Robotic Systems / Neural Networks / MIT Press / Computer Sciences / Baxter / Pearson / Human-Robot Collaborative Systems / Intel / Vinci / / /
Event
Product Issues / /
Facility
UC Berkeley Center / Link¨oping University / /
IndustryTerm
offline and online parameter estimation / surgical tools / motion planning algorithm / elastic joint systems / vision applications / non-linear automation systems / non-linear systems / online model learning / industrial automation systems / nonlinear systems / i7 processor / automation systems / /
OperatingSystem
Mac OS X / /
Organization
Cambridge university / Division of Automatic Control and Communication Systems / MIT / U.S. National Science Foundation / Department of Mechanical Engineering / Link¨oping University / Department of Electrical Engineering and Computer Sciences / Department of Industrial Engineering and Operations Research / UC Berkeley Center for Information Technology / /
Person
J. Binney / S. Kosari / H. H. King / M. Kalakrishnan / D. C. Friedman / J. Kelly / G. Sukhatme / S. Schaal / Kan Anant / J. Ma / L. Righetti / Hawkeye King / P. Roan / Evan Chang-Siu / Ji Ma / L. White / Greg Hager / S. Ramadurai / Blake Hannaford / Jacob Rosen / L. Cheng / E. Naerum / D. Glozman / H. Chizeck / B. Hannaford / /
Learning Accurate Kinematic Control of Cable-Driven Surgical Robots Using Data Cleaning and Gaussian Process Regression Jeffrey Mahler1 , Sanjay Krishnan1 , Michael Laskey1 , Siddarth Sen1 , Adithyavairavan Murali1 , Ben