Team - HRI-CMM
We took our first steps in creating Curious Minded Machine with the following three institutions: MIT, University of Pennsylvania, and University of Washington. Watch this space for updates on the program.
The University of Washington team has a goal to build a mathematical model of curiosity for the robots to formalize qualitative ideas and assumptions concretely, and with such a computational model implemented on robots, to autonomously demonstrate curiosity.Read More
Lead Principal Investigator: Siddhartha S. Srinivasa
Siddhartha Srinivasa is the Boeing Endowed Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. He is a full-stack roboticist, with the goal of enabling robots to perform complex manipulation tasks under uncertainty and clutter, with and around people. To this end, he founded the Personal Robotics Lab in 2005.
He has been a PI on the Quality of Life Technologies NSF ERC, DARPA ARM-S and the CMU CHIMP team on the DARPA DRC. Understanding the interplay between system components has helped produce state of the art algorithms for object recognition and pose estimation (MOPED), and dense 3D modeling (CHISEL, now used by Google Project Tango).
Siddhartha received a B.Tech in Mechanical Engineering from the Indian Institute of Technology Madras in 1999, and a PhD in 2005 from the Robotics Institute at Carnegie Mellon University.
Principal Investigator: Dieter Fox
Dieter Fox is a Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, where he heads the UW Robotics and State Estimation Lab.
Since October 2017, he is on partial leave from UW and serves as Senior Director of Robotics Research at Nvidia. Dieter obtained his Ph.D. from the University of Bonn, Germany. His research is in robotics and artificial intelligence, with a focus on state estimation and perception applied to problems such as mapping, object detection and tracking, manipulation, and activity recognition.
Dieter has published more than 200 technical papers and is the co-author of the textbook “Probabilistic Robotics.” He is a Fellow of the IEEE and the AAAI, and has received several best paper awards at major robotics, AI, and computer vision conferences. He was an editor of the IEEE Transactions on Robotics, program co-chair of the 2008 AAAI Conference on Artificial Intelligence, and program chair of the 2013 Robotics: Science and Systems conference.
Principal Investigator: Maya Cakmak
Maya Cakmak is an Assistant Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, where she directs the Human-Centered Robotics lab.
She received her PhD in Robotics from the Georgia Institute of Technology in 2012, after which she spent a year as a post-doctoral research fellow at Willow Garage. Her research interests are in human-robot interaction, end-user programming, and assistive robotics. Her work aims to develop robots that can be programmed and controlled by a diverse group of users with unique needs and preferences to do useful tasks.
Maya's work has been published at major Robotics and AI conferences and journals, demonstrated live in various venues and has been featured in numerous media outlets. Tools that she and her students developed are currently being used by robotics companies like Savioke and Fetch Robotics.
She received an NSF CAREER award, a Sloan Research Fellowship, and an RSS Early Career Spotlight.
Principal Investigator: Leila Takayama
Leila Takayama is a human-robot interaction researcher with social science perspective. In 2016, she joined the faculty at the University of California, Santa Cruz, as an acting associate professor of Psychology. Prior to UC Santa Cruz, she was a senior user experience researcher at GoogleX, and was a research scientist and area manager for human-robot interaction at Willow Garage.
She is a World Economic Forum Global Futures Council Member and Young Global Leader. In 2015, Leila was presented the IEEE Robotics & Automation Society Early Career Award. In 2012, she was named a TR35 winner and one of the 100 most creative people in business by Fast Company.
Leila completed her PhD in Communication at Stanford University, advised by Professor Clifford Nass. She also holds a PhD minor in Psychology from Stanford, a master's degree in Communication from Stanford, and bachelor's of arts degrees in Psychology and Cognitive Science from UC Berkeley. During her graduate studies, she was a research assistant in the User Interface Research group at Palo Alto Research Center.
The MIT team is focused on the notion that any intelligent agent must have a theory of the world that can be used to infer a causal theory of sensor percepts, in order to be able to predict future percepts and assorted effects of future actions.Read More
Lead Principal Investigator: Nicholas Roy
Nicholas Roy is the Bisplinghoff Professor of Aeronautics & Astronautics at MIT, and a member of CSAIL. He received his B.Sc. in Physics and Cognitive Science in 1995 and his M.Sc. in Computer Science in 1997, both from McGill University. He received his Ph.D. in Robotics from Carnegie Mellon University in 2003.
He has made research contributions to planning under uncertainty, machine learning, human-computer interaction and aerial robotics.
Nicholas founded and led Project Wing at Google [X] from 2012-2014, and is currently the director of the Bridge under MIT's Quest for Intelligence. He and his students have received best paper or best student paper awards at many conferences (ICRA, RSS, ICMI, ICAPS, PLANS) and he is the recipient of the IEEE RAS Early Career award.
Principal Investigator: Tomas Lozano-Perez
Tomas Lozano-Perez is currently the School of Engineering Professor in Teaching Excellence at MIT and a member of CSAIL. He has been Associate Director of the Artificial Intelligence Laboratory and Associate Head for Computer Science of MIT's Department of Electrical Engineering and Computer Science. He was a recipient of the 2011 IEEE Robotics Pioneer Award and a 1985 Presidential Young Investigator Award. He is a Fellow of the AAAI, a Fellow of the ACM, and a Fellow of the IEEE.
His research has been in robotics (configuration-space approach to motion planning), computer vision (interpretation-tree approach to object recognition), machine learning (multiple-instance learning), medical imaging (computer-assisted surgery) and computational chemistry (drug activity prediction and protein structure determination from NMR & X-ray data). Tomas' current research is aimed at integrating task, motion and decision-theoretic planning for robotic manipulation.
Principal Investigator: Leslie Kaelbling
Leslie Pack Kaelbling is the Panasonic Professor of Computer Science and Engineering at MIT and a member of CSAIL. She has made research contributions to decision-making under uncertainty, learning, and sensing with applications to robotics, with a particular focus on 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.
Leslie is the recipient of the US National Science Foundation Presidential Faculty Fellowship, the IJCAI Computers and Thought Award, and several teaching prizes, and has been elected a fellow of the AAAI. She was the founder and editor-in-chief of the Journal of Machine Learning Research.
Principal Investigator: Josh Tenenbaum
Josh Tenenbaum is Professor of Computational Cognitive Science in the Department of Brain and Cognitive Sciences at MIT, and a member of CSAIL and Center for Brains, Minds and Machines.
His twin goals are to reverse-engineer distinctively human aspects of intelligence, and to use what we learn to build more human-like intelligence in machines. His scientific work currently focuses on two areas: describing the structure, content, and development of people’s core intuitive theories, especially intuitive physics and intuitive psychology, and understanding how people are able to learn and generalize new concepts, models, theories and tasks from very few examples -- often called "one-shot learning".
On the AI side, he and his group have developed widely used models for nonlinear dimensionality reduction, probabilistic programming, and Bayesian approaches to unsupervised learning, program induction and discovering the structural form of data. Josh and his students have received best paper or best student paper awards at many conferences (CogSci, CVPR, NIPS, UAI, RLDM, ICDL, SPP, RSS), and he is the recipient of the Howard Crosby Warren Medal from the Society of Experimental Psychologists, the Distinguished Scientific Award for Early Career Contribution to Psychology from the American Psychological Association, and the Troland Research Award from the National Academy of Sciences.
The University of Pennsylvania team seeks to mimic biological learning by applying an embodied, active, and efficient approach to acquiring representations of the surrounding world and actions.Read More
Lead Principal Investigator: Kostas Daniilidis
Kostas Daniilidis is the Ruth Yalom Stone Professor of Computer and Information Science at the University of Pennsylvania where he has been faculty since 1998. He is an IEEE Fellow.
He was the director of the GRASP laboratory from 2008 to 2013, Associate Dean for Graduate Education from 2012-2016, and Faculty Director of Online Learning since 2016. He obtained his undergraduate degree in Electrical Engineering from the National Technical University of Athens in 1986, and his PhD (Dr.rer.nat.) in Computer Science from the University of Karlsruhe in 1992, under the supervision of Hans-Hellmut Nagel. Kostas was Associate Editor of IEEE Transactions on Pattern Analysis and Machine Intelligence from 2003 to 2007. He co-chaired with Pollefeys IEEE 3DPVT 2006, and he was Program co-chair of ECCV 2010. He received the Best Conference Paper Award at ICRA 2017 and had Best Paper Finalist papers at IEEE CASE 2015 and RSS 2018. His most cited works have been on visual-odometry, omni-directional vision, 3D pose estimation, 3D registration, hand-eye calibration, structure from motion, and image matching. Kostas’ main interest today is in geometric deep learning, data association, event-based cameras, and vision based manipulation and navigation.
Principal Investigator: Jianbo Shi
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, for his thesis on Normalize Cuts image segmentation algorithm. Jianbo joined The Robotics Institute at Carnegie Mellon University in 1999 as a research faculty, where he led the Human Identification at Distance project, developing vision techniques for human identification and activity inference. In 2003 he joined the Department of Computer & Information Science at University of Pennsylvania where he is currently a professor. He is one of the most cited computer vision researchers and his 1998 work on Normalized Cuts received the Longuet-Higgins test of time award.
Jianbo's group is developing vision algorithms for both human and image recognition. Their ultimate goal is to develop computation algorithms to understand human behavior and interaction with objects in video, and to do so at multiple levels of abstractions: from the basic body limb tracking, to human identification, gesture recognition, and activity inference. Jianbo and his group are working to develop a visual thinking model that allows computers not only to understand their surroundings, but also to achieve higher level cognitive abilities such as machine memory and learning.
Principal Investigator: Vijay Balasubramanian
Vijay Balasubramanian is a theoretical physicist at the University of Pennsylvania, where he holds a chair as the Cathy and Marc Lasry Professor. After completing undergraduate degrees in Physics and Computer Science at MIT, and a Ph.D. in physics at Princeton, Vijay became a Junior Fellow of the Harvard Society of Fellows. For part of that time, he was also a Fellow-at-Large of the Santa Fe Institute. He spent the 2012-13 year at the École Normale Supérieure in Paris, where he held a fellowship from the Fondation Pierre Gilles de Gennes. He has been a visiting professor at the CUNY Graduate Center, Rockefeller University, and the Vrije Universiteit Brussel (Free University of Brussels), and a research associate at the International Center for Theoretical Physics in Trieste, Italy. He has received the Ira H. Abrams Memorial Award for Distinguishing Teaching, which is the highest teaching award given by the University of Pennsylvania’s School of Arts and Sciences.
Vijay’s research in particle physics and string theory has related to the origin of the thermodynamics of gravitating systems, the apparent loss of quantum information in the presence of black holes, and the role of quantum entanglement in creating the fabric of spacetime. He pursues this research as part of the “It From Qubit” international collaboration between physicists and computer scientists that is supported by the Simons Foundation. In the neural and cognitive sciences, Vijay has written groundbreaking papers on geometrical and statistical mechanics approaches to learning theory, and has engaged with neurophysiological experiments to test the idea that retinal coding of visual signals into spikes is efficient, transmitting the maximum possible information at fixed metabolic cost. He has also written extensively on principles of neural circuit organization in many systems of the brain. His current work touches on problems ranging from the neural representation of space, to the unsupervised learning of abstract auditory objects from natural stimuli, to the neural circuitry that sustains the sense of smell.