Physical AI & Robotics Data Annotation
Robotics & Physical AI Data Annotation for Embodied AI
Physical AI annotation — also called robotics data annotation — is the labeling of multimodal, time-synchronized sensor data (egocentric video, LiDAR, depth, IMU and audio) that teaches robots and embodied AI to perceive, reason and act. Shaip delivers vision-language-action (VLA), egocentric, manipulation & grasping, activity recognition and safety annotation across six domains with experienced domain experts — not generic crowd work.
What is Physical AI annotation?
Physical AI annotation — the robotics data annotation behind embodied systems — is the labeling of multi-sensor, temporal data (synchronized video, LiDAR, depth, IMU and audio) that teaches robots and embodied agents to act in the real world. It spans VLA annotation, egocentric data annotation, manipulation & grasping, activity recognition, object detection and safety labeling.
It’s not image annotation: the data is 30-fps and multimodal, the labels need real domain expertise, and the safety labels decide whether a robot model is safe to deploy — which is why robotics and Physical AI teams treat annotation as core training infrastructure, not a commodity task.
Robotics & Physical AI annotation services across 6 domains
From VLA annotation and egocentric data annotation to manipulation & grasping, activity recognition and safety labeling — robotics data annotation delivered by domain experts, one dedicated team per domain.
VLA annotation — aligning what the robot sees, hears and does
Vision-language-action annotation pairs natural-language commands with the exact video segments and objects they refer to, so every word maps to something observable.
Object detection & recognition annotation — what the robot sees
Frame-level object labeling built for cluttered, dynamic scenes — not single static images.
Activity recognition annotation — understand and anticipate behaviour
Label what a human is doing now and the early cues that signal what happens next.
Egocentric data annotation — Shaip's most active programme
First-person video from VR headsets and head-mounted cameras — the richest source of human demonstration data.
Manipulation & grasping annotation — teaching robots to use their hands
Label the full grasp, the approach, and every failure — so models learn to recover, not just to succeed.
Safety & anomaly annotation — the release blocker you can't skip
Safety labels decide whether a model deploys. Annotators are trained to find what could go wrong.
