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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Ph.D. Dissertation Defense Announcement
Title: Autonomous Environment Manipulation to Facilitate Task Completion
Martin LevihnRobotics PhD CandidateSchool of Interactive ComputingDate: Tuesday, March 10th, 2015
Time: 10:30am-12:30pm EST
Location: College of Computing Building, Room 345
Committee:Dr. Henrik Christensen (Advisor), School of Interactive Computing, Georgia TechDr. Frank Dellaert, School of Interactive Computing, Georgia TechDr. Charles Isbell, School of Interactive Computing, Georgia TechDr. Magnus Egerstedt, School of Electrical and Computer Engineering, Georgia TechDr. Tomás Lozano-Pérez, Department of Electrical Engineering and Computer Science, MITDr. Leslie Kaelbling, Department of Electrical Engineering and Computer Science, MITAbstract:
A robot should be able to autonomously modify and utilize its environment to assist its task completion. While mobile manipulators and humanoid robots have both locomotion and manipulation capabilities, planning systems typically just consider one or the other. In traditional motion planning the planner attempts to find a collision free path from the robot's current configuration to some goal configuration. In general, this process entirely ignores the fact that the robot has manipulation capabilities. We argue that robots should use their manipulation capabilities to move or even use environment objects. This thesis aims at bringing robots closer to such capabilities. There are two primary challenges in developing practical systems that allow a real robotic system to tightly couple its manipulation and locomotion capabilities: the inevitable inaccuracies in perception as well as actuation that occur on physical systems, and the exponential size of the search space.To address these challenges, this thesis first extends the previously introduced domain of Navigation Among Movable Obstacles (NAMO), which allows a robot to move obstacles out of its way. We extend the NAMO domain to handle the underlying issue of uncertainty. In fact, this thesis introduces the first NAMO framework that allows a real robotic systems to consider sensing and action uncertainties while reasoning about moving objects out of the way. However, the NAMO domain itself has the shortcoming that it only considers a robot's manipulation capabilities in the context of clearing a path. This thesis therefore also generalizes the NAMO domain itself to the Navigation Using Manipulable Obstacles (NUMO) domain. We present a complete NUMO system that led a real humanoid robot to autonomously build itself a bridge to cross a gap and a stair step to get on a platform.
