The material here provided was developed as part of the Robotic Vision Summer School.
Please see this separate README.
You can run the notebooks via Colab: http://colab.research.google.com/github/rvss-australia/RVSS.
Links to slide decks and information about the lectorials will appear below.
Presented by Peter Corke, Queensland University of Technology
The slides, details and resources for the A stream are here: A Slides
Presented by Teresa Vidal-Calleja, University of Technology Sydney
Describing poses, transformations, and uncertainty
Keeping track of stuff with imperfect measurement; Kalman filters, factor graphs, and batch optimisation
Presented by Simon Lucey, University of Adelaide
Introduction to visual learning with shallow and deep networks
Introduction to convolutional neural networks
Presented by Dana Kulic, Monash University
Action selection as supervised learning; behaviour cloning
Introduction to reinforcement learning Part 1
Introduction to reinforcement learning part 2
If you'd like to know more
- David Silver's RL Video Lectures at UCL
- Prof. Pascal Poupart's Video Lectures at University of Waterloo, Canada
- Sutton and Barton's Introduction to Reinforcement Learning book
- Sergey Levine's Video Lectures on deep reinforcement learning at UCBerkeley