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AI+ Robotics Practitioner (AT-420): Exam-Focused Prep
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AI+ Robotics Practitioner (AT-420): Exam-Focused Prep

Vendor-neutral exam-focused prep covering ROS 2, deep learning, RL, generative AI, and robot safety for AT-420
Created byAseem Mankotia
Last updated 10/2026
English

What you'll learn

  • Map sensors, actuators, and the perceive-plan-act loop in a robotic system
  • Distinguish autonomy levels and explain core SLAM and navigation concepts
  • Design a ROS 2 node graph and describe the robotics software stack
  • Apply deep learning (computer vision, sensor fusion) to robot perception
  • Evaluate reinforcement learning and sim-to-real transfer strategies for robots

Course content

2 sections • 12 lectures • 3h 17m total length
  • Robotics and AI Foundations19:08
  • Autonomous Systems and Intelligent Agents16:49
  • AI and Robotics Development Frameworks16:37
  • Deep Learning for Robotics15:40
  • Reinforcement Learning for Robotics18:52
  • Generative AI for Robotic Creativity16:29
  • NLP for Human-Robot Interaction14:18
  • Applied Robotics: Practical Use-Cases13:30
  • Robotic Process Automation (RPA)16:40
  • Emerging Technologies and Future Trends20:19
  • AI Ethics, Safety, and Policy in Robotics16:09
  • Full Exam Simulation and Time-Management Strategy12:34

Requirements

  • Basic familiarity with core AI/ML concepts. Some Python helps for the applied sections but is not required. No physical robotics hardware needed.

Description

This course contains the use of artificial intelligence.


AI+ Robotics Practitioner (AT-420) by AI CERTs sits at the intersection of two fast-growing fields: artificial intelligence and physical robotics. This course, built by instructor Aseem Mankotia, delivers independent, vendor-neutral companion preparation for the AT-420 credential. It is not a replacement for the official AI CERTs training-plus-exam bundle, but a focused way to build the applied knowledge the exam blueprint actually tests. Across twelve chapters you will work through every official AT-420 module, consolidated into nine exam-weighted domains: robotics and AI foundations, autonomous systems and intelligent agents, ROS 2 and simulation frameworks, deep learning and reinforcement learning for robotics, generative AI and NLP for human-robot interaction, applied use-cases and RPA, emerging technologies like humanoids and VLA foundation models, and AI ethics and safety in physical systems.


Every chapter pairs direct, exam-style explanations with a tabletop or architecture lab, with no physical hardware required. You will sketch perception-plan-act loops, design a ROS 2 node graph, plan a sim-to-real reinforcement learning pipeline for robot locomotion, spec an LLM-driven voice-control stack for a robot arm, build a use-case selection matrix for industrial and healthcare robots, and construct a robot-safety risk register. Aseem Mankotia structures each domain around real exam patterns: scenario-based questions, service and robot-type selection decisions, configuration choices, and the common traps that trip up candidates who only know the theory.


The course closes with a full-length practice exam simulation mirroring the real AT-420 format: 50 scenario-based multiple-choice and multiple-response questions, a 90-minute timer, and a required minimum score of 70 percent (35 out of 50), plus a time-management strategy session so you walk into exam day with a clear plan. Whether you are a robotics or mechanical engineer, an AI/ML specialist, a systems integrator, or a student entering the physical-AI field, this course gives you a structured, practical path to master the AT-420 domains alongside the official AI CERTs training.


AI content disclosure: This course was produced with the assistance of artificial intelligence tools. Lecture narration is AI-voice generated, and lecture scripts, slides, and practice questions were drafted with AI assistance, then reviewed and curated by the instructor for technical accuracy and alignment with the official AT-420 exam guide.

Who this course is for:

  • Robotics and mechanical engineers, AI/ML specialists, systems integrators, and students entering the physical-AI field who want structured, vendor-neutral preparation for the AI+ Robotics Practitioner (AT-420) exam.