INSIGHTS
Selection & capture notes
Short notes on embodied AI data capture, UMI gripper, FMG armbands, and robot-learning data delivery. Marketing overview — confirm details with our team.
Embodied AI data capture: start from the task
Embodied AI data capture is not about stacking sensors. Define the task, valid samples, and delivery format first; then choose egocentric vision, grippers, gloves, or armbands — and plan calibration and QA.
Read UMI GRIPPERUMI gripper teaching: how GB / GT fit capture
UMI-gripper records human manipulation with a parallel gripper. GB covers video, inertial, and jaw width; GT adds fingertip tactile. Write locally first, then archive; capture and deployment stay separate.
Read FMG + EGOFMG armbands and egocentric vision: recording natural bimanual work
FMG + IMU forearm bands record muscle-deformation pressure changes and inertial motion, often paired with head-worn vision. They are not EMG, and not robot action ground truth.
Read DATA PIPELINERobot-learning data: from capture workstation to deliverable datasets
Robot-learning data needs a closed loop of ingest, processing, publish, and evaluation. Workstations enable reproducible experiments; the data platform covers versions, access, and failure recollects.
ReadSTART WITH YOUR TASK
Tell us about your research task.
From device selection to data and training workflows, we'll help you find the right place to start.