Robotics & Physical AI Teams

Custom dataset pilots designed around your target policy.

We scope and execute structured human task demonstration projects. We define the camera mounts, environment variations, and ontology parameters to generate dataset packages ready for model training.

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Model Capabilities

Training inputs for physical representation learning.

How custom demonstrations support key perceptual and control models.

VLA-Model Pretraining

Diverse demonstration segments to pretrain Vision-Language-Action policies for task understanding.

Hand-Object Interactions

High-resolution multi-angle logs to model contact points, grasp frames, and finger positioning.

Success & Failure Classification

Explicit recordings of failed task trajectories and recovery loops to train robust world models.

Affordance Learning

Mapping dynamic object surfaces and spatial boundaries to pre-determine potential actions.

Task Decomposition

Breaking complex, multi-stage sequences into clean temporal subtasks labeled sequentially.

Action Anticipation

Predicting next-step action verbs and object targets based on sequential visual histories.

Multiview Perception

Aligning camera feeds from wearable chest setups and workspace mounts to resolve occlusion.

Human-to-Robot Transfer

Gathering structured human kinematics that map cleanly onto target robotic joint configurations.

Operating Lifecycle

The Custom Dataset Pilot workflow.

We move systematically from initial requirements scoping to scaled, geographical collections.

PHASE_01 // DISCOVERY

Requirements Scoping

The customer specifies the model target goals, target environments (apartment mock, logistics zone), required camera mounts (chest rig, workspace static), desired variation depth (object types, illumination), budget parameters, and delivery schedule.

PHASE_02 // DESIGN

Protocol Specification

RoboWorkData engineers map out the exact setup: starting object coordinates, goal validation rules, allowed failure trajectories (for recovery training), camera models and calibration details, background workspace boundaries, and dataset labeling ontology.

PHASE_03 // PILOT

Pilot Collection

A localized pilot batch is captured in our operated mockups. Pilot scope is defined based on task complexity, annotation depth, camera setup, and intended model use. No artificial minimum quantities are required for pilot testing.

PHASE_04 // AUDIT

Customer Review

The customer receives initial sample clips, annotated JSON schemas, a Dataset Card detailing metadata metrics, and a QA verification log. Format validation is confirmed before scaling.

PHASE_05 // SCALE

Scale-Up Execution

Following client clearance, we activate our verified contributor network across international studios or regions. We introduce broader object variants, varying light levels, and distinct environments while enforcing identical QA checklists.

Capability Limitations

Clear suitability boundaries.

We believe B2B dataset transparency is a core engineering requirement. Our human task demonstrations are designed for specific modeling stages:

Suitable for

Pretraining visual-language representation models.
Modeling temporal subtask planning & workflows.
Isolating success and failure markers.
Learning affordance bounds and scene configurations.

Not included by default

Proprioceptive joint positions or motor actions.
End-effector commands or force torque metrics.
Gripper force values or tactile signals.
Direct executable robot policies.

Technical Note: Human demonstration datasets can support pretraining, task understanding, visual representation learning, planning, and human-to-robot transfer. Direct robot control generally requires additional robot-specific state and action data.

Discuss a custom dataset pilot project

Contact our technical studio team to explore camera perspectives, label scopes, and timeline frameworks.

Request a Dataset Pilot