![\"Photo](\"https:\/\/cdn.vanderbilt.edu\/vu-URL\/wp-content\/uploads\/sites\/193\/2019\/03\/19170928\/ron-alterovitz-300x220.jpg\")
<\/p>\nSpeaker: Ron Alterovitz, Ph.D.
\nAssociate Professor
\nDepartment of Computer Science
\nUniversity of North Carolina at Chapel Hill<\/p>\n
<\/p>\n
Date:<\/strong> Thursday, February 23, 2017
\nTime:<\/b><\/span> 12:20pm start, 12:15pm lunch
\n<\/b><\/span>Place:<\/b><\/span>\u00a0 Stevenson Center 5326
\n<\/span>
\n\u00a0<\/span>Abstract:<\/b><\/span>\u00a0 Advances in robotics have the potential to improve healthcare delivery, from enabling surgical procedures that are beyond current clinical capabilities to autonomously assisting people with daily tasks in their homes. In this talk, we will discuss new algorithms to enable medical and assistive robots to safely and semi-autonomously operate inside people’s bodies or homes. These algorithms must effectively leverage human expertise, integrate data from imaging and other sensors, compensate for uncertainty due to variability in humans and the environment, consider deformations of soft tissues, and guarantee safety.\u00a0 First, I will present new algorithms for two emerging medical devices, steerable needles and concentric tube robots, designed for image-guided neurosurgery and pulmonary procedures. These devices can maneuver around anatomical obstacles to perform procedures at sites inaccessible to traditional straight instruments. To ensure patient safety, our algorithms explicitly consider uncertainty in motion and sensing to maximize the probability of avoiding obstacles and successfully accomplishing the task. We compute motion policies by integrating sampling-based motion planners, optimal control, and parallel computation. We will also discuss our new algorithms for autonomous robotic assistance for tasks of daily living in the home. I will present demonstration-guided motion planning, an approach in which the robot first learns an assistive task from human-conducted demonstrations and then autonomously plans motions to accomplish the learned task in new, dynamic environments with never-before-seen obstacles.<\/span><\/p>\n