Robot data collection SDK for teleoperation and embodied AI

Build a usable demonstration data pipeline

The SDK evaluation covers device output, robot state synchronization, task labels, data formats, and the engineering effort required to move from a live teleoperation demo to repeatable training data.

Capabilities

• Hand pose and joint-state streams
• Timestamped recording and replay
• Data export for training and analysis
• ROS2 workflow and controller integration evaluation
• Windows, macOS, and Linux development support

Use cases

Imitation learning datasets, dexterous manipulation research, robot control validation, multimodal synchronization, and experiment replay.

FAQ

Can the SDK connect to ROS2?

ROS2 integration can be evaluated against your robot controller, message definitions, and timing requirements.

Data quality requirements

Training data is useful only when each stream can be interpreted and aligned. A practical implementation should define coordinate frames, units, sampling rates, timestamp sources, calibration records, task boundaries, and failure labels before large-scale collection begins. Camera frames, operator motion, robot state, and control commands should use a shared clock or a documented post-processing alignment method.

Integration checklist

• Confirm device SDK versions and supported operating systems
• Define ROS2 messages or controller input formats
• Choose recording, export, replay, and storage conventions
• Document calibration and motion-mapping parameters
• Test dropped-frame, disconnect, and recovery behavior

From prototype to repeatable collection

Early prototypes often focus on making a robot respond to live motion. A production data workflow also needs session metadata, operator identifiers, task definitions, episode boundaries, quality checks, and repeatable export. Evaluating these requirements early reduces rework when a demonstration setup grows into a larger embodied AI dataset.

Can existing robot logs be synchronized?

Yes, provided the robot logs expose reliable timestamps or another alignment signal. The exact synchronization method depends on the controller, camera pipeline, and data recorder used by the project.