VASC Seminar
Prof. Roberto Manduchi manduchi@soe.ucsc.edu
Professor of Computer Engineering
University of California, Santa Cruz

Assistive technology for wayfinding, information access, and public transit

Event Location: Newell Simon Hall 1507Bio: Roberto Manduchi is a Professor of Computer Engineering at the University of California, Santa Cruz, where he conducts research in the areas of computer vision and sensor processing with applications to assistive technology. Prior to joining UCSC in 2001, he worked at the NASA Jet Propulsion Laboratory and at [...]

RI Seminar

Stabilizing the Unstable Brain

NSH 1305

Noah Cowan Associate Professor of Mechanical Engineering, Johns Hopkins University Abstract The nervous system is arguably the most sophisticated control system in the known universe, riding at the helm of an equally sophisticated plant. Understanding how the nervous system encodes and processes sensory information, and then computes motor action, therefore, involves understanding a closed loop. [...]

RI Seminar
Peter Stone
David Bruton, Jr. Centennial Professor
The University of Texas at Austin

Robot Skill Learning: From the Real World to Simulation and Back

Event Location: NSH 1305Bio: Dr. Peter Stone is the David Bruton, Jr. Centennial Professor and Associate Chair of Computer Science, as well as Chair of the Robotics Portfolio Program, at the University of Texas at Austin. In 2013 he was awarded the University of Texas System Regents' Outstanding Teaching Award and in 2014 he was [...]

RI Seminar

Robot Skill Learning: From the Real World to Simulation and Back

NSH 1305

Peter Stone David Bruton, Jr. Centennial Professor, The University of Texas at Austin Abstract For autonomous robots to operate in the open, dynamically changing world, they will need to be able to learn a robust set of interacting skills. This talk begins by introducing "Overlapping Layered Learning" as a novel hierarchical machine learning paradigm for [...]

VASC Seminar
Pulkit Agrawal
PhD Student 
University of California Berkeley

Intuitive Physics & Intuitive Behavior 

Event Location: Newell Simon Hall 1507Bio: Pulkit is a PhD Student in the department of Computer Science at UC Berkeley. His research focuses on computer vision, robotics and computational neuroscience. He is advised by Dr. Jitendra Malik. Pulkit completed his bachelors in Electrical Engineering from IIT Kanpur and was awarded the Director’s Gold Medal. He is a recipient of Fulbright Science [...]

PhD Thesis Proposal
Tony Dear
Carnegie Mellon University

Extensions of the Principal Fiber Bundle Model for Locomoting Robots

Event Location: NSH 1507Abstract: Our goal is to establish a rigorous formulation for modeling the locomotion of a broad class of robotic systems. Recent research has identified a number of systems with the structure of a principal fiber bundle. This framework has led to a number of tools for analysis and motion planning applicable to [...]

PhD Thesis Defense
Christopher Cunningham
Carnegie Mellon University

Improving Prediction of Traversability for Planetary Rovers Using Thermal Imaging

Event Location: GHC 4405Abstract: The most significant mobility challenges that planetary rovers encounter are compounded by loose, granular materials that cause slippage and sinkage on slopes or are deep enough to entrap a vehicle. The inability of current technology to detect loose terrain hazards has caused significant delays for rovers on both the Moon and [...]

RI Seminar

Deep Robotic Learning

NSH 1305

Sergey Levine Assistant Professor, UC Berkeley Abstract Deep learning methods have provided us with remarkably powerful, flexible, and robust solutions in a wide range of passive perception areas: computer vision, speech recognition, and natural language processing. However, active decision making domains such as robotic control present a number of additional challenges, standard supervised learning methods [...]

PhD Thesis Proposal

Learning to Learn and Structure Learning in Model Spaces for Small Sample Visual Recognition

GHC 8102

Yuxiong Wang Carnegie Mellon University Abstract Understanding how to recognize novel categories from few examples for both humans and machines remains a fundamental challenge. Humans are remarkably able to grasp a new category and make meaningful generalization to novel instances from just few examples. By contrast, state-of-the-art machine learning techniques and visual recognition systems typically [...]