Learning Roadmap
How to Become a AI Robotics AI Engineer
A step-by-step, phase-based learning path from beginner to job-ready AI Robotics AI Engineer. Estimated completion: 9 months across 3 phases.
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Foundations: Robotics & Core Programming
8 weeksGoals
- Master Python and C++ for systems programming.
- Understand core robotics concepts: kinematics, dynamics, sensors, and actuators.
- Become proficient in ROS2 architecture, nodes, topics, services, and packages.
- Set up a basic simulation environment in Gazebo.
Resources
- 'Robotics: Perception' & 'Robotics: Estimation and Learning' on Coursera (UPenn)
- ROS2 Official Tutorials
- Python for Robotics (roboticsbackend.com)
- Hands-on with a turtlebot3 simulation.
MilestoneCan build a simulated mobile robot that navigates a simple environment using pre-built ROS2 navigation stack packages.
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Core AI & Perception for Robotics
12 weeksGoals
- Learn fundamental ML/DL and computer vision.
- Implement perception pipelines: object detection (YOLO), semantic segmentation, and depth estimation.
- Understand sensor fusion principles (camera-LiDAR).
- Introduction to SLAM and localization algorithms.
Resources
- Andrew Ng's Machine Learning Specialization
- Deep Learning Specialization (deeplearning.ai)
- OpenCV documentation and tutorials
- Udacity's Sensor Fusion Nanodegree
- Papers: PointNet, RT-DETR.
MilestoneCan train a custom object detection model and deploy it on a ROS2 node to process simulated camera feeds in real-time.
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Advanced AI, Simulation & Deployment
16 weeksGoals
- Dive into RL and imitation learning for decision-making.
- Master Sim2Real techniques using NVIDIA Isaac Sim or Gazebo.
- Learn to optimize and deploy models on edge devices (TensorRT).
- Explore Generative AI applications: using VLMs for scene understanding and LLMs for task decomposition.
Resources
- NVIDIA Isaac Sim documentation and examples
- Spinning Up in Deep RL (OpenAI)
- Coursera 'Generative AI with LLMs'
- Papers: SayCan, RT-2, VoxPoser
- TensorRT and DeepStream SDK tutorials
MilestoneCan design and deploy a full-stack AI system on a simulated robot that performs a complex pick-and-place task using a combination of perception, a fine-tuned VLM, and a motion planner.
Practice Projects
Apply your skills with hands-on projects. Ordered by difficulty.
ROS2-Powered Mobile Robot Navigation
BeginnerBuild a simulated mobile robot (e.g., TurtleBot3) in Gazebo that uses a pre-built navigation stack (Nav2) to autonomously navigate a known map. Integrate a simple object detection model to stop at specific objects.
Autonomous Drone Landing on a Moving Platform
IntermediateIn simulation, create a drone that uses a camera feed to detect and track a specific landing pad (ArUco marker or custom shape) on a moving vehicle, then execute a precision landing using a control loop informed by the perception output.
Sim2Real Pick-and-Place with Domain Randomization
IntermediateTrain a reinforcement learning or imitation learning policy in NVIDIA Isaac Sim for a robotic arm to pick up random objects from a bin. Use domain randomization on textures, lighting, and object poses to create a policy that works on a real or photorealistic simulated arm.
Human-Robot Interaction Assistant
AdvancedBuild a system where a stationary robot arm uses a Visual Language Model (VLM) to understand natural language commands (e.g., 'Hand me the tool to the left of the red box'). The system must ground the language in the visual scene and plan a safe manipulation trajectory.
Multi-Robot Warehouse Simulation
AdvancedDesign a simulation of a small warehouse with 3-5 AMRs (Autonomous Mobile Robots) that use a combination of SLAM, collision avoidance, and a central task dispatcher to efficiently retrieve and deliver items. Focus on system architecture and inter-robot communication.
Ready to Start Your Journey?
Prep for interviews alongside your learning — it reinforces every concept.