Machine Learning in Robotics: Teaching Robots to Learn and Adapt
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Course Modules:
Module 1: Introduction to Robot Learning
What makes robot learning unique?
Overview of machine learning types used in robotics
Applications: object recognition, navigation, manipulation, control
Module 2: Supervised Learning for Perception
Image classification and object detection
Dataset labeling and training for robotic vision
Using CNNs with OpenCV or TensorFlow
Module 3: Reinforcement Learning in Robotics
Trial-and-error learning in dynamic environments
Policies, rewards, and exploration strategies
Examples: robotic arm control, balancing bots
Module 4: Learning from Demonstration
Imitation learning and human-in-the-loop systems
Behavior cloning and trajectory learning
Safe learning for physical robot tasks
Module 5: Online and Continual Learning
Adapting to new environments in real-time
Handling sensor drift and hardware variation
Robot learning under constraints (power, safety, speed)
Module 6: Capstone Project – Build a Learning Robot
Choose a task (e.g., pick-and-place, obstacle avoidance, gesture following)
Train a model using supervised or reinforcement learning
Submit code, performance visuals, and a report explaining learning curves
Tools & Technologies Used:
Python
TensorFlow, PyTorch
OpenCV (for vision tasks)
OpenAI Gym, PyBullet, ROS (for simulation or real-time testing)
Target Audience:
Robotics students and engineers
AI/ML practitioners entering physical computing
Developers creating smart, adaptive systems
Researchers exploring robot perception and learning
Global Learning Benefits:
Bridge theory and practice in robotics and ML
Learn to train robots that improve and evolve
Apply ML tools to real-world physical tasks
Prepare for careers in autonomous systems, industrial automation, and AI-driven hardware
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