10 Best Practices for Robotics Data Collection

To succeed at building reliable autonomous robots a company needs to be able to scale their data collection while still having it be precise. Precise meaning it covers the cases you need it to cover while also having enough data that your AI works in the situations it needs to. Below is a list of 10 best practices in data collection. By Siddharth Lunawat

1. Define Clear Data Collection Goals

It sounds simple but you need a well-defined objective tied to specific robotics tasks such as navigation, manipulation, or perception. Identify the learning method (supervised, reinforcement, or imitation learning) and success metrics like accuracy, task success rate, or safety violations. Clear goals prevent wasted data collection and reduce storage and labeling costs.

2. Focus on High-Quality, Task-Relevant Data

In robotics (and any other AI), quality beats quantity. Prioritize your main use cases, then failures and edge cases. Minimize repetitive success data. Target, task relevant data sets to improve model performance and reduce training time.

3. Capture Multimodal and Time-Synchronized Data

Most robots rely on multiple types of sensors working together. If yours is like this then capture data across all formats such as:

- RGB and depth images

- LiDAR or radar

- IMU, encoder, and force/torque data

4. Automate Data Quality Checks

Scalable robotics data pipelines include automated validation to detect sensor failures, missing data, timestamp drift, and corrupted files. Early detection prevents costly retraining and unsafe deployments.

5. Balance Simulation and Real-World Data

Simulated / synthetic data is great for early iteration, but real-world data is very different. You should leverage both. Simulated data can get you a jump started on many use cases then combine that with real world data for achieve true robot mobility.

6. Use Human-in-the-Loop Strategically

Using humans to annotate data is very valuable but can be expensive. Apply human teams selectively and on use cases that are hardest to train on.

7. Version and Document Robotics Datasets

Version control is very important. Many organizations miss version control of data sets and labels. This can cause your AI to use older data that is not as relevant or new data that makes your robot go haywire. Version control makes is easy to add / remove problem data sets.

8. Prioritize Safety, Privacy, and Ethics

Robotics data often includes people and private spaces. Follow best practices for anonymization, consent, and bias detection. Ethical data collection builds trust and supports regulatory compliance.

9. Enable Continuous Data Collection in Deployment

Treat deployed robots as ongoing data sources. Use them to collect data, then feed failures and other learnings into your data sets.

10. Align Data Strategy With System Architecture

Design data collection alongside hardware and software decisions. Ensure data that is collected can reach your systems.

If you are in robotics and looking to get help collecting data we have over 600K monthly active data collectors.