SUNMONTUEWEDTHUFRISAT
Events for March 27, 2019
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ECE Seminar: Exploiting Terrain Responses for Effective Locomotion in Complex Environments
Wed, Mar 27, 2019 @ 10:30 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Feifei Qian, Postdoctoral Researcher, GRASP Lab, University of Pennsylvania
Talk Title: Exploiting Terrain Responses for Effective Locomotion in Complex Environments
Abstract: Today, robots are expected to take on increasingly important roles in human society. However, state-of-the-art robots still struggle to move on natural terrain, due to the lack of understanding of the interactions between robots and non-flat, non-rigid surfaces. My research aims to generate simplified models and representations of locomotor-terrain interactions, and improve robot mobility in complex environments.
In this talk, I will demonstrate how I integrate granular physics, bio-inspired robotics, and locomotion biomechanics to create interaction models that can guide design and control of bio-inspired robots to produce effective movement on challenging terrains. First, I will briefly review my previous work of animal and robot locomotion on granular terrain such as sand, debris, and gravel, and discuss how locomotors can manipulate granular responses and achieve effective locomotion on sand through adjustments in morphological parameters or contact strategy. Then I will present my recent work on creating simplified representations of robot interaction with perturbation-rich environments such as cluttered rubble or fallen tree trunks, and discuss how a multi-legged robot can adjust its gait patterns to exploit obstacle disturbances and generate different dynamics from the same physical environment. I will conclude with a vision of how these models and representations can lead to innovative strategies for obstacle-aided locomotion, better understanding of animal gait transition behaviors, and embodied sensing of environment properties.
Biography: Feifei Qian is currently a postdoctoral researcher in the GRASP lab at University of Pennsylvania. She received her PhD degree in Electrical Engineering from Georgia Tech in 2015. She is interested in understanding interactions between legged robots and complex terrains, and creating solutions for robots to exploit obstacles and disturbances to improve mobility. Her work was awarded the best student paper at Robotics: Science and Systems, and has been covered by media press including BBC, R&D Magazine, Phys.org, and PennCurrent.
Host: Professor Paul Bogdan, pbogdan@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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Mathematical Foundations of Learning from Signals and Data (Math-FLDS)
Wed, Mar 27, 2019 @ 03:00 PM - 04:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Alex Cloninger, University of California, San Diego
Talk Title: Crafting Laplacian Eigenfunctions to the Data Science Task
Series: MHI
Abstract: We will discuss two topics related to the importance of selecting particular eigenfunctions of the graph Laplacian. First, we discuss the geometry of Laplacian eigenfunctions on compact manifolds and combinatorial graphs. We will use a notion of similarity between eigenfunctions that allows to reconstruct a dual geometry, which recovers classical duals in particular cases. We will focus on the applications of discovering such a dual geometry, namely in constructing anisotropic graph wavelet packets and anisotropic graph cuts. A second topic will be the relevance of selecting import eigenfunctions for two sample testing, namely kernel Maximum Mean Discrepancy. This creates a more powerful test than the classical MMD while still maintaining sensitivity to common departures. We examine this two-sample testing in several medical examples.
Biography: Alex Cloninger is an Assistant Professor of Mathematics at UCSD. He received his PhD in Applied Mathematics and Scientific Computation from the University of Maryland in 2014 and was then an NSF Postdoc and Gibbs Assistant Professor of Mathematics at Yale University until 2017, when he joined UCSD. Alex researches problems around the analysis of high dimensional data. He focuses on approaches that model the data as being locally lower dimensional, including data concentrated near manifolds or subspaces. These types of problems arise in a number of scientific disciplines, including imaging, medicine, and artificial intelligence, and the techniques developed relate to a number of machine learning and statistical algorithms, including deep learning, network analysis, and measuring distances between probability distributions
Host: Mahdi Soltanolkotabi and Paul Bogdan
More Information: Cloninger, Alex Seminar.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Gloria Halfacre
