Advanced Robotics AI Systems Integration Certification

Week 1 – Deep Learning and Computer Vision in Robotics

Written Lectures:

• Implementing deep learning techniques (CNNs, RNNs) in robotic perception.
• Object detection and segmentation with YOLOv8 and OpenCV.
• Edge AI deployment with TensorFlow Lite on embedded systems.

Labs (VS Code + PlatformIO):

• Develop and train an image classification model for robotic vision.
• Deploy a deep learning model to detect objects via a real-time camera feed.
• Integrate inference results into robotic navigation logic (obstacle detection).

Audio Lectures:

• “The Influence of Deep Learning on Robotic Autonomy” (30 mins).
• “Deploying AI Models on Microcontrollers” (25 mins).

Assessments:

• Matching Quiz: Deep learning models vs. functions (10 questions).
• MCQ Quiz: 40 questions on CNN architecture, object detection, and AI integration.

Week 2 – Reinforcement Learning and Adaptive Robotics

Written Lectures:

• Introduction to reinforcement learning (RL) for robotics control.
• Policy gradient methods and Q-learning explained.
• Teaching robots to adapt through continuous environment feedback.

Labs (VS Code + PlatformIO):

• Create a virtual agent in Python to teach a robotic arm task via RL (OpenAI Gym simulation).
• Apply reward functions to optimize motor control in a simulated environment.
• Port the trained policy model to a real robot using ESP32 hardware simulation.

Audio Lectures:

• “Robots That Learn by Doing” (30 mins).
• “Applications of RL in Autonomous Systems” (20 mins).

Assessments:

• Matching Quiz: RL algorithms vs. control objectives (10 questions).
• MCQ Quiz: 40 questions on policy optimization and convergence.

Week 3 – Internet of Robotic Things (IoRT) and Edge AI

Written Lectures:

• IoRT frameworks: connecting robotic fleets via cloud and edge.
• Network orchestration for smart manufacturing (5G, MQTT, ROS2).
• Designing fault-tolerant distributed robotic systems.

Labs (VS Code + PlatformIO):

• Build a multi-robot IoRT network using MQTT brokers for real-time data sharing.
• Implement on-device AI inference for edge-based decision making.
• Simulate robotic communication latency vs. cloud efficiency.

Audio Lectures:

• “IoRT: Connecting Robots to the Smart World” (25 mins).
• “Balancing Cloud Robotics and Edge Computing" (20 mins).

Assessments:

• Matching Quiz: IoRT concepts vs. applications (10 questions).
• MCQ Quiz: 40 questions on IoRT and edge AI topics.