Case Studies / Physical AI & Robotics Data
Synchronized Multi-Sensor Episodes for Embodied AI Training
Synchronized sensor data workflows for embodied AI training and validation — teleoperation episodes with RGB-D, force, and kinematics aligned to a common clock.
The outcome
- 5000+
- Validated demonstration episodes
- 9
- Synchronized sensor streams
Client context
A robotics company training manipulation policies for a mobile manipulator needed thousands of demonstration episodes — but their pilot data had unusable timestamp drift between cameras and force sensors.
Challenge
Policy training kept plateauing. Diagnosis traced it to data quality: dropped frames, inconsistent task segmentation between operators, and sensor clocks drifting several hundred milliseconds over long episodes.
Data strategy
We rebuilt the collection protocol end to end: hardware-triggered synchronization, per-session calibration routines, standardized operator scripts with task-phase boundaries, and an automated validation gate that rejected episodes before they reached annotation.
Workflow
- Task & environment design — scenario matrix across objects, layouts, lighting
- Sensor setup & calibration — common-clock triggering, per-session checks
- Operator protocol — scripted variation with consistent phase boundaries
- Collection — monitored sessions with live drop-frame alerts
- Annotation & validation — task-phase and object-state labels, episode-completeness gate
Quality controls
Timestamp alignment verified per episode (<2ms drift), drop-frame monitoring with automatic re-capture, embodiment-consistency checks across operators, and environment diversity tracking against the scenario matrix.
Outcome
5,000+ validated episodes delivered in MCAP with JSONL episode indexes. Post-training success rates on the client's manipulation benchmark improved materially once retrained on the synchronized corpus — and the validation gate became part of their own internal collection standard.
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