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INDUSTRY · ROBOTICS

Full-Stack Physical AI Infrastructure for Robotics

From perception-decision-motion architecture to on-device LLM deployment, real-time communication and production certification: one technology foundation supports the rapid productionization of embodied AI, AMR / AGV and collaborative robots.

Industry Challenges

The engineering gap between demo and production

CHALLENGE 01

Diverse Form Factors, Isolated R&D

Humanoid, wheeled and quadruped platforms are developed in parallel, and perception-decision-motion architectures rarely carry over, stretching new-model development cycles.

CHALLENGE 02

Demos That Never Leave the Lab

Multi-platform adaptation costs are high and production engineering is lacking, so the same bench and field issues consume engineering effort again and again.

CHALLENGE 03

LLMs on Robots

VLA / LLM workloads lack a dedicated on-device runtime, AI compute competes with real-time control, and power and thermal budgets are hard to balance.

CHALLENGE 04

High Safety & Compliance Bars

Collaborative-safety standards (ISO 10218 / ISO 3691-4) and CE / UL market access put robots with insufficient controller safety levels out of reach for overseas markets.

How We Help

01

One Architecture, Many Bodies

Layered perception-decision-motion architecture with real-time communication (DDS) and cross-body platform planning, dramatically shortening new-model development.

02

On-Device Embodied AI

LLMs and real-time control isolated: RQX-71G runs VLA / VLM locally while Robo3588 handles real-time control, closing the full loop on-device.

03

Highly Dynamic Motion

Deterministic scheduling with high-precision motor and joint synchronization over EtherCAT keeps running and jumping motions stable.

04

Certification & Global Markets

Full IEC 61508 / ISO 13849 / ISO 10218 / IEC 62443 implementation: certify once and reuse across markets, cutting certification time by 30%+.

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