Two ways to record a demonstration
A human demonstration captures a person performing a task with their own body. It provides natural perception and manipulation signal expressed in human embodiment rather than a robot's control interface. A teleoperation episode captures a task through the robot itself, so the recorded actions are already in the robot's action space.
Both are demonstrations; the difference is embodiment. That difference determines how directly the data can be used and what has to be bridged before a policy can learn from it.
Human-manipulation recordings
Datoric's multimodal human-manipulation program records synchronized egocentric and external video with RGB-D/depth, motion capture, IMU, and hand and body pose. Action segments, object and tool labels, hand-object contact, task phases, and completion states are annotated, and calibration and sensor-alignment QA keep the streams aligned.
Depth and pose make human-manipulation recordings useful for fine-grained manipulation, perception pretraining, and world-model work. The motion remains human, so a downstream robot policy still has to map from human hands to its own actuators.
Robot-teleoperation episodes
The teleoperation program records episodes performed through a robot: multi-camera and wrist-camera video paired with joint states, end-effector pose, gripper state, action commands, and trajectory logs, plus object, phase, and success/failure labels and QA reports.
Because the actions are already in the robot's action space, teleoperation data provides direct supervision for imitation learning and VLA policies.
Choosing between them
A practical rule of thumb
- Use human-manipulation data for scale, perception pretraining, depth and pose supervision, and world-model training.
- Use teleoperation data for imitation learning and VLA policies that consume actions in the robot's own action space.
- Combine them when you want broad perception coverage from human demonstrations plus embodiment-aligned fine-tuning from teleoperation.
Labels are shared, action spaces are not
Both specifications include object and tool labels, action or phase segmentation, and completion or success/failure outcomes. What differs is the action representation: human pose and contact on one side, joint states and action commands on the other. The model's expected action space is the central selection question.
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