Moritz Göbelbecker – Publikationen
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2015
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Johannes Aldinger, Robert Mattmüller und Moritz Göbelbecker.
Complexity Issues of Interval Relaxed Numeric Planning.
In
KI 2015: Advances in Artificial Intelligence
(KI 2015).
2015.
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Automated planning is computationally hard even in its most
basic form as STRIPS planning. We are interested in numeric
planning with instantaneous actions, a problem that is not
decidable in general. Relaxation is an approach to simplifying
complex problems in order to obtain guidance in the original
problem. We present a relaxation approach with intervals for
numeric planning and show that plan existence can be decided
in polynomial time for tasks where dependencies between
numeric effects are acyclic.
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Johannes Aldinger, Robert Mattmüller und Moritz Göbelbecker.
Complexity Issues of Interval Relaxed Numeric Planning.
In
Proceedings of the ICAPS-2015 Workshop on Heuristic and Search for Domain-Independent Planning (HSDIP 2015).
2015.
Superseded by the KI 2015 paper of the same name..
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Automated planning is a hard problem even in its most basic form
as STRIPS planning. We are interested in numeric planning tasks
with instantaneous actions, a problem which is not even
decidable in general. Relaxation is an approach to simplifying
complex problems in order to obtain guidance in the original
problem. In this paper we present a relaxation approach with
intervals for numeric planning and discuss the arising
complexity issues.
2013
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Alper Aydemir, Andrzej Pronobis, Moritz Göbelbecker und Patric Jensfelt.
Active Visual Object Search in Unknown Environments Using Uncertain Semantics.
IEEE Transactions on Robotics 29 (4), S. 986-1002. 2013.
2011
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Alper Aydemir, Moritz Göbelbecker, Andrzej Pronobis, Kristoffer Sjöö und Patric Jensfelt.
Plan-based Object Search and Exploration Using Semantic Spatial Knowledge in the Real World.
In
Proceedings of the 5th European Conference on Mobile Robotics (ECMR 2011).
2011.
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In this paper we present a principled planner based
approach to the active visual object search problem in unknown
environments. We make use of a hierarchical planner that combines
the strength of decision theory and heuristics. Furthermore, our
object search approach leverages on the conceptual spatial
knowledge in the form of object cooccurences and semantic place
categorisation. A hierarchical model for representing object
locations is presented with which the planner is able to perform
indirect search. Finally we present real world experiments to show
the feasibility of the approach.
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Moritz Göbelbecker, Charles Gretton und Richard W. Dearden.
A Switching Planner for Combined Task and Observation Planning.
In
Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI 2011).
2011.
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From an automated planning perspective the problem of
practical mobile robot control in realistic environments poses many
important and contrary challenges. On the one hand, the planning
process must be lightweight, robust, and timely. Over the lifetime of
the robot it must always respond quickly with new plans that
accommodate exogenous events, changing objectives, and the underlying
unpredictability of the environment. On the other hand, in order to
promote efficient behaviours the planning process must perform
computationally expensive reasoning about contingencies and possible
revisions of subjective beliefs according to quantitatively modelled
uncertainty in acting and sensing. Towards addressing these
challenges, we develop a continual planning approach that switches
between using a fast satisficing ``classical'' planner, to decide on
the overall strategy, and decision-theoretic planning to solve small
abstract subproblems where deeper consideration of the sensing model
is both practical, and can significantly impact overall
performance. We evaluate our approach in large problems from a
realistic robot exploration domain.
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Moritz Göbelbecker, Alper Aydemir, Andrzej Pronobis, Kristoffer Sjöö und Patric Jensfelt.
A Planning Approach to Active Visual Search in Large Environments.
In
Proceedings of the AAAI-11 Workshop on Automated Action Planning for Autonomous Mobile Robots (PAMR).
2011.
Workshop version of the ECMR11 paper "Plan-based Object Search and Exploration Using Semantic Spatial Knowledge in the Real World".
(Abstract einblenden)
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(PDF)
(BIB)
In this paper we present a principled planner based
approach to the active visual object search problem in unknown
environments. We make use of a hierarchical planner that combines
the strength of decision theory and heuristics. Furthermore, our
object search approach leverages on the conceptual spatial
knowledge in the form of object co-occurrences and semantic place
categorisation. A hierarchical model for representing object
locations is presented with which the planner is able to perform
indirect search. Finally we present real world experiments to show
the feasibility of the approach.
-
Moritz Göbelbecker, Charles Gretton und Richard W. Dearden.
A Switching Planner for Combined Task and Observation Planning.
In
Electronic Proceedings of the Workshop on Decision Making in Partially Observable, Uncertain Worlds: Exploring Insights from Multiple Communities at the Twenty-Second International Join Conference on Artificial Intelligence (DMPOUW 2011).
2011.
Workshop version of the AAAI11 paper of the same title..
(Abstract einblenden)
(Abstract ausblenden)
(PDF)
(BIB)
From an automated planning perspective the problem of
practical mobile robot control in realistic environments poses many
important and contrary challenges. On the one hand, the planning
process must be lightweight, robust, and timely. Over the lifetime of
the robot it must always respond quickly with new plans that
accommodate exogenous events, changing objectives, and the underlying
unpredictability of the environment. On the other hand, in order to
promote efficient behaviours the planning process must perform
computationally expensive reasoning about contingencies and possible
revisions of subjective beliefs according to quantitatively modelled
uncertainty in acting and sensing. Towards addressing these
challenges, we develop a continual planning approach that switches
between using a fast satisficing ``classical'' planner, to decide on
the overall strategy, and decision-theoretic planning to solve small
abstract subproblems where deeper consideration of the sensing model
is both practical, and can significantly impact overall
performance. We evaluate our approach in large problems from a
realistic robot exploration domain.
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Marc Hanheide, Charles Gretton, Richard Dearden, Nick Hawes, Jeremy Wyatt, Andrzej Pronobis, Alper Aydemir, Moritz Göbelbecker und Hendrik Zender.
Exploiting Probabilistic Knowledge under Uncertain Sensing for Efficient Robot Behaviour.
In
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence (IJCAI 2011).
2011.
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Robots must perform tasks efficiently and reliably
while acting under uncertainty. One way to achieve efficiency is to
give the robot common-sense knowledge about the structure of the
world. Reliable robot behaviour can be achieved by modelling the
uncertainty in the world probabilistically. We present a robot system
that combines these two approaches and demonstrate the improvements in
efficiency and reliability that result. Our first contribution is a
probabilistic relational model integrating common-sense knowledge
about the world in general, with observations of a particular
environment. Our second contribution is a continual planning system
which is able to plan in the large problems posed by that model, by
automatically switching between decision-theoretic and classical
procedures. We evaluate our system on object search tasks in two
different real-world indoor environments. By reasoning about the
trade-offs between possible courses of action with different
informational effects, and exploiting the cues and general structures
of those environments, our robot is able to consistently demonstrate
efficient and reliable goal-directed behaviour.
2010
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D. Skočaj, M. Kristan, A. Leonardis, M. Mahnič, A. Vrečko, M. Janíček, G.-J. M. Kruijff, P. Lison, M. Zillich, C. Gretton, M. Hanheide und Moritz Göbelbecker.
A system approach to interactive learning of visual concepts.
In
Tenth International Conference on Epigenetic Robotics (EPIROB 2010).
2010.
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In this work we present a system and underlying
mechanisms for continuous learning of visual concepts in dialogue
with a human.
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Marc Hanheide, Nick Hawes, Jeremy Wyatt, Moritz Göbelbecker, Michael Brenner, Kristoffer Sjöö, Alper Aydemir, Patric Jensfelt, Hendrik Zender und Geert-Jan Kruijff.
A Framework for Goal Generation and Management.
In
Proceedings of the AAAI Workshop on Goal-Directed Autonomy.
2010.
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Goal-directed behaviour is often viewed as an
essential char- acteristic of an intelligent system, but
mechanisms to generate and manage goals are often overlooked. This
paper addresses this by presenting a framework for autonomous goal
gener- ation and selection. The framework has been implemented as
part of an intelligent mobile robot capable of exploring unknown
space and determining the category of rooms au- tonomously. We
demonstrate the efficacy of our approach by comparing the
performance of two versions of our inte- grated system: one with
the framework, the other without. This investigation leads us
conclude that such a framework is desirable for an integrated
intelligent system because it re- duces the complexity of the
problems that must be solved by other behaviour-generation
mechanisms, it makes goal- directed behaviour more robust in the
face of a dynamic and unpredictable environments, and it provides
an entry point for domain-specific knowledge in a more general
system.
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Moritz Göbelbecker, Thomas Keller, Patrick Eyerich, Michael Brenner und Bernhard Nebel.
Coming Up with Good Excuses: What To Do When No Plan Can be Found.
In
Ronen Brafman, Héctor Geffner, Jörg Hoffmann und Henry Kautz (Hrsg.),
Proceedings of the 20th International Conference on Automated Planning and Scheduling
(ICAPS 2010), S. 81-88.
AAAI Press 2010.
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When using a planner-based agent architecture, many things can
go wrong. First and foremost, an agent might fail to execute
one of the planned actions for some reasons. Even more
annoying, however, is a situation where the agent is
incompetent, i.e., unable to come up with a plan. This
might be due to the fact that there are principal reasons that
prohibit a successful plan or simply because the task's
description is incomplete or incorrect. In either case, an
explanation for such a failure would be very helpful. We will
address this problem and provide a formalization of coming
up with excuses for not being able to find a plan. Based
on that, we will present an algorithm that is able to find
excuses and demonstrate that such excuses can be found in
practical settings in reasonable time.
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Nick Hawes, Marc Hanheide, Kristoffer Sjöö, Alper Aydemir, Patric Jensfelt, Moritz Göbelbecker, Michael Brenner, Hendrik Zender, Pierre Lison, Ivana Kruijff-Korbayov, Geert-Jan M. Kruijff und Michael Zillich.
Dora The Explorer: A Motivated Robot.
In
Proc. of 9th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2010).
2010.
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Dora the Explorer is a mobile robot with a sense of
curios- ity and a drive to explore its world. Given an incomplete
tour of an indoor environment, Dora is driven by internal
motivations to probe the gaps in her spatial knowledge. She
actively explores regions of space which she hasn't previously
visited but which she expects will lead her to further unex-
plored space. She will also attempt to determine the cate- gories
of rooms through active visual search for functionally important
objects, and through ontology-driven inference on the results of
this search.
2009
2005
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Alexander Kleiner, Michael Brenner, Tobias Braeuer, Christian Dornhege, Moritz Göbelbecker, Matthias Luber, Johann Prediger, Joerg Stueckler und Bernhard Nebel.
Successful Search and Rescue in Simulated Disaster Areas.
In
Proceedings of the International RoboCup Symposium '05.
Osaka, Japan 2005.
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RoboCupRescue Simulation is a large-scale multi-agent simulation
of urban disasters where, in order to save lives and minimize damage, rescue
teams must effectively cooperate despite sensing and communication limitations.
This paper presents the comprehensive search and rescue approach of the ResQ
Freiburg team, the winner in the RoboCupRescue Simulation league at RoboCup
2004.
Specific contributions include the predictions of travel costs and civilian lifetime,
the efficient coordination of an active disaster space exploration, as well as
an any-time rescue sequence optimization based on a genetic algorithm.
We compare the performances of our team and others in terms of their capability
of extinguishing fires, freeing roads from debris, disaster space exploration, and
civilian rescue. The evaluation is carried out with information extracted from
simulation log files gathered during RoboCup 2004. Our results clearly explain
the success of our team, and also confirm the scientific approaches proposed in
this paper.
2004
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Alexander Kleiner, Michael Brenner, Tobias Braeuer, Christian Dornhege, Moritz Göbelbecker, Matthias Luber, Johann Prediger und Joerg Stueckler.
ResQ Freiburg: Team Description and Evaluation, Team Description Paper, Rescue Simulation League.
In
CDROM Proceedings of the International RoboCup Symposium '04.
Lisbon, Portugal 2004.
(PDF)
