CogmeltEgocentric
First-person human task demonstrations with visible hands, tools, objects, and outcomes.
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THE EXPERIENCE LAYER FOR PHYSICAL AI
Every robot failurereveals a missingexperience.
Cogmelt turns robot capability gaps into targeted, rights-cleared multimodal demonstrations—then validates whether the resulting data improves performance.
Tens of thousandsof hours of real-world task experience
EgocentricMultiviewTactileTeleoperationMission-defined
See the world robots
still struggle to understand.
▶WATCH THE FIELD REELOPEN EXPERIENCE LIBRARY ↗
THE GAPBenchmarks reward the expected.
Benchmarks reward the expected.
The physical world does not.
Robots fail in the long tail: unfamiliar objects, deformable materials, subtle contact, occlusion, imperfect tools, human variation, and recovery after mistakes.
Cogmelt turns those failures into a production specification—not another generic data dump.
THE COGMELT CLOSED LOOPFrom robot failure
From robot failure
to measurable progress.
01
Diagnose
Identify the missing experience behind the failure.
02
Specify
Define the task, environments, signals, variations, and success criteria.
03
Produce
Launch targeted real-world collection missions through Cogmelt.
04
Validate
Check quality, rights, safety, structure, and task completion.
05
Measure
Evaluate whether the resulting data improves robot performance.
ONE OPERATING SYSTEM · MANY SIGNALSEvery modality physical
Every modality physical
intelligence demands.
Programs can begin with one capture type or combine synchronized signals around the same task, environment, action, and outcome.
Multiview
Synchronized first-person, third-person, spatial, and environment perspectives.
↗Tactile
Force, pressure, contact, grasp-state, and manipulation signals.
↗Teleoperation
Remote demonstrations, corrections, intervention, and operator intent.
↗Motion + pose
Body, hand, object, tool, and camera trajectories aligned to task events.
↗Audio + language
Spoken intent, task narration, acoustic events, and language grounding.
↗Robot evaluation
Failure, recovery, success, robustness, and performance evidence.
↗Environment state
Scene, object, workflow, equipment, and outcome metadata.
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RIGHTS-CLEARED · TRACEABLE · PILOT-READYNot collected because it exists.
Explore sample collections ↗
Not collected because it exists.
Produced because your model needs it.
Explore sample collections ↗Data products built around the decision you need to make.
Training collections
Structured demonstrations organized around skills, variations, environments, and outcomes.
Failure + recovery sets
High-value episodes showing retries, corrections, intervention, and successful recovery.
Evaluation slices
Controlled evidence for benchmarking model behavior and comparing releases.
Custom pilot programs
Buyer-defined collections with mission controls, validation, and delivery packaging.