Meeting Time: M, W 9:45AM-11:00PM CT - Applebay Hall 102
This course covers the necessary background of neural-network-based deep learning for robot perception – building on advancements in computer vision that enable robots to physically manipulate objects. During the first part of this course, students will learn to implement, train and debug their own neural networks. During the second part of this course, students will explore recent emerging topics in deep learning for robot perception and manipulation. This exploration will include analysis of research publications in the area, building up to reproducing one of these publications for implementation as a final course project.
This course is being offered at the University of Minnesota in the Fall of 2024 (Karthik Desingh).
This course is being developed-maintained-offered through a Distributed Teaching Collaborative between faculty at the University of Minnesota (Karthik Desingh) and the University of Michigan (Anthony Opipari, Xiaoxiao Du, Chad Jenkins)
This course builds on and is indebted to these existing courses (as a “star” and a “fork” in the open source sense):
- University of Michigan - ROB 498-011 & 599-011: Deep Learning for Robot Perception instructed by (Xiaoxiao Du, (Anthony Opipari, Chad Jenkins)
- University of Minnesota - CSCI5980: Deep Learning for Robot Perception and Manipulation instructed by (Karthik Desingh, in collaboration with (Anthony Opipari, Chad Jenkins) from the University of Michigan.
- University of Michigan - EECS 498-007 / 598-005: Deep Learning for Computer Vision instructed by Justin Johnson
- Stanford - CS231n: Deep Learning for Computer Vision instructed by Fei-Fei Li and Andrej Karpathy
