.to_pylist():
    task = next(t for t in annotations["tasks"]
                if t["task_index"] == row["task_index"])
    definition = next(s for s in task["subtasks"]
                      if s["subtask_id"] == row["subtask_id"])
    print(definition["definition_en"], row["boundary_uncertain"])

Join frame labels to native samples using episode_index and frame_index; global_index matches the LeRobot index. Training segments use half-open [start_frame, end_frame) ranges, so the last included frame is end_frame - 1. Initial waiting belongs to the first subtask, and the final subtask extends to the episode end. boundary_uncertain preserves the supplied adjustment band.

Indices are local to each subset. An ordinal S-number identifies a stage, not a fixed object across execution orders. Missing temporal labels remain absent, including simulation TRR/MEM intervals; reference definitions and sampling anchors do not supply missing ground truth.