Samisaliveagain/so101_wm

SO-101 Dual-View Teleoperation Dataset for World Models

This dataset contains leader-follower teleoperation recorded with an SO-101 follower arm, six joint-position command/state channels, and two synchronized RGB cameras. It was collected in two phases: repeated goal-directed pick-and-place demonstrations for visuomotor policy research, followed by longer exploratory interaction intended to broaden the state-transition coverage available to visual world models.

This is the privacy-cleaned release. Identifiable faces in the wrist-camera stream were blurred without deleting frames or changing video timing. The recorded Parquet rows, timestamps, commands, and measured follower states are not shifted or rewritten to compensate for physical control delay.

What raw means in this release

This is a privacy-cleaned raw sensor release, not a derived policy dataset.

Unchanged recorded data:

• Every Parquet action, measured state, identifier, frame index, and timestamp
• Every fixed-camera frame
• Video frame order, timing, duration, geometry, and codec
• Episode boundaries and task strings

Intentional release additions or modifications:

• Identifiable faces are blurred in five FPV files. Re-encoding changes pixels and compressed bytes but preserves every per-frame timestamp.
• Human interventions are described by an exclusion manifest. No source rows or frames are deleted or concatenated.
• Repaired descriptive statistics, train-only normalization, deterministic episode splits, audit reports, and training adapters are included.

The complete V-JEPA 2-AC procedure is in TRAINING.md. Run its preflight before submitting a GPU job.

Dataset summary

Property	Value
Episodes	177
Frames	355,884
Recorded duration	3.295 hours
Recording rate	30 Hz
Robot state dimension	6
Robot action dimension	6
Cameras	2 synchronized RGB streams
Video format	AV1, 640 x 480, 30 fps, yuv420p
Audio	None
Depth/force/torque	Not recorded
LeRobot format	v3.0

All episodes remain addressable through the standard LeRobot train split for compatibility. A deterministic, collection-stratified 141/18/18 episode split is provided in meta/cleaning/episode_splits.json; use that manifest for model selection and final evaluation rather than splitting frames randomly.

Collection phases

The two task_index values describe collection intent. They are not success/failure or reward labels.

task_index	Episodes	Frames	Share of frames	Collection intent
0	0-149	113,691	31.95%	Repeated goal-directed pick-and-place demonstrations originally collected for VLA/policy training
1	150-176	242,193	68.05%	Longer exploratory interaction collected to broaden world-model dynamics coverage

The task strings stored in meta/tasks.parquet are:

0: Grab the red hexagon on the right and place it on the red hexagon on the left.
1: exploratory training for world models

Only 27 of the 177 episodes are exploratory, but those episodes are much longer and therefore contain approximately 68% of all frames. Uniform random frame sampling will consequently devote approximately 68% of optimization to the exploratory phase. This is not data corruption; it is a training-sampling decision.

For action-conditioned world-model experiments, compare at least:

• Natural frame mixture: approximately 32% demonstrations / 68% exploration
• Balanced mixture: 50% demonstrations / 50% exploration
• Task-weighted mixture: 70% demonstrations / 30% exploration

The 70/30 setting is a recommended experiment, not a claim that it is optimal. Select the mixture using held-out results reported separately for the two collection phases. Do not delete the unused group to create a mixture; choose the group and episode hierarchically in the sampler.

Sampling ratio and dataset split are separate choices: use only training episodes first, then apply the desired demonstration/exploration mixture inside that set.

Recording setup

• Follower: SO-101 arm (robot_type: so_follower).
• Input device: paired SO-100/SO-101-style leader arm.
• Control: the leader supplies commanded joint-position targets and the follower reports its measured joint positions.
• Rate: commands, follower state, and both cameras are recorded at 30 Hz.
• Fixed camera: observation.images.left, a stable global workspace view.
• Wrist camera: observation.images.fpv, a moving local view near the gripper and contact area.

The two camera streams and the numeric rows are synchronized. Matching record timestamps do not imply an instantaneous mechanical response: the follower requires time to process and physically execute a leader command.

Data fields

Key	Type	Shape	Meaning
action	float32	(6,)	Commanded leader joint-position target sent toward the follower
observation.state	float32	(6,)	Measured follower joint position
observation.images.left	video	(480, 640, 3)	Fixed third-person RGB camera
observation.images.fpv	video	(480, 640, 3)	Wrist-mounted RGB camera
timestamp	float32	(1,)	Seconds from the start of the episode
frame_index	int64	(1,)	Frame index inside the episode
episode_index	int64	(1,)	Episode identifier
index	int64	(1,)	Global row identifier
task_index	int64	(1,)	Collection-phase identifier

Joint ordering for both action and observation.state:

shoulder_pan.pos
shoulder_lift.pos
elbow_flex.pos
wrist_flex.pos
wrist_roll.pos
gripper.pos

The integer identifiers and timestamps are alignment metadata. They should not be normalized or supplied as physical action/state values.

Command-to-motion delay

Cross-correlation between commanded targets and future measured follower state shows an end-to-end response delay of approximately:

Joint	Estimated response delay
Shoulder pan	4 frames / 133 ms
Shoulder lift	4 frames / 133 ms
Elbow flex	5 frames / 167 ms
Wrist flex	4 frames / 133 ms
Wrist roll	4 frames / 133 ms
Gripper	Approximately 4 frames / 133 ms, with larger residual error

This is not evidence that timestamps were recorded incorrectly. It is the combined delay of leader sensing, processing/communication, follower control, motor mechanics, and follower-state measurement. The available data does not contain separate timestamps for each of those stages, so their individual contributions cannot be isolated retrospectively.

Recommended action conditioning

Do not modify the raw dataset by shifting action rows. Construct temporal conditioning windows inside the loader.

For a visual transition sampled at approximately 4 Hz (250 ms):

1. Read the current camera clip and measured follower state.
2. Include approximately five preceding 30 Hz commands to cover the measured 133-167 ms response delay.
3. Include the seven or eight ordered commands issued over the visual transition.
4. Resample the complete interval to a fixed ordered sequence and encode it with a learned temporal action projection.
5. Predict the next visual latent. Next measured state prediction can be added as an auxiliary experiment, but it is not part of the supplied baseline.

Because 30 / 4 is not an integer, preserve timestamps and use timestamp-based resampling/masks rather than assuming every visual interval contains exactly the same number of control samples.

This representation allows the model to learn the delay and joint-dependent response from data without discarding or fabricating measurements.

The bundled adapter uses 13 ordered samples for each transition. Five source control frames cover the preceding 167 ms, and the remaining temporal extent reaches the following visual frame. With six joints, the action projection receives 13 x 6 = 78 ordered values. The current normalized six-dimensional measured state is encoded separately.

Wrist-roll cable constraint

The wrist cannot rotate continuously through 360 degrees because the motor cable limits the safe route. When attempting to reach an equivalent orientation near the -180/+180 representation boundary, the controller can command a value on the opposite side of that boundary while the physical wrist remains continuous and then reverses direction.

A representative recorded transition occurs in episode 174 near 288.60 s:

commanded wrist target: -179.824 -> +179.912
measured wrist state:   -174.374 -> -175.604

The approximate 360-degree numerical command jump is therefore not an instantaneous physical rotation.

For learning and planning:

• Treat wrist roll as a bounded, cable-constrained joint.
• Preserve the raw commanded target and measured motor coordinate.
• Provide command/state history, velocity, and direction.
• Do not use shortest-angle wrapping as the sole representation.
• Sine/cosine orientation may be added only as an auxiliary feature; it must not replace the cable-aware motor coordinate.
• Enforce measured hardware-safe clockwise and anticlockwise limits in any controller or planner. Safe limits are hardware configuration, not values inferred solely from this dataset.

Camera characteristics

The fixed camera provides stable global scene context. The wrist camera gives useful local contact information and includes deliberate look-around motion, but it is sometimes partly occupied by the gripper or a nearby workpiece.

Temporary FPV occlusion does not make a frame invalid. Recommended models should combine:

• Both synchronized views
• Several preceding visual frames
• Explicit camera/view embeddings
• Global fixed-camera context with local FPV contact detail

The supplied V-JEPA 2-AC baseline follows the official DROID loader pattern and randomly selects one synchronized camera view for each clip. This trains one view-robust predictor without changing the pretrained encoder input shape. Explicit simultaneous dual-view fusion is a useful later experiment, not a feature silently claimed by the baseline.

Normal motion blur is retained because it occurs during real robot motion and is likely to occur at deployment. A clip should be excluded for visual quality only when several consecutive frames are corrupted or both views simultaneously lose the robot, objects, and usable scene context. No such systemic corruption was found during the current integrity review.

Privacy cleaning

Identifiable faces were found only in selected wrist-camera videos. The cleaned release blurs faces in:

file-003.mp4
file-005.mp4
file-007.mp4
file-008.mp4
file-010.mp4

The cleaning process preserves each affected video's AV1 codec, 640 x 480 resolution, 30 fps rate, yuv420p pixel format, duration, frame order, and exact frame count. The other eight wrist-camera files remain byte-identical to their source versions. All affected files passed complete decode validation.

An additional manual review confirmed and blurred a face in file-005.mp4 from approximately 00:05:01.35 through 00:05:02.92. Fast-rotation detector gaps at the entrance and top-edge exit are covered by reviewed manual boxes. Exact curated windows are recorded in meta/cleaning/privacy_blur_windows.json.

Blurring changes decoded pixels and therefore necessarily changes compressed file bytes. It does not change video/action/state alignment.

Human resets and external intervention

Some collection intervals contain a human hand resetting workpieces, and a few contain possible manual contact with the follower. Such motion is exogenous: the visual scene or measured follower state may change without being explained by the recorded robot command.

Do not remove those frames from only one video and concatenate the remaining sections. That would create a false object teleportation. Instead:

• Keep source videos and Parquet rows unchanged.
• Exclude synchronized video, action, and state intervals together.
• Prevent visual clips and action-history windows from crossing an exclusion.
• Split a logical episode around a reset that occurs inside an episode.
• Add a short guard interval for hand entry, object settling, and hand exit.

Most reviewed resets occur at existing episode boundaries, which already stop ordinary episode-aware samplers from crossing them. Resets inside longer episodes still require an explicit exclusion manifest.

The release provides meta/cleaning/intervention_exclusions.csv. It contains 17 reviewed source-video intervals mapped into 27 episode-local ranges because some intervals cross episode boundaries. Fifteen confirmed interventions are active exclusions; two uncertain human-near-robot intervals remain included and are marked reviewed_unconfirmed. Active intervals include a 0.5-second guard on both sides. Apply the manifest as a clip-validity mask; it does not physically remove or concatenate frames.

Examples visible in observation.images.left:

Video	Source time	What is visible
file-000.mp4	00:04:21.5-00:04:23.0	A hand repositions a red workpiece in episode 8
file-001.mp4	00:03:19.0-00:03:22.0	Two hands reset the red blocks across episodes 19 and 20
file-021.mp4	00:03:27.5-00:03:30.0	A hand relocates a red block inside exploratory episode 163

Exclusion changes which candidate clips may be sampled, not the stored data. For a candidate start time, the loader considers its complete visual context, preceding command history, and prediction target. If any part overlaps an active exclusion, that start is invalid. The sampler selects another valid start without joining the clean regions on either side. The released 70/30 training configuration contains 269,278 valid eight-frame clip starts.

Normalization

Normalize only continuous physical features used by the model:

• Six commanded joint targets in action
• Six measured joint positions in observation.state

Do not normalize episode_index, task_index, frame_index, global index, or timestamps. Compute continuous-feature statistics from the cleaned training episodes only, after exclusions and train/validation/test assignment, to avoid validation leakage. Apply the same stored training statistics to validation, test, and deployment inputs.

Normalization is a reversible affine transformation; it does not delete motion information. It improves conditioning so joints with larger numerical ranges do not dominate optimization.

Use meta/cleaning/normalization_stats.json for the supplied episode split. It is computed exactly from 277,478 retained training rows after active intervention exclusions. meta/stats.json is separately repaired as full-dataset descriptive metadata for LeRobot compatibility; it must not be substituted for train-only normalization in a controlled evaluation.

Integrity review

The following checks were performed locally:

• All 177 episode metadata entries agree with Parquet row ranges.
• Global row indices are consecutive.
• Episode timestamps are monotonic.
• Actions and states contain no NaN or infinite values.
• Both camera streams contain 355,884 frames.
• All reviewed videos decode successfully.
• A systematic 1 Hz motion/contrast scan found zero samples where both views simultaneously met the conservative unusable-frame criteria.
• Numeric episode_index values span 0-176 and task_index values span 0-1.
• Privacy-modified videos preserve their original frame counts and stream geometry.
• Per-frame presentation timestamps for all five privacy-modified FPV files match their original counterparts with measured maximum drift of 0 seconds.

The source release's meta/stats.json contained incorrect identifier aggregates (episode_index, task_index, and global index) and invalidly small camera standard deviations, even though the underlying Parquet/video data were sound. The cleaned release repairs those values: numeric statistics use all exact rows and camera statistics use the documented systematic 1 Hz sample in meta/cleaning/video_audit.json. Identifiers remain metadata and must not be normalized.

Reproducibility files

File	Purpose
meta/cleaning/episode_splits.json	Deterministic episode-level train/validation/test assignment
meta/cleaning/intervention_exclusions.csv	Confirmed and reviewed human-intervention intervals in episode-local time
meta/cleaning/normalization_stats.json	Exact train-only action/state normalization after exclusions
meta/cleaning/video_audit.json	Cleaned-video frame counts, sampled pixel statistics, and quality triage
meta/cleaning/privacy_blur_windows.json	Curated face-blur windows and reviewed manual gap coverage
meta/cleaning/alignment_validation.json	Full cross-modal alignment, immutable-asset, split, exclusion, and normalization audit
meta/cleaning/README.md	Artifact semantics and regeneration commands
TRAINING.md	Exact environment, preflight, patch, smoke-test, and distributed-training instructions
training/vjepa2-so101.patch	Adapter for Meta V-JEPA 2 revision 204698b45b3712590f06245fbfba32d3be539812
training/preflight.py	Local frame, split, exclusion, action-window, and sampler verification
training/configure_vjepa2.py	Writes concrete training configs without hand-editing dataset or checkpoint paths

Intended uses

• Action-conditioned latent video prediction
• Dual-view visual world models
• Robot representation learning
• Visuomotor policy and VLA experiments
• Comparing temporal action-conditioning designs
• Offline model-predictive-control research after adding a planner and safety constraints

A world model trained on this dataset is not by itself a deployable policy. A robot deployment still needs action selection/planning, joint and cable safety limits, collision handling, and hardware supervision.

Limitations

• Single robot setup and workspace
• Limited visual/environment diversity
• No depth, force, or torque observations
• No explicit success/failure or reward annotations; these are unnecessary for dynamics learning but limit reward/value learning
• Few externally caused recovery or failure examples
• End-to-end delay is measured, but its communication/mechanical components are not separately timestamped
• Exact safe wrist cable limits must come from the robot/controller setup
• FPV frames can contain expected contact occlusion and motion blur
• Sampling mixture and held-out split must be selected for the downstream experiment

Loading with LeRobot

from lerobot.datasets.lerobot_dataset import LeRobotDataset

dataset = LeRobotDataset("Samisaliveagain/so101_wm")

print(dataset.num_episodes)
print(dataset.num_frames)
sample = dataset[0]
print(sample["action"].shape)
print(sample["observation.state"].shape)

For action-conditioned world-model training, use an episode-aware custom sampler that applies the provided exclusions and constructs timestamp-based command-history windows. A plain single-frame action[t] -> video[t+1] pairing does not represent the measured follower delay adequately.

V-JEPA 2 action-conditioned training

The directly supported starting point is Meta's V-JEPA 2 ViT-g encoder and action-conditioned predictor structure. The official AC implementation expects seven-dimensional Cartesian DROID trajectories, so it cannot consume this six-dimensional joint dataset without adaptation. The supplied pinned patch:

• Reads LeRobot v3 directly
• Applies the released splits and intervention mask
• Implements hierarchical 70/30 sampling
• Builds ordered 30 Hz command-history windows for each 4 Hz transition
• Separates the 78-dimensional command projection from the six-dimensional measured-state projection
• Preserves the wrist-roll motor coordinate without circular wrapping

Meta's repository includes V-JEPA 2.1 visual backbones, but the pinned official code does not contain a V-JEPA 2.1 action-conditioned post-training recipe. This release therefore provides an exact V-JEPA 2-AC baseline first. A 2.1 AC adaptation must be labeled experimental and separately validate its 384-pixel encoder, checkpoint layout, tokenizer, and predictor compatibility.

Follow TRAINING.md exactly. The short form is:

python training/preflight.py --dataset-root .
git clone https://github.com/facebookresearch/vjepa2.git
cd vjepa2
git checkout 204698b45b3712590f06245fbfba32d3be539812
git apply /path/to/so101_wm/training/vjepa2-so101.patch
python tests/test_so101_index.py

Do not deploy a trained predictor directly as a robot policy. Planning, hardware joint limits, wrist cable limits, collision checks, and supervised robot testing remain separate requirements.

Contributors

The raw recordings were collected collaboratively by Samisaliveagain and Shubham. This release is maintained and published from the Samisaliveagain account.

Citation

@misc{so101wm2026,
  title  = {SO-101 Dual-View Teleoperation Dataset for World Models},
  author = {Samisaliveagain and Shubham},
  year   = {2026},
  url    = {https://huggingface.co/datasets/Samisaliveagain/so101_wm}
}

License

MIT