Egocentric POV Data for Robotics That Trains From the Operator Viewpoint
TLDR: If your robot works next to people, train it from the operator viewpoint. posterior.xyz captures head and chest mounted task video in real consented environments, synced with depth and motion, and delivers annotated sessions in your export formats. Request a pilot
A security camera sees posture and room layout. It does not see what the hands see.
That gap kinda kills manipulation. Gaze direction, hand contact, fine tool movement, the exact moment an object changes state. All of that lives in the first person view. So we collect egocentric task data for robotics teams that want perception and action models trained from where the work actually happens.
Why the operator viewpoint wins
Third person footage is useful for tracking people across a room. It falls short when you need to predict a grasp or verify a placement.
Egocentric video keeps hands, tools, and the immediate work surface in frame through the full task flow. You get approach through completion from the mover perspective, with occlusions and clutter intact. Your models learn the view your system will share with human operators, not a clean corner angle that will never exist at deployment.
Task lists drive what we capture
You bring the task list. We capture exactly that, from head mounted or chest mounted viewpoints matched to your deployment.
Coverage often includes:
- Assembly, packing, sorting, and inspection steps with visible hand contact
- Cooking, cleaning, laundry, and domestic tool use with object state changes
- Workshop, retail backroom, kitchen, and outdoor work across varied lighting
- Navigation segments that show approach, search behavior, repositioning, and placement
Framing follows privacy by framing protocols you approve. That can mean hands only views and face out angles where dialogue or bystanders are involved. Scripted fictional speech covers cases where you need dialogue without collecting real personal conversation.
Real consented environments with documented context
Lab only data misses reflections, clutter, interruptions, and worn surfaces. We collect in real homes, workshops, kitchens, retail backrooms, and outdoor work areas, all consented and controlled.
Each session documents layout characteristics, lighting conditions, surface types, and object sets. You can slice performance by context because the context was written down at capture time.
Video is captured in HD to 4K to preserve fine manipulation detail. Where helpful, we add stills alongside video for object and surface categories relevant to your task.
Synced depth and motion with documented calibration
Video alone leaves depth ambiguous. We sync egocentric video with spatial and movement streams so you can connect pixels to structure and motion.
| Stream | What you get | How it arrives |
|---|---|---|
| Egocentric video | HD to 4K head or chest view with hands and work surface centered | Time stamped frames in your container |
| Depth and LiDAR | Frame aligned depth plus LiDAR where your stack uses it | Aligned to video with per session calibration |
| Motion | Head and body movement through task phases | Synced signals with documented rates |
| Extras on request | Sensors and radar for multimodal stacks | Same clock, same session folder |
Calibration and synchronization are documented per session. Your team can fuse streams without reverse engineering alignment. The mix supports grasp analysis, obstacle awareness, placement verification, and task phase understanding from the actor viewpoint.
Delivery that fits your training pipeline
Raw sessions do not help much. Structured sessions do.
We deliver temporal annotations to your taxonomy, including action segments, object tracks, contact events, and state changes where needed. You define the rest before we scale.
- Export formats for video, depth, motion, and calibration notes
- Annotation schemas for actions, objects, states, and phases
- Burned in preview videos for independent review
- Metadata conventions linking sessions, environments, guideline versions, and annotator notes
Vision only or multimodal, you receive aligned data and labels that reflect decisions you made during the pilot.
Frequently asked questions
Can you match our mount position? Yes. Tell us head or chest, lens height, and field of view. We test the framing in the pilot and lock it before scaling.
How do you handle faces and personal information? We use consented environments, approved framing, and scripted speech where dialogue is needed. Sessions are reviewed so incidental personal information stays out of your dataset.
What resolution and sync accuracy do we get? HD to 4K video with frame aligned depth and motion. Per session docs list sensors, rates, offsets, and calibration so you can verify sync yourself.
How do we start? Start with a paid pilot. Both sides evaluate fit before scaling. Request a pilot or Talk to us
Action is the antidote. Send us one task list and we will show you what it looks like from the operator viewpoint.
