frame_id stringlengths 14 14 | timestamp_ms int64 1,775B 1,775B | capture_rate_hz int64 30 90 | scene_type stringclasses 5
values | clutter_level float64 0.25 0.98 | domain_randomization dict | camera_extrinsics dict | robot_state dict | vision_data dict | objects listlengths 12 42 | annotations dict | edge_case_flags listlengths 0 7 |
|---|---|---|---|---|---|---|---|---|---|---|---|
FRAME-00000001 | 1,775,282,577,973 | 90 | dining_room | 0.525 | {
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FRAME-00000002 | 1,775,282,577,978 | 60 | bathroom | 0.587 | {
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FRAME-00000003 | 1,775,282,577,981 | 30 | bathroom | 0.889 | {
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FRAME-00000004 | 1,775,282,577,984 | 90 | bathroom | 0.453 | {
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FRAME-00000005 | 1,775,282,577,986 | 90 | bathroom | 0.292 | {
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} | [
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FRAME-00000006 | 1,775,282,577,989 | 30 | living_room | 0.905 | {
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FRAME-00000007 | 1,775,282,577,993 | 90 | kitchen | 0.836 | {
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} | [
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FRAME-00000008 | 1,775,282,577,997 | 60 | dining_room | 0.679 | {
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FRAME-00000009 | 1,775,282,578,001 | 90 | dining_room | 0.721 | {
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FRAME-00000010 | 1,775,282,578,005 | 60 | dining_room | 0.6 | {
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FRAME-00000011 | 1,775,282,578,008 | 30 | bedroom | 0.452 | {
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FRAME-00000012 | 1,775,282,578,012 | 90 | kitchen | 0.594 | {
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FRAME-00000013 | 1,775,282,578,017 | 90 | dining_room | 0.575 | {
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"color_shift": -0.02
} | {
"camera_to_base_transform": {
"position": [
0.156,
0.079,
1.366
],
"rotation_quat": [
0,
0,
0,
1
]
}
} | {
"base_pose": {
"x": 1.281,
"y": -0.971,
"yaw_deg": 360
},
"joint_angles_deg": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"joint_velocities": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"end_effector_force_N": [
0,
0,
0,
0,
0,
0
]
} | {
"rgb_resolution": "1280x720",
"depth_resolution": "640x480",
"segmentation_mask_count": 24,
"pixel_perfect": true
} | [
{
"object_id": "adult_01",
"class": "adult",
"class_id": 2,
"pose_6dof": {
"position": [
0.885,
0.597,
0.295
],
"rotation_quat": [
0,
0,
0,
1
]
},
"velocity": [
0,
0,
0
],
"mass_kg": 1.3... | {
"6dof_poses": true,
"contact_points": true,
"velocity_vectors": true,
"semantic_segmentation": true,
"grasp_labels": true
} | [
"reflective_surface"
] |
FRAME-00000027 | 1,775,282,578,074 | 60 | bedroom | 0.346 | {
"lighting_lux": 937.6,
"texture_variation": 0.584,
"color_shift": 0.126
} | {
"camera_to_base_transform": {
"position": [
0.138,
0.077,
1.481
],
"rotation_quat": [
0,
0,
0,
1
]
}
} | {
"base_pose": {
"x": 1.101,
"y": -0.834,
"yaw_deg": 0
},
"joint_angles_deg": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"joint_velocities": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"end_effector_force_N": [
0,
0,
0,
0,
0,
0
]
} | {
"rgb_resolution": "1280x720",
"depth_resolution": "640x480",
"segmentation_mask_count": 29,
"pixel_perfect": true
} | [
{
"object_id": "knife_01",
"class": "knife",
"class_id": 12,
"pose_6dof": {
"position": [
1.181,
0.391,
0.86
],
"rotation_quat": [
0,
0,
0,
1
]
},
"velocity": [
0,
0,
0
],
"mass_kg": 5.5... | {
"6dof_poses": true,
"contact_points": true,
"velocity_vectors": true,
"semantic_segmentation": true,
"grasp_labels": true
} | [
"motion_blur"
] |
FRAME-00000028 | 1,775,282,578,076 | 90 | bedroom | 0.413 | {
"lighting_lux": 422.3,
"texture_variation": 0.408,
"color_shift": 0.048
} | {
"camera_to_base_transform": {
"position": [
0.141,
0.084,
1.525
],
"rotation_quat": [
0,
0,
0,
1
]
}
} | {
"base_pose": {
"x": 1.211,
"y": -1.051,
"yaw_deg": 360
},
"joint_angles_deg": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"joint_velocities": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"end_effector_force_N": [
0,
0,
0,
0,
0,
0
]
} | {
"rgb_resolution": "1280x720",
"depth_resolution": "640x480",
"segmentation_mask_count": 29,
"pixel_perfect": true
} | [
{
"object_id": "book_01",
"class": "book",
"class_id": 11,
"pose_6dof": {
"position": [
0.475,
0.412,
0.704
],
"rotation_quat": [
0,
0,
0,
1
]
},
"velocity": [
0,
0,
0
],
"mass_kg": 0.87... | {
"6dof_poses": true,
"contact_points": true,
"velocity_vectors": true,
"semantic_segmentation": true,
"grasp_labels": true
} | [
"motion_blur"
] |
End of preview. Expand in Data Studio
🤖 Anode AI: Humanoid Kinetic Fleet (v1.0)
High-Fidelity Synthetic Tensors for Next-Gen Humanoid Perception & Control.
Anode AI’s Humanoid Kinetic Fleet is a mathematically deterministic synthetic dataset designed to bridge the Sim2Real gap for domestic and industrial humanoid robotics. Unlike standard computer vision datasets, this collection includes full 6-DoF ground truth, kinematic torque vectors, and Gaussian stochastic noise modeled on real-world 24GHz radar and LiDAR interference.
📊 Dataset Summary
- Total Records: 1,240,000+ Frames
- Format:
.jsonl.gz(Compressed JSON Lines) - Capture Rate: 90Hz (Temporal Coherence)
- Domain: Domestic Environments (Kitchen, Living Room, Dining)
- Physics Engine: Anode Mud Engine v2.1 (Euler Integration)
🛠 Data Structure & Schema
Each record contains a multi-modal snapshot of the robot's state and its environment.
1. Robot Kinematics
- 6-DoF Pose: Precise [x, y, z] and Quaternions for the base and end-effectors.
- Joint Dynamics: 18-axis joint angles and velocities.
- Force Feedback: Torque vectors (Nm) and gripper pressure (N).
2. Semantic Intelligence
- Object Metadata: Includes
mass_kgandkinetic_energy_jfor interaction logic. - Intent Prediction: Behavioral labels for dynamic entities (e.g.,
Child_5yo_Running). - Threat Vectors: Closing speeds and potential impact time calculations.
3. Sensor Fidelity (Stochastic Layer)
- Gaussian Noise: Modeled via Box-Muller transforms to simulate sensor jitter.
- Domain Randomization: Variable lighting (Lux), texture shifts, and color variations.
🔬 Technical Specifications
| Parameter | Specification | Logic |
|---|---|---|
| Noise Model | Gaussian (Box-Muller) | Sustainable Real-World Noise |
| Physics Integration | Euler (dt=0.1s) | Kinematic Continuity |
| Integrity Check | SHA-256 | Cryptographic Data Provenance |
| Coordinate System | RHS (Right-Handed) | Standard Robotics Convention |
🚀 Usage
This dataset is optimized for:
- Reinforcement Learning (RL): Training humanoids for object manipulation using mass/torque metadata.
- Edge-Case Detection: Testing model failure points in low-light/high-clutter scenarios.
- Sensor Fusion: Aligning 24GHz Radar returns with LiDAR point clouds.
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