[talks] Talk: Ashutosh Saxena - Monday 12:30 CS 402

[Nicole E. Wagenblast]

Date: Monday, September 23, 2013 

Time: 12:30-13:30 

Room: CS 402 

Title: How should a robot perceive the world? 

Speaker: Ashutosh Saxena 

Abstract: 
In order to perform assistive tasks, a robot should perceive a functional understanding of the environment. This comprises learning 
how the objects in the environment could be used (i.e., their affordances). In this talk, I will discuss what types of object 
representations could be useful. One challenge is to model the object's context with each other and with (hidden) humans. Our 
learning algorithm will be Infinite Latent CRFs (ILCRFs) that allow modeling the data with different plausible graph structures. Unlike 
CRFs, where the graph structure is fixed, ILCRFs learn distributions over possible graph structures in an unsupervised manner. 

We then show that our idea of modeling environments using object affordances and hidden humans is not only useful for robot 
manipulation tasks such as arranging a disorganized house, haptic manipulation, unloading items from a dishwasher, but also in 
significantly improving standard robotic tasks such as scene segmentation, 3D object detection, human activity detection and 
anticipation, and task and path planning. 

Bio: 
Ashutosh Saxena is an assistant professor in computer science department at Cornell University. His research interests include 
machine learning and robotics perception, especially in the domain of personal robotics. He received his MS in 2006 and Ph.D. in 2009 from Stanford University, and his B.Tech. in 2004 from Indian Institute of Technology (IIT) Kanpur. He is a recipient of National Talent Scholar award in India, Google Faculty award, Alfred P. Sloan Fellowship, Microsoft Faculty Fellowship, and NSF Career award. 

In the past, Ashutosh developed Make3D (http://make3d.cs.cornell.edu), an algorithm that converts a single photograph into a 3D model. Tens of thousands of users used this technology to convert their pictures to 3D. He has also developed algorithms that enable robots (such as STAIR, POLAR, see http://pr.cs.cornell.edu) to perform household chores such as unload items from a dishwasher, place items in a fridge, etc. His work has received substantial amount of attention in popular press, including the front-page of New York Times, BBC, ABC, New Scientist, Discovery Science, and Wired Magazine. He has won best paper awards in 3DRR, IEEE ACE and RSS, and was named a co-chair of the IEEE technical committee on robot learning. 


-- 
Best, 

Jianxiong Xiao 
Assistant Professor 
Department of Computer Science 
Princeton University 
http://vision.princeton.edu 

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