Egocentric Data Collection
For Robotics

What Egocentric Capture Is

Egocentric data collection instruments the human, not the robot. Workers wear head-mounted cameras and wrist-mounted cameras while performing real manipulation work, producing video from the same viewpoints a robot's own cameras would occupy: a global view that moves with attention, and a close view that moves with the hand. The current generation of robot foundation models leans on exactly this vocabulary, egocentric wrist and head streams, because it transfers across embodiments better than fixed third-person views.

The appeal is economics. A human performing a task at natural speed generates demonstrations far faster than any teleoperated robot, with no robot in the loop to schedule, maintain, or crash. For breadth of scenes, objects, and behaviors per dollar, nothing else comes close.

What It Trains Well

• Visual representations. Encoders pretrained on egocentric manipulation video consistently transfer to downstream policy learning, and human video is the richest source of manipulation-relevant pixels available.
• Affordances and priors. Where objects are grasped, in what order a task unfolds, what hand-object configurations precede success. These priors shrink how much on-robot data the final policy needs.
• Subgoal and language grounding. Narrated or task-scripted egocentric video links language instructions to visual state sequences, which vision-language-action models exploit directly.

The Catch: No Actions Without Instrumentation

Raw human video has no action labels. There is no gripper command stream to imitate, only pixels of fingers, and human hands do things no parallel-jaw gripper can reproduce. Bare egocentric video therefore feeds pretraining and priors, not behavior cloning on its own.

The fix is instrumentation. When the human works through a handheld instrumented gripper instead of bare hands, the same session yields egocentric video plus a recoverable end-effector trajectory and gripper state, which is trainable demonstration data. That combination is its own method with its own trade-offs, covered on our UMI-style capture page. Most corpora we build for buyers mix the two deliberately: broad bare-hand egocentric video for representation learning, instrumented capture for the action-labeled core.

Protocol Is What You Are Actually Buying

The gap between a useful egocentric corpus and a hard drive of shaky video is protocol. Ours is boring on purpose:

• Fixed, calibrated mounts. Camera positions on head and wrist are standardized and recorded, with intrinsics per device. Random mounting turns viewpoint into an uncontrolled variable.
• Scripted variation. Object sets, layouts, lighting, and task order vary by plan, not by operator mood, so coverage matches the spec instead of clustering around convenient defaults.
• Task and stage labels. Every clip is segmented and labeled against the task taxonomy in the spec, which is what makes the corpus searchable and trainable rather than an archive.
• Per-clip QA. Exposure, blur, framing, and completeness are checked clip by clip. Field-of-view drift, where the action wanders out of frame, is the leading cause of scrapped egocentric footage, so framing is checked live during capture rather than discovered afterward.

Collection runs in our purpose-built capture studios in Asia Pacific, where mounts, lighting, and object libraries stay controlled between sessions. Studio control is what lets two collection days six weeks apart produce statistically compatible data.

What You Receive

An egocentric delivery is more than video files. Each package includes the synchronized wrist and head streams with per-device intrinsics, task and stage annotations against the agreed taxonomy, per-clip QA results, and a data card recording mounts, devices, lighting setups, and the operator protocol. Where the spec includes instrumented capture, recovered trajectories and gripper state ship alongside the video in your training format. Delivery is LeRobot, RLDS, or GR00T compatible, the same as every collection we run, and the trade-offs between those formats are laid out in our format guide.

One planning note from experience: decide the downstream use before the capture, not after. A corpus destined for encoder pretraining can trade label density for volume. A corpus meant to ground language-conditioned subgoals needs dense stage annotation from day one, because retrofitting labels onto tens of thousands of clips costs more than collecting them correctly the first time. This is a one-line decision in the spec that changes the entire protocol, which is why the spec conversation comes first.