■ Industry Challenge Identification

This approach identifies real-world challenges directly from industrial, competitive, and engineering settings, emphasizing practical, real-life problems rather than virtual or simulated exercises.

By structuring real-world challenges across industries into a database of over 100 cases, it provides an environment that enables a clear, practical understanding of real robotic application scenarios.

■ Task Scenario Analysis

Decompose problems into executable tasks while clearly defining objectives, constraints, and expected deliverables.

By extracting common elements from real-world challenges, the system organizes them into 10 generalized task scenarios, where learners conduct task analysis and define functional requirements based on each scenario.

■ Learning Challenge Design

Identify and structure the knowledge, skills, and tools required for each task into organized learning components.

A structured, progressive engineering knowledge base is built, covering robotics hardware, perception, control, algorithms, and intelligent system design. Learners design learning paths based on tasks and gradually acquire knowledge in a step-by-step manner.

 

■ Modular Solution Development

It provides a suite of implementation tools that support zero-to-one validation based on the knowledge base.

Standardized hardware and software modules can be combined in a block-based approach to rapidly build solutions, lowering development barriers and enabling users to focus on system design.

 

■ Consolidation of Real-World Problem-Solving Skills

Learners work through small-scale projects to gain hands-on experience across the full workflow, from problem identification to implementation.

By going through implementation, validation, and improvement cycles, learners develop the following integrated capabilities:
Product Thinking / Engineering Thinking / System Design Skills / Hands-on Experience