The Forum for Artificial Intelligence meets every other week (or so) to discuss scientific, philosophical, and cultural issues in artificial intelligence. Both technical research topics and broader inter-disciplinary aspects of AI are covered, and all are welcome to attend!

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Learning in Human-Robot Teams

Chad Jenkins   [homepage]

Brown University

A principal goal of robotics is to realize embodied systems that are effective collaborators for human endeavors in the physical world. Human-robot collaborations can occur in a variety of forms, including autonomous robotic assistants, mixed-initiative robot explorers, and augmentations of the human body. For these collaborations to be effective, human users must have the ability to realize their intended behavior into actual robot control policies. At run-time, robots should be able to manipulate an environment and engage in two-way communication in a manner suitable to their human users. Further, the tools for programming, communicating with, and manipulating using robots should be accessible to the diverse sets of technical abilities present in society.
Towards the goal of effective human-robot collaboration, our research has pursued the use of learning and data-driven approaches to robot programming, communication, and manipulation. Learning from demonstration (LfD) has emerged as a central theme of our efforts towards natural instruct of autonomous robots by human users. In robot LfD, the desired robot control policy is implicit in human demonstration rather than explicitly coded in a computer program.
In this talk, I will describe our LfD-based work in policy learning using Gaussian Process Regression and humanoid imitation learning through spatio-temporal dimension reduction. This work is supported by our efforts in markerless, inertial-based, and physics-based human kinematic tracking, notably our indoor-outdoor person following system developed in collaboration with iRobot Research. I will additionally argue that collaboration in human-robot teams can be modeled by Markov Random Fields (MRFs), allowing for unification of existing multi-robot algorithms, application of belief propagation, and faithful modeling of experimental findings from cognitive science. Time permitting, I will also discuss our work learning tactile and force signatures to distinguish successful versus unsuccessful grasping on the NASA Robonaut.

About the speaker:

Odest Chadwicke Jenkins, Ph.D., is an Assistant Professor of Computer Science at Brown University. Prof. Jenkins earned his B.S. in Computer Science and Mathematics at Alma College (1996), M.S. in Computer Science at Georgia Tech (1998), and Ph.D. in Computer Science at the University of Southern California (2003). In 2007, he received Young Investigator funding from the Office of Naval Research and the Presidential Early Career Award for Scientists and Engineers (PECASE) for his work in learning primitive models of human motion for humanoid robot control and kinematic tracking.

Statistical Relational Learning, Diagnosing Breast Cancer and Transfer Learning

Dr. Jesse Davis   [homepage]

University of Washington

Standard inductive learning makes two key assumptions about the structure of the data. First, it requires that all examples are independent and identically distributed (iid). Second, it requires that the training and test instances come from the same distribution. Decades of research have produced many sophisticated techniques for solving this task. Unfortunately, in real applications these assumptions are often violated. In the first part of this talk, I will motivate the need to handle non-iid data through the concrete task of predicting whether an abnormality on a mammogram is malignant. I will describe the SAYU algorithm, which automatically constructs relational features. Our system makes significantly more accurate predictions than both radiologists and other machine learning techniques on this task. Furthermore, we identified a novel feature that is indicative of malignancy. In the second part of this talk, I will discuss a transfer learning algorithm that removes both restrictions made by standard inductive learners. In shallow transfer, test instances are from the same domain, but have a different distribution. In deep transfer, test instances are from a different domain entirely (i.e., described by different predicates). Humans routinely perform deep transfer, but few learning systems are capable of it. I will describe an approach based on a form of second-order Markov logic, which discovers structural regularities in the source domain in the form of Markov logic formulas with predicate variables, and instantiates these formulas with predicates from the target domain. Using this approach, we have successfully transferred learned knowledge between a molecular biology domain and a Web one. The discovered patterns include broadly useful properties of predicates, like symmetry and transitivity, and relations among predicates, like various forms of homophily.

About the speaker:

Jesse Davis is a post-doctoral researcher at the University of Washington. He received his Ph.D in computer science at the University of Wisconsin - Madison in 2007 and a B.A. in computer science from Williams College in 2002. His research interests include statistical relational learning, transfer learning, inductive logic programming and data mining for biomedical domains.