About the Course

Most robots today can only do simple things as programmed by professionals. If the tasks have changes, the robots need to be re-programmed. Therefore, the robots cannot adapt to product changes. With the development of Artificial Intelligence, we can teach robots to learn skills. Robotic Learning is a course covering the AI technology which is the fundamentals for the next generation robots. 

Main Contents

Robots and Applications 
  • Manipulation
  • Locomotion
  • Smart manufacturing application
  • Warehouse application
  • Service application
Challenges with Current Deep Reinforcement Learning 
  • Sample complexity
  • Hyperparameter tuning
  • Reward specification
  • Exploration
  • Generalization
  • Scalability
Deep Imitation Learning
  • Demonstration
  • DAGGER
  • Few-shot imitation learning
  • Policy aggregation
  • Policy gradient with demonstrations
Soft Actor-Critic and Applications
  • Maximum entropy RL
  • Soft policy and soft actor-critic
  • The optimization problem
  • Soft Actor-Critic algorithm
  • Applications
Meta Learning
  • RL2 - Fast Reinforcement Learning Via Slow Reinforcement Learning
  • A Simple Neural Attentive Meta-Learner
  • Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Hierarchical Reinforcement Learning
  • Data=efficient hierarchical RL
  • FeUdal networks
Vision-based Robotic Manipulation
  • Imagined goals
  • QT-Opt