Datasets:
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frame_index int64 | timestamp float32 | episode_index int64 | index int64 | task_index int64 | observation.state.camera_pose list | observation.state.camera_pose_valid int64 | observation.state.left_wrist list | observation.state.right_wrist list | observation.state.left_keypoints_3d list | observation.state.right_keypoints_3d list | observation.state.left_contact int64 | observation.state.right_contact int64 | observation.state.left_mano_rot list | observation.state.right_mano_rot list | observation.state.left_mano_pose list | observation.state.right_mano_pose list | observation.state.left_mano_trans list | observation.state.right_mano_trans list | observation.state.left_mano_betas list | observation.state.right_mano_betas list | observation.state.left_force list | observation.state.right_force list | observation.state.left_tactile list | observation.state.right_tactile list | observation.state.gripper_force list | action.left_wrist_delta list | action.right_wrist_delta list | action.left_grasp int64 | action.right_grasp int64 | action.left_gripper list | action.right_gripper list |
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End of preview. Expand in Data Studio
BaseMatrix EGO Binocular v1
Egocentric bimanual manipulation dataset with 3D hand tracking, EMG muscle signals, and robot-ready action representations. Captured from a first-person perspective using head-mounted stereo cameras and forearm EMG wristbands, processed through a 7-stage automated pipeline.
Key differentiators:
- Egocentric + binocular stereo — first-person view matching humanoid robot camera placement
- 21-joint 3D hand skeleton per hand (MANO topology) — retargetable to any robot hand
- 6-channel surface EMG (11/14 episodes) — muscle activation signals unavailable in any other public manipulation dataset
- Robot-agnostic — human demonstrations retarget to parallel-jaw grippers, dexterous hands, or humanoid manipulators via IK
Format: LeRobot v3.0 — load directly with HuggingFace datasets or LeRobotDataset.
Quick Start
from datasets import load_dataset
ds = load_dataset("basematrix/ego-binocular-v1", split="train")
print(f"{len(ds)} frames, {len(set(ds['episode_index']))} episodes")
frame = ds[1000]
left_kp3d = frame["observation.state.left_keypoints_3d"] # 63D: 21 joints × 3
left_emg = frame["observation.state.left_tactile"] # 48D: 5 EMG features × 6ch + 18D reserved
right_wrist = frame["observation.state.right_wrist"] # 3D: world-frame position (m)
Retarget to Your Robot (5 lines)
import numpy as np
from datasets import load_dataset
ds = load_dataset("basematrix/ego-binocular-v1", split="train")
ep3 = ds.filter(lambda x: x["episode_index"] == 3)
# For parallel-jaw grippers (Franka, UR5, ALOHA):
wrist_right = np.array(ep3["observation.state.right_wrist"]) # (T, 3) end-effector target
grasp_right = np.array(ep3["action.right_grasp"]) # (T,) 0=open, 1=closed
# → Feed wrist position to IK solver, grasp signal to gripper command
# For dexterous hands (LEAP, Allegro, Shadow, Ability):
kp3d = np.array(ep3["observation.state.right_keypoints_3d"]) # (T, 63)
kp3d = kp3d.reshape(-1, 21, 3) # (T, 21, 3) MANO topology
# → Use fingertip-position IK or our retarget_demo.py for joint angle mapping
A complete retargeting example with robot joint angle export is available at examples/retarget_demo.py.
Dataset Summary
| Property | Value |
|---|---|
| Episodes | 14 |
| Total frames | 70,754 |
| Duration | ~39 minutes |
| FPS | 30 |
| Scenes | Desktop manipulation (office environments) |
| Hands | Bimanual (left + right tracked independently) |
| EMG coverage | 11/14 episodes (ep3–ep13, NB3 wristband, 99.3–100% either-hand, 89–97% per-hand) |
Capture Hardware
| Device | Specs | Mounting |
|---|---|---|
| Orbbec Femto Mega | Dual global-shutter fisheye, 1600×1300, 120mm stereo baseline, 1kHz IMU | Head-mounted (egocentric) |
| CEREBLINK NB3 | 6ch surface EMG @1kHz + 9-axis IMU @143Hz, BLE streaming | Forearm wristband |
| iPhone (optional) | 1080p 30fps wide lens | Tripod (exocentric) — not included in this release |
Features
Observation — Vision (2 video streams)
| Feature | Shape | Description |
|---|---|---|
observation.images.ego_left |
(3, 1300, 1600) | Left stereo fisheye, AV1 30fps |
observation.images.ego_right |
(3, 1300, 1600) | Right stereo fisheye, AV1 30fps, frame-synchronized with ego_left |
Observation — Hand State (9 features, ×2 hands)
| Feature | Shape | Source | Description |
|---|---|---|---|
observation.state.{side}_keypoints_3d |
(63,) | WiLoR | 21 MANO joints × 3D in a hand-local frame (meters); joint 0 sits ≈ 9.6 cm from the origin — subtract joint 0 for wrist-relative coordinates |
observation.state.{side}_wrist |
(3,) | HaWoR | Wrist position in world frame (meters) |
observation.state.{side}_mano_pose |
(45,) | HaWoR | MANO hand pose: 15 joints × axis-angle |
observation.state.{side}_mano_rot |
(3,) | HaWoR | MANO global orientation (axis-angle) |
observation.state.{side}_mano_trans |
(3,) | HaWoR | MANO translation in HaWoR's reconstruction frame — not the same frame as {side}_wrist; do not mix the two |
observation.state.{side}_mano_betas |
(10,) | WiLoR | MANO shape coefficients, estimated per frame (average over frames for a stable hand shape) |
observation.state.{side}_contact |
scalar | Hands23 | Hand-object contact binary (0/1) |
observation.state.{side}_tactile |
(48,) | NB3 EMG | 5 features × 6 channels = 30D active + 18D reserved |
observation.state.{side}_force |
(3,) | NB3 EMG | EMG-derived force proxy (accelerometer) |
Observation — Scene State
| Feature | Shape | Source | Description |
|---|---|---|---|
observation.state.camera_pose |
(7,) | ORB-SLAM3 | 6DoF camera pose: tx, ty, tz, qx, qy, qz, qw |
observation.state.camera_pose_valid |
(1,) | ORB-SLAM3 | 1 where the pose is backed by SLAM, 0 where it is not. Zero-pose frames must not be used as poses |
observation.state.gripper_force |
(2,) | NB3 EMG | EMG-derived grip force (left, right) |
Action (6 features)
| Feature | Shape | Description |
|---|---|---|
action.{side}_wrist_delta |
(3,) | Wrist displacement from the previous frame, wrist[t] − wrist[t−1] in meters (0 on an episode's first frame). This is a backward difference; for next-step targets use wrist[t+1] − wrist[t] |
action.{side}_grasp |
scalar | Grasp signal derived from contact + keypoint closure (0=open, 1=grasping) |
action.{side}_gripper |
(7,) | 7D retarget vector from MANO skeleton: [thumb_aperture, index_curl, middle_curl, ring_curl, pinky_curl, spread, opposition] |
Metadata
timestamp, frame_index, episode_index, index, task_index
EMG Muscle Signals
11 out of 14 episodes include 6-channel forearm EMG captured by CEREBLINK NB3 wristbands at 1kHz, downsampled and feature-extracted to 30Hz to align with video frames.
Each observation.state.{side}_tactile vector (48D) layout:
- Index 0–5: RMS × 6 channels
- Index 6–11: MAV (mean absolute value) × 6 channels
- Index 12–17: WL (waveform length) × 6 channels
- Index 18–23: ZC (zero-crossing rate) × 6 channels
- Index 24–29: SSC (slope sign changes) × 6 channels
- Index 30–47: reserved (zeros in current version)
Why EMG matters for robot learning:
- EMG activates 50–200ms before physical contact — a predictive signal for grasp timing
- Co-contraction patterns encode intended stiffness/compliance
- Force intention without requiring robot-mounted sensors
To select EMG-active frames:
import numpy as np
emg = np.array(ds["observation.state.left_tactile"])
emg_active = np.abs(emg).sum(axis=-1) > 0 # True for episodes with wristband data
Hand Tracking Pipeline
Three complementary vision models provide layered hand understanding:
| Model | Output | Role |
|---|---|---|
| Hands23 (HOI-DETR) | Bounding boxes, contact state, grasp type | When and what is being grasped |
| WiLoR-mini | 21 MANO keypoints in 3D (per-frame) | Hand shape and finger articulation |
| HaWoR | Wrist trajectories in world coordinates + MANO pose/orientation/translation (temporal) | Globally consistent hand position and articulation |
The 21-keypoint skeleton follows MANO topology:
- Joint 0: wrist
- Joints 1–4: thumb (CMC → MCP → IP → tip)
- Joints 5–8: index (MCP → PIP → DIP → tip)
- Joints 9–12: middle, 13–16: ring, 17–20: little
Full Processing Pipeline
| Stage | Tool | Output |
|---|---|---|
| L0 | Audio beep detection (1kHz threshold) | Multi-camera time alignment |
| L1 | Nearest-neighbor PTS matching | Stereo frame pairs + NB3 EMG sync |
| L2 | ORB-SLAM3 (stereo) | 6DoF camera trajectory |
| L3 | Hands23 (HOI-DETR) | Hand-object contact + grasp classification |
| L3b | WiLoR-mini | Single-frame 3D hand skeleton |
| L3c | HaWoR | Temporal wrist trajectory in world frame |
| L4 | GPT-4.1 Vision | Semantic annotation (objects, phases) |
| L5 | Cross-layer verification (24 checks) | Quality score + data card |
| L6 | LeRobot v3.0 packaging | This dataset |
Data Quality
| Metric | Value | Notes |
|---|---|---|
| NaN/Inf | 0% | All numeric features verified clean |
| Camera pose coverage | 100% | 70,748 of 70,754 frames carry a SLAM-backed pose (camera_pose_valid == 1); the six exceptions sit past the last keyframe of their episode |
| Keypoint zero frames | L: 9.5%, R: 5.8% | Hand outside FOV or WiLoR detection failure; use abs(kp3d).sum() > 0 as validity mask |
| Wrist trajectory outliers | L: 1.7%, R: 2.6% | HaWoR tracking loss → delta > 3 m/s; apply median filter or clip to ≤0.1 m/frame |
| EMG frame coverage | 99.3–100% either-hand | Per-hand: L 89–97%, R 93–96%. At least one hand has EMG in 99.3–100% of frames |
| Contact detection rate | 97% | Hands23 successfully classified contact in 97% of hand-visible frames (from the pipeline's L5 report; not recomputed for this revision) |
Coordinate System
- World frame: right-handed, Y-up; origin at ORB-SLAM3 initialization point. Scale is metric, recovered from the 120 mm stereo baseline rather than from the IMU
- Keypoints (63D): hand-local frame in meters (WiLoR); subtract joint 0 for wrist-relative coordinates
- Wrist position (3D): absolute world-frame coordinates in meters (HaWoR)
- MANO translation (3D): HaWoR reconstruction frame — differs from the wrist-position frame
- MANO parameters: zero on frames where hand tracking was lost
- Camera pose (7D): position (tx, ty, tz) + unit quaternion (qx, qy, qz, qw)
Use Cases
| Application | Which features to use |
|---|---|
| Imitation learning (ACT, Diffusion Policy) | ego_left + {side}_wrist + {side}_grasp |
| VLA pre-training (π0, Octo, RT-2) | All vision + state + task descriptions |
| Dexterous hand retargeting | {side}_keypoints_3d (63D) → IK to target hand |
| Parallel-jaw gripper control | {side}_wrist_delta + {side}_grasp |
| Grasp timing prediction | {side}_tactile (EMG) + {side}_contact |
| Force-aware manipulation | {side}_tactile + gripper_force |
Comparison with Teleoperation Datasets
| Property | This dataset (EGO) | Typical teleoperation |
|---|---|---|
| Robot-specific? | No — retarget to any robot | Yes — locked to source robot |
| Hand representation | 21-joint 3D skeleton (63D) | 1D gripper open/close |
| Force sensing | EMG muscle signals (predictive) | F/T sensor (reactive) |
| Perspective | Egocentric (matches humanoid cameras) | Third-person fixed cameras |
| Collection cost | Low (no robot hardware needed) | High (robot + operator) |
| Scaling | Parallelizable (multiple wearers) | Sequential (one robot at a time) |
Roadmap
- Scale to 50+ episodes with diverse household manipulation tasks
- Add multi-environment recordings (kitchen, workshop, warehouse)
Changelog
2026-09-13 — re-packaged from source (this revision)
The previous revision (f03d1f6c) had packaging defects. If you downloaded it, please download again.
- Stereo sync fixed. Videos were the raw camera files starting at their own first frame, while each row is defined by a synchronized left/right frame pair. The right view was 1–19 frames out of sync in 13 of 14 episodes (episode 5: the left view, by 12 frames). Each view is now re-encoded so that video frame i is exactly the frame row i refers to.
- Loads at LeRobot's default timestamp tolerance. Video timestamps now start at 0 at a constant 30 fps, matching the
timestampcolumn. Previously most frames failed withFrameTimestampError. - Hand data realigned in episode 5. Wrist, MANO and keypoint columns were offset by 12 frames against contact, camera pose and EMG.
- MANO columns now present. The previous
meta/info.jsondeclared the 8 MANO columns, but the parquet did not contain them. - 8 trailing rows removed (70,762 → 70,754). The camera leaves a truncated final packet with no image; rows whose frame fell on it were dropped (episodes 3–7, 9, 12, 13).
- EXO camera removed. It existed for 1 of 14 episodes, and declaring it made loading fail for the other 13.
- Image normalization statistics added to
meta/stats.json. Videos are encoded as AV1 (GOP 2), LeRobot's default.
Citation
@misc{basematrix2026ego,
title={BaseMatrix EGO Binocular v1: Egocentric Hand Manipulation Dataset with EMG for Robot Learning},
author={BaseMatrix},
year={2026},
url={https://huggingface.co/datasets/basematrix/ego-binocular-v1}
}
License
CC BY-NC 4.0 — free for research and evaluation. Contact info@basematrix.ai for commercial licensing.
Links
- Retarget demo:
examples/retarget_demo.py— MANO keypoints → robot joint angles, export to CSV - Website: basematrix.ai
- X/Twitter: @BaseMatrixAI
- Email: info@basematrix.ai
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