Calendar
Abstract
The humanoid robot DURUS was unveiled to the public in the midst of the DARPA Robotics Challenge (DRC). While the main competition took place in the stadium, DURUS took part in the Robot Endurance Test with the goal of demonstrating locomotion that is an order of magnitude more efficient than existing bipedal walking on humanoid robots, e.g., the ATLAS robot utilized in the DRC. During this accessible public demonstration of humanoid robotic walking, DURUS walked continuously for over 2½ hours covering over 2 km—all on a single 1.1 kWh battery. At the core of this success was a methodology for designing and realizing dynamic and efficient walking gaits on bipedal robots through a mathematical framework that utilizes hybrid systems models coupled with nonlinear controllers that provably result in stable locomotion. This mathematical foundation allowed for the full utilization of novel mechanical components of DURUS, including: efficient cycloidal gearboxes (allowing for almost lossless transmission of power) and compliant elements at the ankles (absorbing the impacts at foot-strike). Through this combination of formal controller design and novel mechanical design, the humanoid robot DURUS was able to achieve the most efficient walking ever recorded on a humanoid robot. This talk will outline the key elements of the methodology used to achieve this result, demonstrate the extensibility to other bipedal robots and robotic assistive devices, e.g., prostheses, and consider the question: when will the humanoid robots of science fiction become science fact?
Speaker Bio
Aaron D. Ames joined the Georgia Institute of Technology in July 2015 as an Associate Professor in the George W. Woodruff School of Mechanical Engineering and the School of Electrical and Computer Engineering. Prior to joining Georgia Tech, he was an Associate Professor and Morris E. Foster Faculty Fellow II in Mechanical Engineering at Texas A&M University, with joint appointments in Electrical & Computer Engineering and Computer Science & Engineering. Dr. Ames received a B.S. in Mechanical Engineering and a B.A. in Mathematics from the University of St. Thomas in 2001, and he received a M.A. in Mathematics and a Ph.D. in Electrical Engineering and Computer Sciences from UC Berkeley in 2006. He served as a Postdoctoral Scholar in Control and Dynamical Systems at the California Institute of Technology from 2006 to 2008. At UC Berkeley, he was the recipient of the 2005 Leon O. Chua Award for achievement in nonlinear science and the 2006 Bernard Friedman Memorial Prize in Applied Mathematics. Dr. Ames received the NSF CAREER award in 2010 for his research on bipedal robotic walking and its applications to prosthetic devices, and is the recipient of the 2015 Donald P. Eckman Award recognizing an outstanding young engineer in the field of automatic control. His lab designs, builds and tests novel bipedal robots, humanoids and prostheses with the goal of achieving human-like bipedal robotic walking and translating these capabilities to robotic assistive devices.
Abstract
We are entering an era where people will interact with smart machines to enhance the physical aspects of their lives, just as smart mobile devices have revolutionized how we access and use information. Robots already provide surgeons with physical enhancements that improve their ability to cure disease, we are seeing the first generation of robots that collaborate with humans to enhance productivity in manufacturing, and a new generation of startups are looking at ways to enhance our day to day existence through automated driving and delivery.
In this talk, I will use examples from surgery and manufacturing to frame some of the broad science, technology, and commercial trends that are converging to fuel progress on human-machine collaborative systems. I will describe how surgical robots can be used to observe surgeons “at work” and to define a “language of manipulation” from data, mirroring the statistical revolution in speech processing. With these models, it is possible to recognize, assess, and intelligently augment surgeons’ capabilities. Beyond surgery, new advances in perception, coupled with steadily declining costs and increasing capabilities of manipulation systems, have opened up new science and commercialization opportunities around manufacturing assistants that can be instructed “in-situ.” Finally, I will close with some thoughts on the broader challenges still be to surmounted before we are able to create true collaborative partners.
Speaker Bio
Gregory D. Hager is the Mandell Bellmore Professor of Computer Science at Johns Hopkins University. His research interests include collaborative and vision-based robotics, time-series analysis of image data, and medical applications of image analysis and robotics. He has published over 300 articles and books in these areas. Professor Hager is also Chair of the Computing Community Consortium, a board member of the Computing Research Association, and is currently a member of the governing board of the International Federation of Robotics Research. In 2014, he was awarded a Hans Fischer Fellowship in the Institute of Advanced Study of the Technical University of Munich where he also holds an appointment in Computer Science. He is a fellow of the IEEE for his contributions to Vision-Based Robotics, and has served on the editorial boards of IEEE TRO, IEEE PAMI, and IJCV. Professor Hager received his BA in Mathematics and Computer Science Summa Cum Laude at Luther College (1983), and his MS (1986) and PhD (1988) from the University of Pennsylvania. He was a Fulbright Fellow at the University of Karlsruhe, and was on the faculty of Yale University prior to joining Johns Hopkins. He is founding CEO of Clear Guide Medical.
Nobuhiko Sugano “CAOS in THA”
Abstract
Various systems of computer-assisted orthopaedic surgery (CAOS) for total hip arthroplasty (THA) have been developed since the early 1990’s. These included computer assisted preoperative planning, robotic devices, navigation, and patient specific surgical templates. The first clinically applied system was an active robotic system (ROBODOC) which performed femoral implant cavity preparation as programmed preoperatively. Several reports on cementless THA with ROBODOC showed better stem alignment and less variance in limb-length inequality on radiographic evaluation, less incidence of pulmonary embolic events trans-esophageal cardio-echogram, and less stress shielding on DEXA analysis than conventional manual methods. On the other hand, some studies raise issues with active systems including a steep learning curve, muscle and nerve damage, and technical complications such as a procedure stop due to a bone motion during cutting requiring re-registration and registration failure. Semi-active robotic systems such as Acrobot and Rio were developed for ease of surgeon acceptance. The drill bit at the tip of the robotic arm is moved by a surgeon’s hand, but it does not move outside of a milling path boundary which is defined according to 3D-image-based preoperative planning.
Thanks to advances in 3D sensor technology, navigation systems were developed. Navigation is a passive system which does not perform any actions on patients. It only provides information and guidance to the surgeon who still uses conventional tools to perform the surgery. There are three types of navigation; CT-based navigation, imageless navigation, and fluoro-navigation. CT-based navigation is the most accurate, but the preoperative planning on CT images takes time that increases cost and radiation exposure. Imageless navigation does not use CT images, but its accuracy depends on the technique of landmark pointing and it does not take into account the individual uniqueness of anatomy. Fluoroscopic navigation is good for trauma and spine surgeries, but its benefits are limited in hip and knee reconstruction surgeries. Several studies have shown that cup alignment with navigation is more precise than conventional mechanical instruments, and that it is useful for optimizing limb length, range of motion, and stability. Recently, patient specific templates based on CT images have attracted attention and some early reports on cup placement and resurfacing showed improved accuracy of the procedures. These various CAOS systems have pros and cons. Nonetheless, CAOS is a useful tool to help surgeons perform accurately what surgeons want to do in order to better achieve their clinical objectives. Thus, it is important that the surgeon fully understands what he or she should be trying to achieve in THA for each patient.
Bio
Dr Nobuhiko Sugano received MD (1985) and PhD (1994) from Osaka University Graduate School of Medicine in Japan. In 1995 he was invited to Baylor College of Medicine in Houston, Texas, USA where he actively worked on three dimensional morphologic analysis of hip joint as Assistant Professor with Prof Philip C Noble. Those works were employed in design of hip joint implants for Japanese patients. In 2002, he received Otto-AuFranc award from the Hip Society for the research. Those computer research works also leaded him to play the role of leading orthopaedic surgeon in the area of computer-aided surgery from 1997. He developed a CT-based navigation system with his colleagues at Osaka University, and its clinical trial started in 1998. He also conducted several clinical studies using ROBODOC. His main interest in this area is robotics, navigation and postoperative motion analysis for arthroplasty. He received Maurice E. Müller Award for Excellence in Computer Assisted Surgery in 2011. He is currently a professor of Department of Orthopaedic Medical Engineering in Osaka University Graduate School of Medicine and Osaka University Hospital.
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Yoshinobu Sato: “Automated musculoskeletal segmentation and THA planning from CT images”
Bio
Yoshinobu Sato received his B.S., M.S. and Ph.D degrees in Information and Computer Sciences from Osaka University, Japan in 1982, 1984, 1988 respectively. From 1988 to 1992, he was a Research Engineer at the NTT Human Interface Laboratories. In 1992, he joined the Division of Functional Diagnostic Imaging of Osaka University Medical School. From 1996 to 1997, he was a Research Fellow in the Surgical Planning Laboratory, Harvard Medical School and Brigham and Women’s Hospital. From 1999 to 2014, He was an Associate Professor at Osaka University Graduate School of Medicine. From 2014, he is a Professor of Information Science at Nara Institute of Science and Technology (NAIST), Japan. His research interests include medical image analysis, computer assisted surgery, and computational anatomy. Dr. Sato was Program Chair of MICCAI 2013. He is a member of IEEE, MICCAI, and CAOS-International, and an editorial board member of Medical Image Analysis journal and International Journal of Computer Assisted Radiology and Surgery.
Abstract
Increasingly, robotic technologies are targeting the general public. The adoption of these technologies depends on understanding the human-machine user experience. Research in how users learn to use a technology (learning curves) and how to train users (training methodologies) are crucial in driving its success. Intuitive Surgical’s da Vinci surgical system is an interesting case study on how a complex machine can positively impact a highly technical and sensitive field – surgery. To augment the hands-on training of the da Vinci, the company introduced the VR-based da Vinci Skills Simulator. The Skills Simulator provides hands-on technical training on the surgeon console. In addition to the cost and time benefits of training on the simulator, it also provides various forms of feedback and evaluation. This talk will discuss the research that goes into developing an effective VR-based surgical simulator – from designing modules with clear training goals to developing proper metrics for feedback and analysis to implementing proper scoring systems to scientifically validating the modules.
Bio
Henry Lin manages the Surgical Simulation Development and Research Team at Intuitive Surgical. He started in the Medical Research Group investigating surgical skill evaluation. He then moved to the Simulation Group to apply his research within the surgical simulation environment. He received his Ph.D. from Johns Hopkins University in 2010 – in the Computational Interaction and Robotics Lab under the guidance of Dr. Gregory Hager. His dissertation research, “Surgical Motion”, focused on understanding surgeon technical skill through the analysis of da Vinci kinematics data and video. He received the 2005 MICCAI Best Student Paper Award and the Link Fellowship for his work. Post-JHU, Dr. Lin spent 2 years as a Post-Doc in the NIAAA at NIH understanding brain morphology changes due to alcohol abuse. He maintains his academic interests – publishing research manuscripts, serving as a reviewer for technical conferences, including MICCAI and M2CAI, and reviews for Intuitive Surgical’s clinical and technical grant programs. Dr. Lin also has degrees in Computer Science from Columbia University and Carnegie Mellon University.
Abstract
The increasing emergence of robotic technologies that serve as automated tools, assistants, and collaborators promises tremendous benefits in everyday settings from the home to manufacturing facilities. While robotic technologies promise interactions that can be far more complex than those with conventional ones, their successful integration into the human environment requires these interactions to also be natural and intuitive. To achieve complex but intuitive interactions, designers and developers must simultaneously understand and address human and computational challenges. In this talk, I will present my group’s work on building human-centered guidelines, methods, and tools to address these challenges in order to facilitate the design of robotic technologies that are more effective, intuitive, acceptable, and even enjoyable. In particular, through a series of projects, this work demonstrates how a marrying of knowledge about people and computational methods can enable effective user interactions with social, assistive, and telepresence robots and the development of novel tools and methods that support complex design tasks across the key stages of the design process. The talk will also include our ongoing work that applies these guidelines to the development of real-world applications of robotic technology and that targets the successful integration of these technologies into everyday settings.
Bio
Bilge Mutlu is an associate professor of computer science, psychology, and industrial engineering at the University of Wisconsin–Madison. He received his Ph.D. degree from Carnegie Mellon University’s Human-Computer Interaction Institute in 2009. His background combines training in interaction design, human-computer interaction, and robotics with industry experience in product design and development. Dr. Mutlu is a former Fulbright Scholar and the recipient of the NSF CAREER award as well as several best paper awards and nominations, including HRI 2008, HRI 2009, HRI 2011, UbiComp 2013, IVA 2013, RSS 2013, HRI 2014, CHI 2015, and ASHA 2015. His research has been covered by national and international press including the NewScientist, MIT Technology Review, Discovery News, Science Nation, and Voice of America. He has served in the Steering Committee of the HRI Conference and the Editorial Board of IEEE Transactions on Affective Computing, co-chairing the Program Committees for ROMAN 2016, HRI 2015, ROMAN 2015, and ICSR 2011, the Program Sub-committees on Design for CHI 2013 and CHI 2014, and the organizing committee for HRI 2017.
Abstract
The goal of my research is to develop algorithmic and theoretical techniques that push highly agile robotic systems to the brink of their hardware limits while guaranteeing that they operate in a safe manner despite uncertainty in the environment and dynamics. In this talk, I will describe my work on algorithms for the synthesis of feedback controllers that come with associated formal guarantees on the stability of the robot and show how these controllers and certificates of stability can be used for robust planning in environments previously unseen by the system. In order to make these results possible, my work connects deeply to computational tools such as sums-of-squares (SOS) programming and semi-definite programming from the theory of mathematical optimization, along with approaches from nonlinear control theory.
I will describe this work in the context of the problem of high-speed unmanned aerial vehicle (UAV) flight through cluttered environments previously unseen by the robot. In this context, the tools I have developed allow us to guarantee that the robot will fly through its environment in a collision-free manner despite uncertainty in the dynamics (e.g., wind gusts or modeling errors). The resulting hardware demonstrations on a fixed-wing airplane constitute one of the first examples of provably safe and robust control for robotic systems with complex nonlinear dynamics that need to plan in real time in environments with complex geometric constraints.
Bio
Anirudha Majumdar is a Ph.D. candidate in the Electrical Engineering and Computer Science department at MIT. He is a member of the Robot Locomotion Group at the Computer Science and Artificial Intelligence Lab and is advised by Prof. Russ Tedrake. Ani received his undergraduate degree in Mechanical Engineering and Mathematics from the University of Pennsylvania, where he was a member of the GRASP lab. His research is primarily in robotics: he works on algorithms for controlling highly dynamics robots such as unmanned aerial vehicles with formal guarantees on the safety of the system. Ani’s research has been recognized by the Siebel Foundation Scholarship and the Best Conference Paper Award at the International Conference on Robotics and Automation (ICRA) 2013.
Abstract
Robotic surgical systems are becoming widely used in various surgical specialties ranging from urologic and gynecological surgery to cardiothoracic surgery. The state-of-the-art robotic surgical systems represent tremendous improvements over manual minimally invasive surgery, with 6 degrees-of-freedom manipulators that provide improved dexterity, and immersive interfaces that provide improved hand-eye coordination. These systems are also becoming platforms for information augmentation. However, the state-of-the-art robotic surgical systems still have substantial shortcomings: Robotic surgical manipulations are slower than open surgical manipulations. Robotic surgical systems lack high fidelity haptic feedback. And, issues with situational awareness still remain.
In this talk, I will introduce the current state of our research towards the development of intelligent robotic surgical assistants. The goal of this research is to develop robotic surgical systems that can act more like surgical assistants and less like master-slave controlled tools. In the proposed paradigm, the robotic surgical system will have subtask automation capabilities for performing basic low-level manipulation tasks. The subtask automation will allow the surgeon to have a high-level interaction with the system rather than controlling it through low-level direct teleoperation. Such a system would potentially reduce tedium from simple, repetitive tasks; assist the surgeon in complex manipulation tasks; and reduce the cognitive load on the surgeon.
Automated execution of surgical tasks requires development of new robotic planning, perception, and control algorithms for robustly performing robotic manipulation under substantial uncertainty. The presentation will introduce our recent work on these three aspects of the problem. I will first focus on our research on robotic perception algorithms. Specifically, I will present algorithms for estimation of deformable object boundary constraints and material parameters, and for localization and tracking of surgical thread. I will then introduce our work on planning algorithms, which focus on needle path planning for surgical suturing, and optimal needle grasp and entry port planning. Finally, I will present control algorithms for needle driving and knot tying.
Bio
- Cenk Cavusoglu is currently a Professor at the Department of Electrical Engineering and Computer Science of Case Western Reserve University (CWRU), with secondary appointments in Biomedical Engineering, and Mechanical and Aerospace Engineering. He received the B.S. degree in Electrical and Electronic Engineering from the Middle East Technical University, Ankara, Turkey, in 1995, and the M.S. and Ph.D. degrees in Electrical Engineering and Computer Sciences from the University of California, Berkeley, in 1997 and 2000, respectively. He was a Visiting Researcher at the INRIA Rhones-Alpes Research Center, Grenoble, France (1998); a Postdoctoral Researcher and Lecturer at the University of California, Berkeley (2000-2002); and, a Visiting Associate Professor at Bilkent University, Ankara, Turkey (2009-2010).
Dr. Cavusoglu’s research spans the general areas of robotics and human-machine interfaces with special emphasis on medical robotics, and haptics. Specifically, for the past twenty years, he has been conducting research on all of the different aspects of medical robotic systems from control, mechanism, and system design, to human-machine interfaces, haptics, and algorithms.
More information on Dr. Cavusoglu’s research can be found at his homepage at
http://engr.case.edu/cavusoglu_cenk/
