Sensor Time Synchronization for Robot Manipulation Data Collection

A few milliseconds can change the meaning of a manipulation sample. A camera may record the final approach to an object while a force-torque or tactile channel has already registered contact. That mismatch breaks the correspondence between observation and action, creating apparent action lag, unreliable contact labels, abnormal force feedback, and training runs that struggle to converge.

The challenge becomes harder when a rig combines 30 or 60 fps cameras with a 200 Hz handheld sensor unit carrying tactile fingertips, a 6-axis force-torque sensor, a gripper encoder, and an IMU. The cameras and the sensor MCU run on independent clocks, while transport, buffering, and operating-system scheduling add variable delay. A single recording-start timestamp cannot describe when every physical sample was taken.

A robust design separates synchronization into two layers. First, all cameras operate in slave-trigger mode and receive the same PWM edge, locking their physical exposure events together. In EGO R9 multi-camera configurations, this shared-trigger approach makes synchronized views of a hand-object interaction available for reconstruction and robot-learning datasets without relying on simultaneous software start commands.

Exposure timing still has to fit inside the trigger period. At 60 fps, one cycle is 16.67 ms; keeping exposure below roughly 14.2 ms leaves room for sensor readout and helps avoid dropped or unstable frames. Cameras may be launched sequentially to reduce Jetson ISP and NVMM contention. This changes their starting frame indices, but shared trigger edges continue to lock corresponding exposures.

The second layer builds a common post-processing timeline across three clock domains: the PWM hardware clock that triggers exposure, Linux host wall time, and the MCU crystal clock that stamps handheld sensor samples. Video presentation timestamps and host capture timestamps establish the camera side of the mapping, while paired host-arrival and MCU timestamps estimate how the sensor clock relates to the same host reference.

Proprioceptive acquisition should preserve the native stream rather than take occasional snapshots. Polling the SDK faster than the sensor rate—for example at 500 Hz—and accepting only samples with new MCU timestamps retains the complete force, tactile, encoder, and IMU series. This also prevents duplicated host reads from being mistaken for new physical measurements.

Once both mappings are available, each frame can be associated with nearby sensor samples. Simple nearest-neighbor matching is adequate for slower tasks, but its error is bounded by sensor sampling intervals and host jitter: approximately ±3–5 ms is common at 200 Hz, while a 50 Hz tactile channel can reach ±10–13 ms. Contact-rich tasks benefit from drift-aware clock fitting, interpolation where physically appropriate, and validation against an observable impact or trigger event.

The practical result is one defensible timeline rather than a bundle of files that merely started together. EGO R9's multi-camera time-synchronization capability provides the hardware-locked visual layer; full-rate MCU-stamped sensing and enhanced post-hoc mapping extend that alignment to force, touch, gripper state, and motion. That foundation gives imitation-learning and force-control pipelines cleaner evidence of what the robot saw, when contact occurred, and how the system responded.