Simulation-First Robotics

Learn ROS 2, digital twins and Physical AI foundations with a robotic-arm reference

Understand robot communication, models, motion and virtual validation, then connect those concepts to the M CAD Dexter training platform.

Learn ROS 2, digital twins and Physical AI foundations with a robotic-arm reference
Learning Journey

A progressive route from ROS 2 foundations to virtual validation

The program focuses on understanding how robotic systems communicate, move and behave in simulation.

01

ROS 2 Foundations

Nodes, topics, services, actions, messages, packages and the structure of a robot application.

02

Robot Description

Coordinate frames, links, joints, URDF concepts and visualising a robot model.

03

Motion & Simulation

Commanding mobile and articulated robots in a virtual environment with observable feedback.

04

Digital Twin Workflow

Connect simulation state, control logic and validation evidence into a repeatable engineering process.

M CAD Dexter

Connect the digital workflow to a physical robotic-arm reference

Dexter gives learners a visible reference for links, joints, coordinate frames, commanded motion and the difference between simulated behaviour and physical response.

  • Relate URDF links and joints to real mechanisms
  • Understand command, feedback and joint-state concepts
  • Discuss calibration, limits and simulation-to-real differences
  • Observe how ROS 2 concepts connect to practical hardware

Hardware demonstrations depend on batch scope, lab availability and the current platform configuration.

Yellow M CAD Dexter robotic arm used as a ROS 2 and digital twin training reference
Two M CAD Dexter robotic arms in the engineering lab
Important Scope

Simulation supports physical implementation—it does not replace it

Learners build a strong virtual foundation and understand the interfaces required for later hardware integration. Physical deployment additionally requires robot-specific calibration, safety engineering, networking, drivers and commissioning.

  • Risk-free exploration of robot behaviour
  • Repeatable tests and observable data
  • Clear separation between simulated and physical validation
  • Transferable concepts across robot platforms
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