Agentic trajectories → Robotics manipulation
Agentic trajectories for Robotics manipulation
Robotics manipulation needs egocentric and third-person demonstrations with pose and contact detail. Here is why Agentic trajectories is the right raw material for it, what buyers typically spec, and how the rights are handled.
Why Agentic trajectories for Robotics manipulation
Robot policies are trained on demonstrations, and a demonstration is exactly what an agentic trajectory is: goal, observations, actions, outcome, in one aligned record. For physical tasks the capture stack is heavier than screen work — synchronized video (often egocentric plus third-person), hand or end-effector pose, object state, and where possible contact or force signals — but the logic is identical: the value is the alignment. Teleoperated demonstrations map most directly onto robot embodiment; instrumented human demonstrations scale further at the cost of a transfer gap. The content requirements robotics briefs get wrong most often: variation over repetition (same task, many object positions, clutter levels, lighting), retained failures and recoveries (policies must learn what correction looks like), and outcome labels per episode so success-conditioned training is possible without relabelling.
What buyers typically spec
Industry-typical ranges — a brief can and should deviate where the task demands it.
| Typical volume | Hundreds to thousands of episodes per task family; variation across instances specced |
|---|---|
| Capture | Synchronized multi-view/egocentric video, pose or end-effector tracks, object state |
| Labels | Goal per episode, phase segmentation, outcome (success/failure/recovered) |
| Content | Object/position/clutter variation; failures retained deliberately |
| Formats | JSON episode logs, MP4 video, CSV tracks |
A sample brief
The shape of a workable request — swap in your own numbers and conditions:
- Modality: instrumented human demonstrations of tabletop manipulation (pick, place, insert, open).
- Volume: 5,000 episodes across 200+ object configurations.
- Labels: goals, phase boundaries, outcome tags; failures retained at natural rate.
- Rights: demonstrator consent naming commercial AI training.
fiund's sourcing angle
Agentic training data barely exists as a licensable category yet. fiund treats it as a sourcing brief from day one. We source to a brief and clear the rights before anything moves, so what you receive is both useful and defensible in diligence.
Rights posture
Signed licence, explicit training rights, separate voice/likeness consent, nothing scraped. See the rights & provenance guides.
Frequently asked questions
Teleoperated or natural-hand demonstrations?
Teleop maps directly to the robot embodiment and trains policies with minimal transfer work, but collection is slow and rig-bound. Natural-hand demonstrations scale and capture human dexterity, at the price of an embodiment gap. Many pipelines pretrain on human demos and fine-tune on teleop.
How much variation per task is enough?
Enough that the policy cannot memorize geometry: object positions, instances, clutter, and lighting should vary across episodes of the same task. A thousand episodes of one arrangement teach less than two hundred spread across arrangements.
Other data for Robotics manipulation
More Agentic trajectories use cases
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