Deep Learning for Robotics

Week 1

Get a beginner’s overview of supervised learning for robotics applications. Topics include:

  • Use backpropagation to train a simple neural network and identify overfitting
  • Build a neural network using PyTorch for an obstacle avoidance system


Week 2

Use neural networks to control a robot’s motion. Additional topics include:

  • Use neural networks for inverse kinematic motion calculation
  • Improve training with techniques such as dropout and regularization
  • Solve high-dimensional problems by dimension reduction with principal component analysis (PCA)


Week 3

Discover reinforcement learning where an agent learns from its environment instead of labels. Topics include:

  • Define a policy and compute its gradient
  • Write a reinforcement learning agent with PyTorch
  • Get an overview of Reinforcement Learning Coach, a state-of-the-art reinforcement learning framework


Week 4

Explore how to incorporate temporal data into neural networks, including:

  • Backpropagation through time and vanishing or exploding gradients
  • Many variations of recurrent neural networks (RNN) and LSTMs and how to implement them in PyTorch