|
|
Robotics Day
|
Presented by:
|
|
|
|
Penn
Engineering and the GRASP Lab
at Penn are organizing one
day robotics research outreach activities at the newly opened Penn
Wharton China Center. The day's
activities are open to researchers in robotics, vision, machine learning and
automation. Our topics cover
broad topics in building machines that can think, plan and move. We highlight
the connections between human, biological mechanism, and robotics. This event brings together six world leaders in
robotics engineering from UPenn, and leading
researchers in China. In order to
be able to attend the day's activities, you must register Here. As a follow up to this, we are organizing with
Institute of Automation, Chinese Academy of Sciences Summer School on Pattern
Recognition and Human Centered Robotics.
The full event page is Here.
http://prhcr2015.csp.escience.cn/dct/page/1 |
|
|
|
|
JUNE 19, Robotics Outreach |
|
|
|
09:00-09:30 |
Round Table Introduction |
|
|
|
09:30-09:50 |
Machine Learning for Robots: Perception,
Planning and Motor Control Director, GRASP
(General Robotics Automation, Sensing, Perception) Lab
These algorithms employ
a variety of techniques central to machine learning: dimensionality
reduction, online learning, and reinforcement learning. I will show and
discuss applications of these algorithms to autonomous vehicles and humanoid
robots. |
Faculty Fellow, and Professor in the School of Engineering and
Applied Science at the University of Pennsylvania. He received his B.A. summa cum laude in Physics from Harvard
University in 1990 and his Ph.D. in Condensed Matter Physics from the
Massachusetts Institute of Technology in 1995. Before coming to Penn,
he was a researcher at AT&T and Lucent Bell Laboratories in the
Theoretical Physics and Biological Computation departments. He is a Fellow of
the IEEE and has received the
National Science Foundation CAREER award and the University of Pennsylvania Lindback
award for distinguished teaching. He was also a fellow of the Hebrew University Institute of
Advanced Studies in Jerusalem, an affiliate of the Korea Advanced
Institute of Science and Technology, and organized the US-Japan National
Academy of Engineering Frontiers of Engineering symposium. As
director of the GRASP Robotics Laboratory, his group focuses on understanding
general computational principles in biological systems, and on applying that
knowledge to build autonomous systems. |
|
|
09:50-10:10 |
Legged Locomotion for the
Urban-Desert Interface: from Robust Steady State to Reactive Transition
Maneuvers
This talk will address
some of the challenges facing legged robot platforms that aim to bring autonomously
borne environmental sensory payloads into the mixed terrain encountered at
the interface between the human engineered urban landscape and the complex
desert substrate. On the one
hand, the urban environment generally presents regular geometries and hard
surfaces, punctuated by occasional slippery or fragile substrates. These
conditions require robust steady state gaits of a kind that contemporary
robotics has become increasingly capable of delivering. On the other hand,
the desert environment presents highly irregular geometries and complex,
poorly modeled substrates that demand a much more aggressive ability to
transition between gaits and other modes of self-manipulation. The talk, an
adaptation and extension of a plenary lecture given at the 2012 IEEE Workshop
on the Future Intelligent Desert (Shanghai), will explore some of the new
approaches we have begun to take toward this rather undeveloped area of
legged locomotion science. |
Daniel E. Koditschek is the Alfred Fitler Moore Professor of Electrical and Systems
Engineering. Dr. Koditschek received his bachelorÕs
degree in Engineering and Applied Science and his M.S. and Ph.D. degrees in
Electrical Engineering in 1981 and 1983, all from Yale University. He served
on the Yale Faculty in Electrical Engineering until moving to the University
of Michigan a decade later. In January 2005, he moved to the University of
Pennsylvania to assume the post of Chair of the Electrical and Systems
Engineering Department, within the School of Engineering and Applied Science. KoditschekÕs research interests
include robotics and, more generally, the application of dynamical systems
theory to intelligent mechanisms. His archival journal and refereed
conference publications, numbering well over 100, have appeared in a broad
spectrum of venues ranging from the Transactions of the American Mathematical
Society through The Journal of Experimental Biology, with a concentration in
several of the IEEE journals and related transactions. |
|
|
10:10-10:30 |
On robotics for health care
monitoring in elder care
The promise of robot
systems as initially imagined in science fiction is that of generic machines
capable of doing a variety of tasks often mimicking
humans. It turns out doing that can be very expensive. This talk will
present some general principles towards designing low cost systems while also
presenting specific examples ranging from mechatronic components (sensors and
actuators), robotic components (grippers) to full systems (mobile manipulator
and flying systems). In each case we will present some practical examples of
methods that can be applied today. |
Mark Yim is a professor at the
University of Pennsylvania. His group designs and builds modular
self-reconfigurable robots and has demonstrated robots that can transform
into different shapes, jump, climb, manipulate objects and reassemble
themselves after being kicked into pieces. Recently, his work has
followed a theme of simplicity and low cost. His other research interests
include product design, reactive art and architecture, origami, snake
locomotion, flying robots, and self-assembling floating structures. Honors include the Lindback Award
for Distinguished Teaching (UPenn's highest
teaching honor); induction as a World Technology Network Fellow; and induction to MIT's TR100 in 1999. He has
over 40 patents issued (perhaps most prominent are related to the video game
vibration control which resulted in over US$100,000,000 in litigation and
settlements). |
|
|
10:30-11:45 |
Tea and Coffee |
|
|
|
10:45-11:05 |
3D object recognition,
localization, and reconstruction
In this talk, we address the problem of detection,
localization, and reconstruction of 3D objects in cluttered scenes. Object
exemplars are given in terms of 3D models and view exemplars. We learn and
select appearance-based discriminative parts which
are mapped onto the 3D model from the training set through a facility
location optimization. The training set of 3D models is summarized into a
sparse set of shapes from which we can generalize by linear combination. The
main challenge is how to combine geometry and appearance: how to select part
hypotheses and compute the 3D pose and shape at the same time. To achieve
this, we optimize a function that minimizes simultaneously the geometric reprojection error as well the appearance matching of the
parts. We apply the alternating direction method of multipliers (ADMM) to
minimize the resulting convex function. For specific classes of objects like surfaces of revolution,
we prove how we can estimate 3D pose and shape from two views without any
appearance information. |
Kostas Daniilidis is Professor of
Computer and Information Science at the University of Pennsylvania where he
has been faculty since 1998. He currently the Associate Dean of
Education. He obtained his undergraduate degree in Electrical Engineering
from the National Technical University of Athens, 1986, and his PhD in Computer Science from the University of
Karlsruhe, 1992, under the supervision of Hans-Hellmut
Nagel. His research interests are on visual motion and navigation,
active perception, 3D object detection and localization, and panoramic
vision. He was Associate Editor of IEEE Transactions on Pattern Analysis
and Machine Intelligence from 2003 to 2007. He founded the series of
IEEE Workshops on Omnidirectional Vision. In June 2006, he co-chaired with Pollefeys the Third Symposium on 3D Data Processing,
Visualization, and Transmission, and he was Program co-Chair of the 11th
European Conference on Computer Vision in 2010. He has been the director of
the interdisciplinary GRASP laboratory from 2008 to 2013 and he is the
Associate Dean for Graduate Education of Penn Engineering since 2013. He is
an IEEE Fellow. |
|
|
11:05-11:25 |
Social vision: social saliency and
future localization
In first part of talk, we
focus on social vision, relating human motion and action with social
attention. We predict social
saliency, the likelihood of joint attention in 3D space. We learn from the social interaction
captured by first person cameras, and apply the model in a third person
image/video. Our representation
does not require directional measurements such as gaze directions. A
geometric analysis of social interactions in terms of existing qualitative
studies such as F-formation and proxemics is presented. In the second part of
talk, we future localization: to predict a set of plausible trajectories
given a depth image. We predict paths avoiding obstacles, between objects,
even paths turning around a corner into space behind objects. Inspired by proxemics, we represent
the space around a person using an EgoSpace map,
akin to an illustrated tourist map.
We learn the relationship between the EgoSpace
map and trajectory from first person video providing in-situ measurements of
the future trajectory. We
quantitatively evaluate our method to show predictive validity and apply to
various real world scenes including walking, shopping, and social
interactions. |
Jianbo Shi studied Computer Science and Mathematics as an
undergraduate at Cornell University where he received his B.A. in 1994. He
received his Ph.D. degree in Computer Science from University of California
at Berkeley in 1998. He joined The Robotics Institute at Carnegie Mellon
University in 1999 as a research faculty, where he lead the Human
Identification at Distance(HumanID)
project, developing vision techniques for human identification and activity
inference. In 2003 he joined
University of Pennsylvania where he is currently a Professor of Computer and
Information Science. In 2007, he
was awarded the Longuet-Higgins Prize for his work
on Normalized Cuts. His papers
have been cited over 27,000 times.
His current research focuses on human behavior analysis and image
recognition-segmentation. His other research interests include image/video
retrieval, 3D vision, and vision based desktop computing. His long-term
interests center around a broader area of machine intelligence, he wishes to
develop a "visual thinking" module that allows computers not only
to understand the environment around us, but also to achieve cognitive
abilities such as machine memory and learning. |
|
|
11:25-11:45 |
Bioinspired, Adaptive Building Skins
Nature provides us fascinating examples
with remarkable optical effects, including the dazzling irridence
on butterfly wings and dynamic underwater camouflage on Cephalopod skins that
can change from transparency to red. Meanwhile, there have been tremendous
interests in design of smart roofing, skylights, and architectural windows,
which can block or reflect sunlight on scorching days, and return to a
transparent state at a low lighting condition to improve light harvesting and
capture free heat from the sun. The assembly of the existing devices,
however, is often complex and costly. Taking the cues from nature, we fabricate different
material systems from colloidal particle dispersions in a solution and in a
film, which can dramatically and reversibly change the optical properties
from a highly transparent state to colorful displays and/or opaqueness in
respond to proximity, lighting, motion, and mechanical stretching. By
coupling the materials-environment response at the nano-
and microscales with CMOS technology, we
demonstrate autonomous tracking/imaging/sensing ability and feedback control
systems as the first step toward
energy efficient building skins. |
Shu
Yang is a Professor in the Departments of Materials Science &
Engineering, and Chemical & Biomolecular
Engineering at University of Pennsylvania. Her group is interested in
synthesis, fabrication and assembly of polymers, liquid crystals, and
colloids with precisely controlled size, shape, and geometry; investigating
the dynamic tuning of their szie and structures,
and the resulting unique optical, mechanical and surface/interface
properties. Yang received her BS degree from Fudan
University, China in 1992, and Ph. D. degree from Chemistry and Chemical
Biology while researching in the Department of Materials Science and
Engineering at Cornell University in 1999. She worked at Bell Laboratories,
Lucent Technologies as a Member of Technical Staff before joing
Penn in 2004. She is elected as Fellow of National Academy
of Inventors (2014) and TR100 as one of the worldÕs top 100 young innovators
under age of 35 by MIT's Technology Review (2004). She was a recipient
of ICI (1999) and Unilever (2001) student awards from American Chemical
Society (ACS) for outstanding research in polymer science and engineering.
She also served as Materials Research Society 2010 Fall meeting co-chair. |
|
|
11:45-13:00 |
Lunch provided |
|
|
|
13:00-14:00 |
Robotics in China 13:00 - 13:20 Prof. Yong Liu Zhejiang
University Title: Semantic
Learning for Robotics 13:20 - 13:40 Prof. Zengguang Hou Institute of
Automation, Chinese Academy of Sciences Title: Design and
Implementation of Active Training for Rehabilitation Robots 13:40 - 14:00 Prof. Xinwan Li Shanghai Jiaotong University Title: Wireless Sensing Network with Sand Robotic in Tengeri Desert of West China |
|
|
|
14:00-15:00 |
14:00 - 14:20 Dr. Yimin Zhang Intel Labs
China/Perceptual Application Innovations Lab Title: Perceptual
Computing for Future Service Robots 14:20 - 14:40 Dr. Duxin
Liu Shenzhen Institutes of
Advanced Technology (SIAT), CAS Title: Lower Limb
Exoskeletons Rehabilitation Robot 14:40 - 15:00 Prof. Xuechao Chen Beijing Institute of
Technology (BIT) Title: Research Highlights from Intelligent Robotics Institute |
|
|
|
15:00-15:30 |
Tea & Coffee |
|
|
|
15:30-16:30 |
Round Table Discussion |
|
|
|
16:30-17:30 |
Reception |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
