How It Works

We define clear task goals, start and end states, constraints, and failure conditions.
Every task is specified to be reproducible, measurable, and aligned with downstream learning objectives.

We design repeatable, operator-consistent data collection protocols.
This ensures demonstrations and executions are comparable across operators, robots, and environments.

We capture human, robot, or factory-based execution data with multimodal sensing.
This includes vision, proprioception, control signals, and interaction events recorded under real-world conditions.

We validate data through consistency checks, task-level filtering, and failure-mode analysis.
Low-signal, ambiguous, or non-representative samples are systematically removed.

We structure and package outputs for downstream training and evaluation.
Datasets are delivered in formats optimized for imitation learning, reinforcement learning, and policy iteration.