🤖 Robotics · Data Flow
Robot Learning Data Pipeline
How data from real robots and simulation is collected, labelled and used to train and deploy new robot models.
Drawing diagram…
What this diagram shows
- Real-world failures are the most valuable training data
- Simulation creates edge cases that are rare in reality
- Every model is evaluated before it reaches robots
Prompt used
Robot learning data flow: robots upload camera, lidar and log data from missions, especially failures. Data is stored in a data lake, selected and labelled. A simulator generates synthetic scenarios. The training pipeline trains perception and grasping models, an evaluation step tests them in simulation and on a test robot, and approved models are deployed to the fleet.
Mermaid code
flowchart LR
subgraph EXT["External Entities"]
ROB[Robot Fleet]
LAB[Labelling Team]
end
subgraph PROC["Processes"]
P1[1. Upload mission data]
P2[2. Select interesting episodes]
P3[3. Label data]
P4[4. Generate simulation scenarios]
P5[5. Train models]
P6[6. Evaluate and approve]
end
subgraph STORE["Data Stores"]
D1[("Data Lake (S3)")]
D2[("Labelled Datasets")]
D3[("Model Registry")]
end
ROB -->|"1. Camera, lidar, logs"| P1 --> D1
D1 --> P2 --> P3
LAB -->|"2. Labels"| P3
P3 --> D2
P4 -->|"3. Synthetic data"| D2
D2 --> P5 --> D3
D3 --> P6
P6 -->|"4. Approved model"| ROBRelated templates
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