Towards Reducing the Barrier to Data Collection

## Towards Reducing the Barrier to Data Collection

We enhanced the data collection pipeline for **LeRobot** by addressing two key limitations: lack of depth data and absence of a real-time simulation model. Our work improves both the robustness of **Vision-Language Agents (VLAs)** and the speed of dataset generation.

### Key Contributions:

- **[Depth Sensor Integration](https://github.com/utn-air/lerobot/tree/depth):**
We incorporated an **Intel RealSense** depth camera into the LeRobot system, enabling asynchronous capture of **RGB-D data** from three perspectives: the gripper camera and two external views. This expands the existing setup—which only supported RGB and text—to include depth, a modality shown in prior research to significantly improve VLA performance.

- **Digital Twin Implementation:**
We developed a **digital twin** that mirrors the physical robot's actions in real time during teleoperation. This provides a simulated view for enhanced monitoring, debugging, and potential offline training in future iterations.

Our system boosts data richness and lays groundwork for more scalable and reliable VLA training and evaluation.

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