Gain a foundational understanding of how artificial intelligence (AI) and robotics converge to create intelligent systems. This segment introduces the core concepts and real-world applications.

• Machine Learning in Robotics: How It Works Explore how machine learning enables robots to recognize patterns, adapt to environments, and make decisions. Examples include object detection, speech recognition, and navigation.
• Types of AI Used in Robotics: Different AI models used in robotics - supervised, unsupervised, and reinforcement learning - and where each is best applied in robotic systems.
• Sensors, Perception & Decision-Making: Understand how robots perceive their surroundings through AI-powered sensors (vision, LIDAR, touch) and convert that data into intelligent actions.
• Real-World Applications of AI-Enabled Robots: From self-driving vehicles and healthcare robots to automated warehouses and drones—see how AI-driven robotics is revolutionizing industries.
• Building an AI-Robotic System Tools & Platforms: Overview of popular platforms like ROS (Robot Operating System), TensorFlow, OpenCV, and Python libraries used for integrating AI into robotics.
• Challenges in AI Integration: Real-world challenges such as computational limitations, training data needs, ethical concerns, and safety in AI-powered robotic systems.
• Career Pathways in AI & Robotics: Guidance on educational routes, certifications, and job roles in AI-robotics, from ML engineer to robotic software developer.
• Live Demo / Case Study: A real-time simulation or a video walkthrough of an AI-based robot in action—like object tracking or autonomous movement.

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