Task-Centric Robotics
We don’t “train tasks into a black-box policy.” We write tasks explicitly as readable, tunable, reusable procedures (SOP). A world model + large model then correct key parameters in real time — achieving stable generalization in the real world.
Why the mainstream approach gets stuck
High data dependence
Policy / VLA pipelines often require large-scale teleop or RL data to acquire a single task.
Slow iteration
When the environment changes, you usually can’t “fix one step.” You collect more data and retrain.
Opaque & hard to debug
Behavior is baked into weights. Failures are hard to localize, reproduce, and engineer improvements.
Our approach
Explicit SOP as the task
Tasks are defined as procedures with constraints and parameters — readable, auditable, and reusable.
Runtime parameter correction
The world model + LLM adapt only the local parameters (pose, forces, timing) without rewriting the procedure.
Composable skill memory (ATM)
Over time, SOP fragments become modules you can share, reuse, and combine into new tasks.
Task demos
These are early demonstrations. Our goal is fast iteration, robust deployment, and cross-platform expansion.