Technology
Understanding the physical world → constrained decision-making → skill reuse → cerebellum-level execution → data feedback. Each layer is industrial-grade, and the safety boundary between AI and actuators is explicit.
Solves: the scene is never the same twice.
Multimodal 2D/3D perception, spatial understanding, and environment modeling build a live representation of racks, trays, containers, and moving people or equipment.
Solves: intent must become an executable, safe plan.
Task understanding, constrained decision-making, and hierarchical planning convert production intent into an ordered task sequence — validated against process, permission, and safety rules before anything moves.
Solves: every SKU change should not mean a new program.
Reusable skill packages carry task workflows and scenario adaptation. A new material changes parameters and scene bindings — not the robot program.
Solves: physical work must be repeatable and safe.
Motion control, force interaction, deterministic control, and safety arbitration execute the plan on industrial hardware. High-level AI requests actions; the deterministic controller decides whether and how they are executed.
Architecture describes deployed system layers. Model size, training data volume, and simulation-to-reality metrics are not published as product claims.
Why Physical AI
| Dimension | Fixed automation | SHANGQU.AI Physical AI |
|---|---|---|
| New material | Re-teach positions and rebuild fixtures | Adjust parameters; perception locates the object |
| Changed layout | Reprogram paths and re-validate the cell | Re-map the scene; planning adapts within constraints |
| Mixed containers | Dedicated gripper per format | Skill selection by detected container class |
| Exceptions | Line stop and manual intervention | Detected, logged, and escalated with context |
| Safety boundary | Hard-wired interlocks | Hard-wired interlocks retained; AI limited to non-safety-critical decisions |
The intelligence layer proposes; the deterministic controller disposes. Speed limits, safety zones, collision protection, and interlocks are enforced below the AI layer, so a planning error degrades into a rejected task — not an unsafe motion.
A skill validated on one container family can be reused on another site after scene calibration. What transfers is the capability and its parameters — not a hard-coded path.
A structured assessment of your material flow, containers, systems, and safety constraints — with a written scope, acceptance criteria, and clear go/no-go.
