Documentation Index
Fetch the complete documentation index at: https://docs.innate.bot/llms.txt
Use this file to discover all available pages before exploring further.
Understanding the dataset format is useful if you want to inspect your recordings, debug training issues, or build custom tooling around the training pipeline.
File structure
Each physical skill lives in a directory under ~/skills/ on the robot:
~/skills/pick_up_cup/
├── metadata.json # Skill config (name, type, execution params)
├── data/
│ ├── episode_0.h5 # First recorded episode
│ ├── episode_1.h5 # Second recorded episode
│ └── ...
└── <run_id>/ # Created after training completes
├── act_policy_step_135000.pth # Trained model checkpoint
└── dataset_stats.pt # Normalization statistics
| Dataset | Shape | Description |
|---|
action | (T, action_dim) | Leader arm commands recorded at each timestep |
qpos | (T, num_joints) | Follower arm joint positions |
qvel | (T, num_joints) | Follower arm joint velocities |
images/main_camera | (T, 480, 640, 3) | Main camera RGB frames |
images/arm_camera | (T, 480, 640, 3) | Wrist camera RGB frames |
T is the number of timesteps in the episode and action_dim is typically 6 (joint positions) or 10 (6 joints + 2 base velocity + 2 reserved).
Additional fields for mobile tasks
When recording with base movement enabled, the episode also includes:
| Dataset | Shape | Description |
|---|
cmd_vel | (T, 2) | Base velocity commands (linear x, angular z) |
odom | (T, ...) | Odometry readings from /odom |
Recording parameters
The recorder captures data at 30 Hz by default with these settings (from recorder.yaml):
| Parameter | Value |
|---|
| Data frequency | 30 Hz |
| Image resolution | 640 × 480 |
| Max timesteps per episode | 1800 (60 seconds at 30 Hz) |
| Camera topics | /mars/main_camera/left/image_raw, /mars/arm/image_raw |
| Arm state topic | /mars/arm/state |
| Leader command topic | /mars/arm/commands |
metadata.json that evolves as you progress through the pipeline:
After creating the skill:
{
"name": "pick_up_cup",
"type": "learned"
}
{
"name": "pick_up_cup",
"type": "learned",
"guidelines": "Use when you need to pick up a cup from the table",
"execution": {
"model_type": "act_policy",
"checkpoint": "run_abc123/act_policy_step_135000.pth",
"stats_file": "run_abc123/dataset_stats.pt",
"action_dim": 10,
"duration": 45.0,
"start_pose": [-0.015, -0.399, 1.456, -1.135, -0.023, 0.833],
"end_pose": []
}
}
execution block tells the BehaviorServer everything it needs to load and run the policy: which checkpoint to use, the action dimensionality, the maximum execution duration, and the arm pose to move to before starting inference.
Normalization statistics
The training pipeline computes per-feature normalization statistics (mean and standard deviation) from your dataset and saves them in dataset_stats.pt. During inference, the policy uses these stats to normalize observations and unnormalize action outputs, ensuring consistency between what the model saw during training and what it sees at runtime.
