CMU talk: Perception on an Offroad Robot: Shallow and Deep Learning Architectures

VASC Seminar
Monday, February 2, 2009

Perception on an Offroad Robot: Shallow and Deep Learning Architectures
Raia Hadsell
Robotics Institute, CMU

Abstract:
Perception for offroad mobile robots is very difficult. Roads and paths aren't visually consistent, nor are they guaranteed to exist at all; obstacles are diverse and often visually complex. In addition, long range perception is more important when path planning needs to be done in the absence of clear roads or corridors. I will describe 2 learning-based approaches to perception in offroad environments: first, a kernel-based method for rough terrain reconstruction, and second, a self-supervised online vision system that can detect paths and obstacles at very long range and that quickly adapts to new environments.

Raia Hadsell is currently a postdoc at the Robotics Institute, working with Martial Hebert, Drew Bagnell, and Daniel Huber. She completed her doctorate at New York University in 2008, under the advisement of Yann LeCun, with research interests that lie in the intersection of machine learning, vision, and robotics. She has also enjoyed internships at Google NYC and Net-Scale Technologies. Dr. Hadsell, if pressed, will own up to her bachelor's degree in religion and philosophy and may discuss Nietzsche on occasion.