This seminar has been cancelled.
Hackerman Hall B-17
Robots hold promise in assisting people in a variety of domains including healthcare services, household chores, collaborative manufacturing, and educational learning. In supporting these activities, robots need to engage with humans in socially cooperative interactions in which they work together toward a common goal in a socially intuitive manner. Such interactions require robots to coordinate actions, predict task intent, direct attention, and convey relevant information to human partners. In this talk, I will present how techniques in human-computer interaction, artificial intelligence, and robotics can be applied in a principled manner to create and study socially cooperative interactions between humans and robots. I will demonstrate social, cognitive, and task benefits of effective human-robot teams in various application contexts. I will also describe my current research that focuses on building socially cooperative robots to facilitate behavioral intervention for children with autism spectrum disorders (ASD). I will discuss broader impacts of my research, as well as future directions of my research program to develop personalized social technologies.
Chien-Ming Huang is a Postdoctoral Associate in the Department of Computer Science at Yale University, leading the NSF Expedition project on Socially Assistive Robotics. Dr. Huang received his Ph.D. in Computer Science at the University of Wisconsin–Madison in 2015, his M.S. in Computer Science at the Georgia Institute of Technology in 2010, and his B.S. in Computer Science at National Chiao Tung University in Taiwan in 2006. Dr. Huang’s research has been published at selective conferences such as HRI (Human-Robot Interaction) and RSS (Robotics: Science and Systems). His research has also been awarded a Best Paper Runner-Up at RSS 2013 and has received media coverage from MIT Technology Review, Tech Insider, and Science Nation. In 2016, Dr. Huang was invited to give an RSS early career spotlight talk at AAAI.
Hackerman Hall B-17
Hackerman Hall B-17
This talk will introduce a kinematic and dynamic framework for creating a representative model of an individual. Building on results from geometric robotics, a method for formulating a geometric dynamic identification model is derived. This method is validated on a robotic arm, and tested on healthy and muscular dystrophy subjects to determine the utility as a clinical tool. In order to capture kinematics of the human body we used Visual observations, either motion capture or the Kinect camera. In order to obtain the dynamical parameters of the individual, we used force plate and force sensors for robot attached to human hand. The work in progress is to use Ultrasound scanner and Acoustic myography in order to estimate the muscle strength. Our current representative kinematic and dynamic model outperformed conventional height/mass scaled models. This allows us for rapid, quantitative measurements of an individual, with minimal retraining required for clinicians. These tools are then used to develop a prescriptive model for developing assistive devices. This framework is then used to develop a novel system for human assistance. A prototype device is developed and tested. The prototype is lightweight, uses minimal energy, and can provide an augmentation of 82% for providing hammer curl assistance.
Ruzena Bajcsy (LF’08) received the Master’s and Ph.D. degrees in electrical engineering from Slovak Technical University, Bratislava, Slovak Republic, in 1957 and 1967, respectively, and the Ph.D. in computer science from Stanford University, Stanford, CA, in 1972. She is a Professor of Electrical Engineering and Computer Sciences at the University of California, Berkeley, and Director Emeritus of the Center for Information Technology Research in the Interest of Science (CITRIS). Prior to joining Berkeley, she headed the Computer and Information Science and Engineering Directorate at the National Science Foundation. Dr. Bajcsy is a member of the National Academy of Engineering and the National Academy of Science Institute of Medicine as well as a Fellow of the Association for Computing Machinery (ACM) and the American Association for Artificial Intelligence.