by A Müller
Abstract:
One of the oldest dreams of Artificial Intelligence is the realization of autonomous robots that achieve a level of problem-solving competency comparable to humans. Human problem-solving capabilities are particularly impressive in the context of everyday ac- tivities such as performing household chores: people are able to deal with ambiguous and incomplete information, they adapt their plans to different environments and specific sit- uations achieving intuitively almost optimal behavior, they cope with interruptions and failures and manage multiple interfering jobs. The investigations presented in this work make substantial progress in the direction of building robots that show similar behavior. This thesis addresses the problem of competently accomplishing everyday manipu- lation activities, such as setting the table and preparing meals, as a plan-based control problem. In plan-based control, robots do not only execute their programs but also reason about and modify them. We propose TRANER (Transformational Planner) as a suitable planning system for the optimization of everyday manipulation activities. TRANER real- izes planning through a generate-test cycle in which plan revision rules propose alternative plans and new plans are simulated in order to test and evaluate them. The unique features of TRANER are that it can realize very general and abstract plan revisions such as "stack objects before carrying them instead of handling them one by one" and that it successfully operates on plans in a way that they generate reliable, flexible, and efficient robot behavior in realistic simulations. The key contributions of this dissertation are threefold. First, it extends the plan rep- resentation to support the specification of robust and transformable plans. Second, it pro- poses a library of general and flexible plans for a household robot, using the extended plan representation. Third, it establishes a powerful, yet intuitive syntax for transforma- tion rules together with a set of general transformation rules for optimizing pick-and-place tasks in an everyday setting using the rule language. The viability and strength of the approach is empirically demonstrated in comprehen- sive and extensive experiments in a simulation environment with realistically simulated action and sensing mechanisms. The experiments show that transformational planning is necessary to tailor the robot's activities and that it is capable of substantially improving the robot's performance.
Reference:
Transformational Planning for Autonomous Household Robots using Libraries of Robust and Flexible Plans (A Müller), PhD thesis, Technische Universität München, 2008.
Bibtex Entry:
@phdthesis{muller_transformational_2008,
author = {A Müller},
title = {Transformational Planning for Autonomous Household Robots using Libraries
of Robust and Flexible Plans},
school = {Technische Universität München},
year = {2008},
abstract = {One of the oldest dreams of Artificial Intelligence is the realization
of autonomous robots that achieve a level of problem-solving competency
comparable to humans. Human problem-solving capabilities are particularly
impressive in the context of everyday ac- tivities such as performing
household chores: people are able to deal with ambiguous and incomplete
information, they adapt their plans to different environments and
specific sit- uations achieving intuitively almost optimal behavior,
they cope with interruptions and failures and manage multiple interfering
jobs. The investigations presented in this work make substantial
progress in the direction of building robots that show similar behavior.
This thesis addresses the problem of competently accomplishing everyday
manipu- lation activities, such as setting the table and preparing
meals, as a plan-based control problem. In plan-based control, robots
do not only execute their programs but also reason about and modify
them. We propose {TRANER} (Transformational Planner) as a suitable
planning system for the optimization of everyday manipulation activities.
{TRANER} real- izes planning through a generate-test cycle in which
plan revision rules propose alternative plans and new plans are simulated
in order to test and evaluate them. The unique features of {TRANER}
are that it can realize very general and abstract plan revisions
such as "stack objects before carrying them instead of handling them
one by one" and that it successfully operates on plans in a way that
they generate reliable, flexible, and efficient robot behavior in
realistic simulations. The key contributions of this dissertation
are threefold. First, it extends the plan rep- resentation to support
the specification of robust and transformable plans. Second, it pro-
poses a library of general and flexible plans for a household robot,
using the extended plan representation. Third, it establishes a powerful,
yet intuitive syntax for transforma- tion rules together with a set
of general transformation rules for optimizing pick-and-place tasks
in an everyday setting using the rule language. The viability and
strength of the approach is empirically demonstrated in comprehen-
sive and extensive experiments in a simulation environment with realistically
simulated action and sensing mechanisms. The experiments show that
transformational planning is necessary to tailor the robot's activities
and that it is capable of substantially improving the robot's performance.},
url = {http://mediatum2.ub.tum.de/node?id=645588},
}