Data acquisition is the process of gathering raw signals and measurements from sensors and the environment, then conditioning and recording them for downstream use in perception, learning, and control. In robotics it spans sampling sensor streams, time-synchronising heterogeneous sources, and logging structured datasets for training and analysis. High-quality acquisition is foundational because the fidelity of perception and learned policies depends on the data captured.
Overview
- Data acquisition turns the physical world into structured signals that robots and learning systems can reason over. It covers sampling rates, calibration, time-synchronisation across sensors, and reliable logging under real-time constraints. Teleoperation and demonstration provide a rich source of acquisition data for imitation learning.
Mechanisms
- Sampling and conditioning of raw sensor signals from cameras, LiDAR, and IMUs
- Time-synchronisation of heterogeneous, asynchronous sensor streams
- Calibration to map raw measurements into consistent reference frames
- Structured logging into datasets suitable for training and replay
- Teleoperated demonstrations captured as supervision for policy learning
Applications
- Building training datasets for robot perception and manipulation
- Capturing demonstrations for imitation and behaviour learning
- Feeding real-time sensor fusion and state estimation pipelines
- Logging telemetry for diagnostics, replay, and offline analysis