--- license: mit task_categories: - robotics tags: - LeRobot - SO-101 - SO-100 - robot-manipulation - teleoperation - imitation-learning - world-models - action-conditioned-video-prediction size_categories: - 100K +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: ```text 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 ```python 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](TRAINING.md) exactly. The short form is: ```bash 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](https://huggingface.co/Samisaliveagain) and [Shubham](https://huggingface.co/shubham4413). This release is maintained and published from the `Samisaliveagain` account. ## Citation ```bibtex @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