Wednesday March 31, 2010

The Robotics Mentor program was created to link current Penn engineering students with high school robotics teams in the Philadelphia School District.  The mentors work either on Penn’s campus or in the schools during club time to help guide students through the engineering design process, electronics, programming, and construction.  Mentors, through robotics, will be showing students the exciting possibilities of STEM education.

Thursday March 18, 2010

The GRASP lab was invited by the Secondary Robotics Initiative to exhibit at the 2010 National Science Teachers Association Conference, March 18th-19th, at the Philadelphia Convention Center.  The conference exhibit exposed K-12 teachers to GRASP and the possibilities of bringing the interdisciplinary world of robotics into the classroom. The RHex, CKbot and a Scarab all made appearances accompanied by video footage of the Bots in action. 

Friday March 26, 2010

Joe Romano, GRASP PhD student, won the Best Short Oral Presentation Award at the IEEE 2010 Haptics Symposium for his talk on "Realistic haptic contacts and textures for tablet computing," a hands-on demonstration co-authored by Dr. Katherine Kuchenbecker.  This award was determined by audience vote and was comprised of 77 contestants.

Presenter: Leslie Kaelbling (Homepage)

Event Dates:
  Friday April 30, 2010 from 11:00am to 12:00pm

* Alternate Location: Berger Auditorium (Skirkanich Hall)*

Most work on robot grasping concentrates on geometric questions of how best to place the fingers in order to achieve a stable grasp, and typically assumes that the relative pose of the robot and object are known fairly accurately. In this talk, I will outline an approach to robust grasping when the object's pose is initially estimated using vision or some other sensor modality with a fair amount of residual uncertainty.

Presenter's Biography:

Leslie Pack Kaelbling is Professor of Computer Science and Engineering at the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology. She has made research contributions to decision-making under uncertainty, learning, and sensing with applications to robotics, with a particular focus on reinforcement learning and planning in partially observable domains.

She holds an A.B in Philosphy and a Ph.D. in Computer Science from Stanford University, and has had research positions at SRI International and Teleos Research and a faculty position at Brown University. She is the recipient of the US National Science Foundation Presidential Faculty Fellowship, the IJCAI Computers and Thought Award, and several teaching prizes. She was the founder and editor-in-chief of the Journal of Machine Learning Research, and is a fellow of the AAAI.

Presenter: Michael Kahana (Homepage)

Event Dates:
  Friday April 23, 2010 from 11:00am to 12:00pm

The fundamental problem of episodic memory concerns linking items with their temporal context (during study) and retrieving the context associated with items (during recall). The reinstatement of mental context is distinguished from the idea that remembering solely involves a reactivation of content information that is specific to that event. I will first present behavioral evidence for the idea associations in episodic memory arise from this contextual encoding/retrieval process, and that forgetting largely reflects the loss of effective contextual cues at retrieval.

Presenter's Biography:

Michael Kahana is currently a Professor in the Department of Psychology and Director of the Computational Memory Lab at the University of Pennsylvania. He received his B.A. from Case Western Reserve University in 1989 and his PhD in Psychology from the University of Toronto in 1993. In 1994, Michael then went on to be a Postdoctoral Fellow in Psychology at Harvard University. Michael's main research interests include human memory and its neural mechanisms: especially episodic memory, spatial memory, and recognition memory.  In the Computational Memory Lab, they do a lot of work on brain oscillations, and computational modeling as well.


Presenter: Dimitris Metaxas (Homepage)

Event Dates:
  Friday April 16, 2010 from 11:00am to 12:00pm

We will first present a new class of model-based learning methods which include hypergraph and structured sparse learning for vision understanding. In our hypergraph framework, a hyperedge is defined by a set of vertices with similar attributes. The complex relationship between the mages can be easily represented by different hyperedges according to different visual cues.

Presenter's Biography:

Dr. Dimitris Metaxas is a Professor II (Distinguished) in the Computer Science Department at Rutgers. He got his PhD in 1992 from the University of Toronto and was on the faculty at UPENN from 1992 to 2002. He is currently directing the Center for Computational Biomedicine, Imaging and Modeling (CBIM). Dr. Metaxas has been conducting research towards the development of formal methods upon which both computer vision, computer graphics and medical imaging can advance synergistically, as well as on massive data analytics problems.In computer vision, he works on the simultaneous segmentation and fitting of complex objects, shape representation, deterministic and statistical object tracking, learning, ASL and human activity recognition. In medical image analysis, he works on segmentation, registration and classification methods for cardiac and cancer applications. In computer graphics he is working on physics-based special effects methods for animation. He has pioneered the use of Navier-Stokes methods for fluid animations that were used in the Movie “Antz” in 1998 by his student Nick Foster. Dr. Metaxas has published over 350 research articles in these areas and has graduated 27 PhD students. His research has been funded by NSF, NIH, ONR, DARPA, AFOSR and the ARO. He is on the Editorial Board of Medical Imaging, an Associate Editor of GMOD, and CAD. Dr. Metaxas received several best paper awards for his work on in the above areas. He is an ONR YIP and a Fellow of the American Institute of Medical and Biological Engineers. He has been a Program Chair of ICCV 2007, a General Chair of MICCAI 2008 and will be a General Chair of ICCV 2011.


Presenter: Alan Stocker (Homepage)

Event Dates:
  Friday April 9, 2010 from 11:00am to 12:00pm

Perception is the process of inferring properties of the world from sensory information. Noise and ambiguities in the sensory signal typically lead to uncertainty in interpreting this information. Perceptual systems, either biological or artificial, require efficient strategies to deal with this uncertainty.
Focusing on the perception of visual motion, I will describe an analog network architecture that can robustly compute the visual motion of an object despite sensory noise and the ambiguities imposed by the aperture problem.

Presenter's Biography:

Alan Stocker received his MSc in Biomedical Engineering and Material Science and his PhD in Physics from the Swiss Federal Institute of Technology, ETH Zürich, in 1995 and 2002 respectively. Alan is currently an Assistant Professor in the Department of Psychology at the University of Pennsylvania, where he is the head of the CPC, Computational Perception and Cognition, Laboratory. His research is aimed at finding a principled, normative description of perceptual and cognitive behaviors through an interdisciplinary approach of psychophysics, neurophysiology and physics.

Friday March 26, 2010

Dr. Katherine Kuchenbecker and her students are attending the IEEE Haptics Symposium Conference in Boston this week. The IEEE Spectrum has highlighted one of their hands-on haptics projects, "Tactile Gaming Vest Punches and Slices".

Presenter: Weichuan Yu (Homepage)

Event Dates:
  Wednesday April 7, 2010 from 12:00pm to 1:00pm

* Alternate Location: Levine 512 (3330 Walnut Street)*

In ultrasound image-based tissue deformation analysis, feature-motion decorrelation causes feature tracking results fail to represent the underlying true tissue deformation. We propose a new coupled filtering method to solve the feature-motion decorrelation problem. After explicitly modeling image variations caused by tissue deformation, we filter the image before tissue deformation and the warped image after tissue deformation with a pair of filters, respectively. We show through theoretical derivation that the two filtered images are identical to each other.

Presenter's Biography:

Weichuan Yu received his Ph.D. degree in Computer Vision and Image Analysis from University Kiel, Germany in 2001.  He was a postdoctoral associate at Yale University from 2001 to 2004, and a research faculty member in the Center for Statistical Genomics and Proteomics at Yale University from 2004 to 2006. Since August 2006, he has been with the faculty in the Department of Electronic and Computer Engineering at the Hong Kong University of Science and Technology.

He is interested in computational analysis problems with biological and medical applications. He has published around 50 journal articles and referred conference papers on a variety of topics including bioinformatics, computational biology, biomedical imaging, signal processing, pattern recognition and computer vision. He was the recipient of the DAAD (German Academic Exchange Center) Fellowship.

Presenter: Fernando De la Torre (Homepage)

Event Dates:
  Thursday March 25, 2010 from 12:00pm to 1:00pm

* Alternate Location: Levine 307 (3330 Walnut Street)*

Providing computers with the ability to understand human behavior from sensory data (e.g. video, audio, or wearable sensors) is an essential part of many applications that can benefit society such as clinical diagnosis, human computer interaction, and social robotics. A critical element in the design of any behavioral sensing system is to find a good representation of the data for encoding, segmenting, classifying and predicting subtle human behavior. In this talk I will propose several extensions of Component Analysis (CA) techniques (e.g.

Presenter's Biography:

Fernando De la Torre received his B.Sc. degree in Telecommunications (1994), M.Sc. (1996), and Ph. D. (2002) degrees in Electronic Engineering from La Salle School of Engineering in Ramon Llull University, Barcelona, Spain. In 1997 and 2000 he was an Assistant and Associate Professor in the Department of Communications and Signal Theory in Enginyeria La Salle. Since 2005 he has been a Research Assistant Professor in the Robotics Institute at Carnegie Mellon University. Dr. De la Torre's research interests include computer vision and machine learning, in particular face analysis, optimization and component analysis methods, and its applications to human sensing. Dr. De la Torre co-organized the first workshop on component analysis methods for modeling, classification and clustering problems in computer vision in conjunction with CVPR'07, and the workshop on human sensing from video jointly with CVPR'06. He has also given several tutorials at international conferences (ECCV'06, CVPR'06, ICME'07, ICPR'08) on the use and extensions of component analysis methods. Currently he leads the Component Analysis Laboratory ( and the Human Sensing Laboratory (