AI for Robotics

Rainbow Robotics integrates AI based on its robot control technology to implement intelligent robotic platforms with enhanced autonomy, adaptability, and safety

Why Physical AI

Existing automation is optimized for repetitive execution under predefined conditions.
However, real industrial environments are dynamic and unpredictable.
Environments are constantly changing, and processes are becoming increasingly complex.
Robots must now evolve from simple executors into intelligent physical systems (Physical AI) that perceive their surroundings, make decisions, and respond autonomously.

Rainbow Robotics is integrating AI-based autonomous capabilities so that robots can perceive their environment, make decisions, and respond on their own.

Physical AI Implementation Strategy

Rainbow Robotics is expanding its technology capabilities around two core pillars with the ultimate goal of realizing Physical AI.

01 VLA Infrastructure Development

Rainbow Robotics tracks advances in VLA (Vision-Language-Action) technology and builds the operational infrastructure needed to apply VLA models in real robot environments.

  • Robot multimodal data collection
  • VLA-ready data structure and management
  • Fine-tuning and inference pipeline optimization
  • Sim-to-real learning and validation

02 Reinforcement Learning & Control-Centric Physical Intelligence

Based on deep model-based control expertise and field application experience, Rainbow Robotics applies reinforcement learning to dynamic locomotion, contact-rich manipulation, and physical interaction optimization.

Model-based control provides stability, predictability, and industrial reliability, while reinforcement learning improves adaptability, nonlinear dynamics optimization, and high-difficulty dynamic behavior learning.

Example: the RBQ quadruped walking robot implements reinforcement-learning-based dynamic locomotion and secures industrial-grade stability through a seamless switching structure between model-based control and RL policies.

Toward Integrated Physical AI

Rainbow Robotics hierarchically integrates VLA-based high-level intelligence and RL-based physical policy with model-based control and safety technologies,
We implement Hybrid Physical AI that connects intelligent decision-making to reliable physical execution.

Core AI Framework

01 Vision-Language-Action Framework

Delivers a Foundation Model-based VLA solution that connects high-level semantic understanding with low-level physical control

  • Supports various telemotion operations for data collection
  • Provides simulation of the Issac Sim/Mujoco environment
  • Supports a learning pipeline linked to various Foundation Models and native frameworks
  • Providing an End-to-End VLA R&D environment

02 Skill Learning & Reinforcement Learning

Through powerful dynamics-based control and reinforcement learning, it learns a wide range of robot skills while simultaneously achieving stable physical execution and learning-based adaptability

  • Model-based control
  • Reinforcement learning integration & Skill Learning
  • Real-Time Policy Switching
  • Sim-to-Real Optimization

03 Industrial-Grade Safe Autonomy

Based on proven industrial collaborative robot and mobile robot technologies, we provide an Industrial-Grade Autonomous Robot platform that ensures the safety and reliability of Physical AI

  • Based on verified drive, control, and safety technologies for collaborative and mobile robots that comply with industrial safety standards such as SO 10218-1, ISO 13849-1, and ISO 3691-4, it enables the realization of highly reliable autonomous robots
  • Next-generation mobile humanoid RB-Y1C and RBY2 pursue industrial safety certification
  • Research in Safety-aware Motion & Operation and Safety-constrained Policy Learning

AI for Robotics

Rainbow Robotics' Hybrid Physical AI strategy extends into manufacturing, mobility, collaboration, and general-purpose task domains.

Autonomous Manufacturing

  • Process automation

Integrates multimodal process data such as vision, torque, and position to assess work status in real time and automatically adjust robot motion and process conditions through Hybrid Intelligent Control.

This enables autonomous optimization that learns and corrects process variation beyond simple repetitive execution.

Autonomous Mobility and Collaboration

  • Mobility and collaboration optimization

Combines simulation-based learning with real-world data to autonomously optimize mobility, collaboration, and workflow across industrial environments.

This makes stable autonomous operation possible even in complex field conditions.

General-Purpose Task Expansion

  • Skill-based intelligence platform

Structures work as reusable skills rather than isolated motions, allowing robots to combine and expand tasks across changing environments.

Through this approach, robots evolve from fixed process equipment into general-purpose Physical AI platforms.

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