Datasets:

Weizm
/

AtomAction_Dataset

pretty_name: AtomAction Dataset
language:
  - en
tags:
  - robotics
  - rlbench
  - robot-manipulation
  - imitation-learning
  - multimodal
  - image
  - timeseries
size_categories:
  - 10K<n<100K

AtomAction Dataset

AtomAction is a phase-level robot-manipulation dataset derived from RLBench expert demonstrations. Full task demonstrations are segmented into short, semantically labeled atomic-action phases and reorganized as:

atomic action -> RLBench task -> task variation -> phase sample

Each phase contains a variable-length low-dimensional robot trajectory, start/end observations from five cameras, segmentation metadata, and an atomic action label. The release is intended for atomic-action representation learning, robot trajectory modeling, phase classification, multimodal pretraining, and analysis of reusable manipulation primitives.

Dataset Summary

Property	Value
Atomic-action labels	18
Unique RLBench tasks	69
Task variations	545
Phase samples	57,803
Camera views	5
Image resolution	256 x 256
Official train/validation/test split	Not provided

The 18 action labels are:

approach, flip-close, flip-open, grasp, hang, insert, lift, place,
pose-adjust, press, pull, push, revolve-in, revolve-out, rotate,
slide, transfer, wipe

Samples per action

Action	Phase samples
`approach`	11,998
`flip-close`	400
`flip-open`	200
`grasp`	13,074
`hang`	100
`insert`	283
`lift`	9,104
`place`	6,265
`pose-adjust`	1,307
`press`	1,200
`pull`	800
`push`	800
`revolve-in`	200
`revolve-out`	600
`rotate`	2,132
`slide`	600
`transfer`	8,340
`wipe`	400

The class distribution is imbalanced. Use per-class sampling, weighting, or macro-averaged metrics where appropriate.

Directory Structure

AtomAction_Dataset/
├── dataset_metadata.json
├── build_atomaction_lowdim_features.py
└── {action}/
    ├── action_metadata.json
    └── {task}/
        ├── task_metadata.json
        └── variation{N}/
            ├── variation_metadata.json
            └── phase_{NNN}/
                ├── low_dim_obs.pkl
                ├── phase_metadata.json
                ├── front_{rgb,depth,mask}/
                ├── left_shoulder_{rgb,depth,mask}/
                ├── right_shoulder_{rgb,depth,mask}/
                ├── overhead_{rgb,depth,mask}/
                └── wrist_{rgb,depth,mask}/

One phase_{NNN} directory is one dataset sample. The numeric phase name is an output sample index within its action/task/variation directory; the original phase index and frame boundaries are recorded in phase_metadata.json.

Data Fields

Low-dimensional trajectory

low_dim_obs.pkl stores a Python list of rlbench.backend.observation.Observation objects, one per trajectory step. The canonical trajectory fields used by this dataset are:

Field	Per-frame shape	Description
`joint_positions`	7	Robot joint positions
`joint_velocities`	7	Robot joint velocities
`joint_forces`	7	Robot joint forces/torques
`gripper_pose`	7	XYZ position and XYZW quaternion
`gripper_open`	scalar	Gripper open state
`gripper_joint_positions`	2	Gripper joint positions
`gripper_touch_forces`	6	Gripper touch-force readings

The trajectory length varies by phase. Image arrays and point clouds inside the pickled observations were cleared before serialization to reduce file size; camera observations are stored separately as PNG files.

Global low-dimensional statistics are available in dataset_metadata.json["traj_stats"]. These include sample counts, mean, variance, standard deviation, minimum, maximum, and 1st/99th percentiles.

Camera observations

The five views are:

front, left_shoulder, right_shoulder, overhead, wrist

For each view, the phase directory contains RGB, depth, and mask folders. The saved PNGs correspond to phase boundary observations (normally the start and end frames), while low_dim_obs.pkl contains the complete low-dimensional sequence.

RGB files are standard 8-bit RGB PNGs.
Depth files are RLBench RGB-encoded depth PNGs and should be decoded with RLBench utilities rather than treated as color images.
Mask files are RGB PNGs containing simulator segmentation-mask values.

Metadata

dataset_metadata.json: dataset-wide counts, label inventory, and trajectory statistics.
action_metadata.json: task inventory and counts for one atomic action.
task_metadata.json: variation inventory and statistics for one action/task pair.
variation_metadata.json: per-phase action labels and eight English instruction styles (short, clear, object_focused, goal_focused, detailed, abstract, natural, and instructional).
phase_metadata.json: source phase index, keyframe, frame boundaries, trajectory length, interaction signals, and optional cached camera-view change scores.

Download and Load

This repository uses a native RLBench directory layout rather than a Parquet/Arrow table. Download the complete repository with Git LFS or huggingface_hub:

git lfs install
git clone https://huggingface.co/datasets/<YOUR_HF_NAMESPACE>/AtomAction_Dataset

or:

from huggingface_hub import snapshot_download

dataset_root = snapshot_download(
    repo_id="<YOUR_HF_NAMESPACE>/AtomAction_Dataset",
    repo_type="dataset",
)
print(dataset_root)

Because the low-dimensional trajectories contain pickled RLBench Observation objects, load them only from a trusted source and use a compatible RLBench/PyRep environment:

import json
import pickle
from pathlib import Path

from PIL import Image

root = Path("AtomAction_Dataset")
phase_dir = root / "grasp" / "open_drawer" / "variation0" / "phase_000"

with (phase_dir / "low_dim_obs.pkl").open("rb") as f:
    observations = pickle.load(f)

with (phase_dir / "phase_metadata.json").open(encoding="utf-8") as f:
    metadata = json.load(f)

rgb_files = sorted(
    (phase_dir / "front_rgb").glob("*.png"),
    key=lambda path: int(path.stem),
)
start_rgb = Image.open(rgb_files[0]).convert("RGB")
end_rgb = Image.open(rgb_files[-1]).convert("RGB")

print(metadata["action"], metadata["task_name"])
print("trajectory length:", len(observations))
print("first gripper pose:", observations[0].gripper_pose)
print("RGB size:", start_rgb.size)

To enumerate every phase without assuming a fixed task list:

import json
from pathlib import Path


def iter_phase_dirs(root: Path):
    dataset_meta = json.loads(
        (root / "dataset_metadata.json").read_text(encoding="utf-8")
    )
    for action in dataset_meta["actions"]:
        action_dir = root / action
        action_meta = json.loads(
            (action_dir / "action_metadata.json").read_text(encoding="utf-8")
        )
        for task in action_meta["tasks"]:
            task_dir = action_dir / task
            task_meta = json.loads(
                (task_dir / "task_metadata.json").read_text(encoding="utf-8")
            )
            for variation in task_meta["variation_names"]:
                yield from sorted((task_dir / variation).glob("phase_*"))


phase_dirs = list(iter_phase_dirs(Path("AtomAction_Dataset")))
print(len(phase_dirs))  # 57803

Auxiliary Feature-Extraction Script

build_atomaction_lowdim_features.py is an optional offline analysis utility; it is not required to load or train on the dataset. For every phase, it concatenates the seven canonical low-dimensional fields into a 37-dimensional per-frame vector and summarizes the sequence with:

start + end + (end - start) + mean + standard deviation

This produces a 185-dimensional phase feature and exports compressed NPZ, CSV, and JSON summary files. --max-per-action applies deterministic per-action reservoir sampling, which is useful for balanced visualization or classical machine-learning experiments.

python build_atomaction_lowdim_features.py \
  --root . \
  --out-dir ./lowdim_features \
  --prefix atomaction_lowdim \
  --max-per-action 400 \
  --seed 42

The script may be removed if these derived low-dimensional summaries do not need to be regenerated. Removing it does not remove data and does not affect the native dataset layout.

Intended Uses

Learning representations or codebooks for reusable atomic robot actions.
Atomic-action or phase classification.
Modeling variable-length low-dimensional manipulation trajectories.
Multimodal learning from robot state and multi-view phase-boundary images.
Studying transfer across RLBench tasks and variations.

Limitations and Responsible Use

All observations are generated in simulation; results may not transfer directly to real robots.
The action classes are substantially imbalanced.
No official train/validation/test split is included. Random phase-level splitting can leak very similar demonstrations across splits. Prefer task-level or variation-level splits and report the split protocol.
The release includes pose-adjust. Its annotation quality should be audited before training; the associated VQAP training configuration excludes this label.
Phase descriptions and action boundaries may contain annotation errors.
Pickle can execute arbitrary code during deserialization. Do not unpickle files from untrusted or modified copies of the dataset.
The dataset is designed for research in simulation, not direct deployment on physical robots or safety-critical control.

License

No dataset-specific license file was present when this card was prepared. Users should not infer a permissive license from this README. Before using or redistributing the dataset, review the RLBench license and the terms of its third-party simulator assets, and add an explicit dataset license to this repository.

Citation

If you use this dataset, cite the dataset release (once a canonical citation is provided by the maintainers) and the RLBench paper:

@article{james2020rlbench,
  title   = {RLBench: The Robot Learning Benchmark \& Learning Environment},
  author  = {James, Stephen and Ma, Zicong and Rovick Arrojo, David and Davison, Andrew J.},
  journal = {IEEE Robotics and Automation Letters},
  year    = {2020}
}