Organizers: Dieter Fox
Abstract: In this talk I present recent work from the Socially Intelligent Machines Lab at Georgia Tech. One of the focuses of our lab is on Socially Guided Machine Learning, building robot systems that can learn from everyday human teachers. We look at standard Machine Learning interactions and redesign interfaces and algorithms to support the collection of learning input from naive humans. This talk starts with an initial investigation comparing self and social learning which motivates our recent work on Active Learning for robots. Then, I will present results from a study of robot active learning, which motivates two challenges: getting interaction timing right, and asking good questions. To address the first challenge we are building computational models of reciprocal social interactions. And to address the second challenge we are developing algorithms for generating Active Learning queries in embodied learning tasks.
Biography: Dr. Andrea L. Thomaz is an Assistant Professor of Interactive Computing at the Georgia Institute of Technology. She directs the Socially Intelligent Machines lab, which is affiliated with the Robotics and Intelligent Machines (RIM) Center and with the Graphics Visualization and Usability (GVU) Center. She earned a B.S. in Electrical and Computer Engineering from the University of Texas at Austin in 1999, and Sc.M. and Ph.D. degrees from MIT in 2002 and 2006. Dr. Thomaz is published in the areas of Artificial Intelligence, Robotics, Human-Robot Interaction, and Human-Computer Interaction. She received an ONR Young Investigator Award in 2008, and an NSF CAREER award in 2010. Her work has been featured on the front page of the New York Times, and in 2009 she was named one of MIT Technology Review’s TR 35.
Abstract: Advances in machine learning, machine perception, neuroscience, and control theory are making possible the emergence of a new science of learning. This discipline could help us understand the role of learning in the development of human intelligence, and to create machines that can learn from experience and that can accelerate human learning and education. I will propose that key to this emerging science is the commitment to computational analysis, for which the framework of probability theory and stochastic optimal control is particularly well suited, and to the testing of theories using physical real time robotic implementations. I will describe our efforts to help understand learning and development from a computational point of view. This includes development of machine perception primitives for social interaction, development of social robots to enrich early childhood education, computational analysis of rich databases of early social behavior, and development of sophisticated humanoid robots to understand the emergence of sensory-motor intelligence in infants.
Abstract: In this talk I will show videos of complex motor behaviors synthesized automatically using new optimal control methods, and explain how these methods work. The behaviors include getting up from an arbitrary pose on the ground, walking, hopping, swimming, kicking, climbing, hand-stands, and cooperative actions. The synthesis methods fall in two categories. The first is online trajectory optimization or model-predictive control (MPC). The idea is to optimize the movement trajectory at every step of the estimation-control loop up to some time horizon (in our case about half a second), execute only the beginning portion of the trajectory, and repeat the optimization at the next time step (say 10 msec later). This approach has been used extensively in domains such as chemical process control where the dynamics are sufficiently slow and smooth to make online optimization possible. We have now developed a number of algorithmic improvements, allowing us to apply MPC to robotic systems. This requires a fast physics engine (for computing derivatives via finite differencing) which we have also developed. The second method is based on the realization that most movements performed on land are made for the purpose of establishing contact with the environment, and exerting contact forces. This suggests that contact events should not be treated as side-effects of multi-joint kinematics and dynamics, but rather as explicit decision variables. We have developed a method where the optimizer directly specifies the desired contact events, using continuous decision variables, and at the same time optimizes the movement trajectory in a way consistent with the specified contact events. This makes it possible to optimize movement trajectories with many contact events, without need for manual scripting, motion capture or fortuitous choice of "features".
Abstract: A central goal of artificial intelligence is the design of agents that can learn to achieve increasingly complex behavior over time. An important type of cumulative learning is the acquisition of procedural knowledge in the form of skills, allowing an agent to abstract away from low-level motor control and plan and learn at a higher level, and thus progressively improving its problem solving abilities and creating further opportunities for learning. I describe a robot system that learns to sequence innate controllers to solve a task, and then extracts components of that solution as transferable skills. The resulting skills improve the robot’s ability to learn to solve a second task. This system was developed by Dr. George Konidaris, who received the Ph.D. from the University of Massachusetts Amherst in 2010 and is currently a Postdoctoral Associate in the Learning and Intelligent Systems Group in the MIT Computer Science and Artificial Intelligence Laboratory.
Abstract: Robots are becoming more and more capable at reasoning about people, objects, and activities in their environments. The ability to extract high-level semantic information from sensor data provides new opportunities for human-robot interaction. One such opportunity is to explore interacting with robots via natural language. In this talk I will present our preliminary work toward enabling robots to interpret, or ground, natural language commands in robot control systems. We build on techniques developed by the semantic natural language processing community on learning grammars that parse natural language input to logic-based semantic meaning. I will demonstrate early results in two application domains: First, learning to follow natural language directions through indoor environments; and, second, learning to ground (simple) object attributes via weakly supervised training. Joint work with Luke Zettlemoyer, Cynthia Matuszek, Nicholas Fitzgerald, and Liefeng Bo. Support provided by Intel ISTC-PC, NSF, ARL, and ONR.
Abstract: Robot-assisted needle steering is a promising technique to improve the effectiveness of needle-based medical procedures by allowing redirection of a needle's path within tissue. Our robot employs a tip-based steering technique, in which the asymmetric tips of long, thin, flexible needles develop tip forces orthogonal to the needle shaft due to interaction with surrounding tissue. The robot steers a needle though two input degrees of freedom, insertion along and rotation about the needle shaft, in order to achieve six-degree-of-freedom positioning of the needle tip. A closed-loop system for asymmetric-tip needle steering was developed, including devices, models and simulations, path planners, controllers, and integration with medical imaging. I will present results from testing needle steering in artificial and biological tissues, and discuss ongoing work toward clinical applications. This project is a collaboration between researchers at Johns Hopkins University, UC Berkeley, and Stanford University.
Biography: Dr. Allison M. Okamura received the BS degree from the University of California at Berkeley in 1994, and the MS and PhD degrees from Stanford University in 1996 and 2000, respectively, all in mechanical engineering. She is currently Associate Professor in the mechanical engineering department at Stanford University. She was previously Professor and Vice Chair of mechanical engineering at Johns Hopkins University. She has been an associate editor of the IEEE Transactions on Haptics, an editor of the IEEE International Conference on Robotics and Automation Conference Editorial Board, and co-chair of the IEEE Haptics Symposium. Her awards include the 2009 IEEE Technical Committee on Haptics Early Career Award, the 2005 IEEE Robotics and Automation Society Early Academic Career Award, and the 2004 NSF CAREER Award. She is an IEEE Fellow. Her interests include haptics, teleoperation, virtual environments and simulators, medical robotics, neuromechanics and rehabilitation, prosthetics, and engineering education. For more information about our work, please see the Collaborative Haptics and Robotics in Medicine (CHARM) Laboratory website: http://charm.stanford.edu.
Abstract: Surgery is a demanding unstructured physical manipulation task involving highly trained humans, advanced tools, networked information systems, and uncertainty. This talk will review engineering and scientific research at the University of Washington Biorobotics Lab, aimed at better care of patients, including remote patients in extreme environments. The Raven interoperable robot surgery research system is a telemanipulation system for exploration and training in surgical robotics. We are currently near completion of seven "Raven-II" systems which will be deployed at leading surgical robotics research centers to create an interoperable network of testbeds. Highly effective and safe surgical teleoperation systems of the future will provide high quality haptic feedback. Research in systems theory and human perception addressing that goal will also be introduced.
Biography: Dr. Blake Hannaford, Ph.D., is Professor of Electrical Engineering, Adjunct Professor of Bioengineering, Mechanical Engineering, and Surgery at the University of Washington. He received the B.S. degree in Engineering and Applied Science from Yale University in 1977, and the M.S. and Ph.D. degrees in Electrical Engineering from the University of California, Berkeley, in 1982 and 1985 respectively. Before graduate study, he held engineering positions in digital hardware and software design, office automation, and medical image processing. At Berkeley he pursued thesis research in multiple target tracking in medical images and the control of time-optimal voluntary human movement. From 1986 to 1989 he worked on the remote control of robot manipulators in the Man-Machine Systems Group in the Automated Systems Section of the NASA Jet Propulsion Laboratory, Caltech. He supervised that group from 1988 to 1989. Since September 1989, he has been at the University of Washington in Seattle, where he has been Professor of Electrical Engineering since 1997, and served as Associate Chair for Education from 1999 to 2001. He was awarded the National Science Foundation's Presidential Young Investigator Award and the Early Career Achievement Award from the IEEE Engineering in Medicine and Biology Society and is an IEEE Fellow. His currently active interests include haptic displays on the Internet, and surgical robotics. He has consulted on robotic surgical devices with the Food and Drug Administration Panel on surgical devices.
Abstract: Electrolocation is used by the weakly electric fish of South America and Africa to navigate and hunt in murky water where vision is ineffective. These fish generate an AC electric field that is perturbed by objects nearby that differ in impedance from the water. Electroreceptors covering the body of the fish report the amplitude and phase of the local field. The animal decodes electric field perturbations into information about its surroundings. Electrolocation is fundamentally divergent from optical vision (and other imaging methods) that create projective images of 3D space. Current electrolocation methods are also quite different from electrical impedance tomography. We will describe current electrolocation technology, and progress on development of a propulsion system inspired by electric fish to provide the precise movement capabilities that this short-range sensing approach requires.
Biography: Dr. Malcolm MacIver is Associate Professor at Northwestern University with joint appointments in the Mechanical Engineering and Biomedical Engineering departments. He is interested in the neural and mechanical basis of animal behavior, evolution, and the implications of the close coupling of movement with gathering information for our understanding of intelligence and consciousness. He also develops immersive art installations that have been exhibited internationally.
Abstract: Programming robots is hard. While demonstrating a desired behavior may be easy, designing a system that behaves this way is often difficult, time consuming, and ultimately expensive. Machine learning promises to enable "programming by demonstration" for developing high-performance robotic systems. Unfortunately, many approaches that utilize the classical tools of supervised learning fail to meet the needs of imitation learning. I'll discuss the problems that result from ignoring the effect of actions influencing the world, and I'll highlight simple "reduction- based" approaches that, both in theory and in practice, mitigate these problems. I'll demonstrate the resulting approach on the development of reactive controllers for cluttered UAV flight and for video game systems. Additionally, robotic systems are often built atop sophisticated planning algorithms that efficiently reason far into the future; consequently, ignoring these planning algorithms in lieu of a supervised learning approach often leads to poor and myopic performance. While planners have demonstrated dramatic success in applications ranging from legged locomotion to outdoor unstructured navigation, such algorithms rely on fully specified cost functions that map sensor readings and environment models to a scalar cost. Such cost functions are usually manually designed and programmed. Recently, our group has developed a set of techniques that learn these functions from human demonstration by applying an Inverse Optimal Control (IOC) approach to find a cost function for which planned behavior mimics an expert's demonstration. These approaches shed new light on the intimate connections between probabilistic inference and optimal control. I'll consider case studies in activity forecasting of drivers and pedestrians as well as the imitation learning of robotic locomotion and rough-terrain navigation. These case-studies highlight key challenges in applying the algorithms in practical settings. J. Andrew Bagnell is an Associate Professor with the Robotics Institute, the National Robotics Engineering Center and the Machine Learning Department at Carnegie Mellon University. His research centers on the theory and practice of machine learning for decision making and robotics.
Biography: Dr. Bagnell directs the Learning, AI, and Robotics Laboratory (LAIRLab) within the Robotics Institute. Dr. Bagnell serves as the director of the Robotics Institute Summer Scholars program, a summer research experience in robotics for undergraduates throughout the world. Dr. Bagnell and his group's research has won awards in both the robotics and machine learning communities including at the International Conference on Machine Learning, Robotics Science and Systems, and the International Conference on Robotics and Automation. Dr. Bagnell's current projects focus on machine learning for dexterous manipulation, decision making under uncertainty, ground and aerial vehicle control, and robot perception. Prior to joining the faculty, Prof. Bagnell received his doctorate at Carnegie Mellon in 2004 with a National Science Foundation Graduate Fellowship and completed undergraduate studies with highest honors in electrical engineering at the University of Florida.