Multimodal Robotics Data Collection

Multimodal robotics data collection is the capture of multiple, tightly time-aligned sensor streams, RGB-D, LiDAR, IMU, force/torque, and proprioception, with documented calibration so observations and actions correspond exactly. Operant designs and validates the sensor rig, characterizes synchronization, and runs automated drift checks during scaled capture. The result is multimodal data your ML engineers can fuse and trust, not streams that silently drift out of alignment.

Why synchronization matters

When sensor streams are not synchronized, the link between what the robot saw and what it did is corrupted, and models learn from misaligned data. For manipulation, sensor fusion, and any timing-dependent policy, sub-millisecond alignment is the difference between usable and wasted data. Synchronization is a first-class deliverable in every Operant program.

Supported modalities

We capture RGB-D camera arrays, LiDAR, IMU, force/torque, audio, and proprioceptive and control signals, aligned to your exact hardware list. This service underpins teleoperation capture and broader robotics data collection programs.

Calibration and drift checks

Each rig is calibrated, intrinsics and extrinsics, and synchronization is characterized during a pilot. During scaled collection we run automated drift checks and report against agreed tolerances, so problems surface immediately rather than in your training logs weeks later.

File formats and metadata

Deliverables include synchronized logs, calibration files, and scene-level metadata in the formats your stack expects. Episodes carry the identifiers needed to filter, balance, and audit the dataset.

Common failure modes

The usual culprits, unflagged clock drift, uncalibrated extrinsics, dropped frames, and incomplete metadata, are exactly what our QA gates catch. For programs targeting deployment gaps, pair this with sim-to-real data collection; for a concrete multimodal scenario, see AV rain LiDAR and camera capture.