Ph.D Dissertation Defense: Martin Levihn

Title: Autonomous Environment Manipulation to Facilitate Task Completion

Date: Tuesday, March 10th, 2015
Time: 10:30am-12:30pm EST
Location: College of Computing Building, Room 345

Dr. Henrik Christensen (Advisor), School of Interactive Computing, Georgia
Dr. Frank Dellaert, School of Interactive Computing, Georgia Tech
Dr. Charles Isbell, School of Interactive Computing, Georgia Tech
Dr. Magnus Egerstedt, School of Electrical and Computer Engineering,
Georgia Tech
Dr. Tomás Lozano-Pérez, Department of Electrical Engineering and Computer
Science, MIT
Dr. Leslie Kaelbling, Department of Electrical Engineering and Computer
Science, MIT


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

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.