Andreas Hertle – Publications
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2018
2017
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Andreas Hertle and Bernhard Nebel.
Identifying Good Poses When Doing Your Household Chores: Creation and Exploitation of Inverse Surface Reachability Maps.
In
Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017).
2017.
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In current approaches to combined task and motion planning, usually symbolic planning and sampling based motion-planning are integrated. One problem is here to come up with good samples. We address the problem of identifying useful poses for a robot close to working surfaces such as tables or shelves. Our approach is based on reachability inversion which answers the question: where should the robot be located in order to reach a certain object? We extend the concept from point-based objects to flat polygonal surfaces in order to enable the robot to have a a good grasping position for many objects. Our approach allows to quickly sample multiple distinct poses for the robot from an prior computed distribution. Further we show how sampling from an inverse reachability distribution can be integrated into a CTAMP system.
2016
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Christian Dornhege, Alexander Kleiner, Andreas Hertle and Andreas Kolling.
Multirobot Coverage Search in Three Dimensions.
Journal of Field Robotics 33 (4), pp. 537-558. 2016.
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Searching for objects and observing parts of a known environment efficiently is a fundamental problem in
many real-world robotic applications, e.g., household robots searching for objects, inspection robots searching
for leaking pipelines, and rescue robots searching for survivors after a disaster. We consider the problem of
identifying and planning sequences of sensor locations from which robot sensors can observe and cover complex
three-dimensional (3D) environments while traveling only short distances. Our approach is based on sampling
and ranking a large number of sensor locations for a 3D environment represented by an OctoMap. The visible
area from these sensor locations induces a minimal partition of the 3D environment that we exploit for planning
sequences of sensor locations with short travel times efficiently. We present multiple planning algorithms
designed for single robots and for multirobot teams. These algorithms include variants that are greedy, optimal,
or based on decomposing the planning problem into a set cover and traveling salesman problem. We evaluated
and compared these algorithms empirically in simulation and real-world robot experiments with up to four
robots. Our results demonstrate that, despite the intractability of the overall problem, computing and executing
effective solutions for multirobot coverage search in real 3D environments is feasible and ready for real-world
applications.
2014
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Armin Hornung, Sebastian Boettcher, Christian Dornhege, Andreas Hertle, Jonas Schlagenhauf and Maren Bennewitz.
Mobile Manipulation in Cluttered Environments with Humanoids: Itegrated Perception, Task Planning, and Action Execution.
In
Proceedings of the IEEE-RAS International Conference on Humanoid Robots (HUMANOIDS).
2014.
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To autonomously carry out complex mobile manipulation tasks, a robot control system has to integrate several components for perception, world modeling, action planning and replanning, navigation, and manipulation. In this paper, we present a modular framework that is based on the Temporal Fast Downward Planner and supports external modules to control the robot. This allows to tightly integrate individual sub-systems with the high-level symbolic planner and enables a humanoid robot to solve challenging mobile manipulation tasks. In the work presented here, we address mobile manipulation with humanoids in cluttered environments, particularly the task of collecting objects and delivering them to designated places in a home-like environment while clearing obstacles out of the way. We implemented our system for a Nao humanoid tidying up a room, i.e., the robot has to collect items scattered on the floor, move obstacles out of its way, and deliver the objects to designated target locations. Despite the limited sensing and motion capabilities of the low-cost platform, the experiments show that our approach results in reliable task execution by applying monitoring actions to verify object and robot states.
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Andreas Hertle, Christian Dornhege, Thomas Keller, Robert Mattmüller, Manuela Ortlieb and Bernhard Nebel.
An Experimental Comparison of Classical, FOND and Probabilistic Planning.
In
Proceedings of the 37th German Conference on Artificial Intelligence (KI 2014), pp. 297-308.
Springer 2014.
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Domain-independent planning in general is broadly applicable to a wide range of tasks.
Many formalisms exist that allow to describe different aspects of realistic problems.
Which one is best is not clear as usually more expressiveness comes with a cost in
planning time. Under the assumption that hard guarantees are not required, users are
faced with a decision between multiple approaches. In this work we study the effect of
nondeterministic action outcomes. As a generic model we use a probabilistic description
in the form of Markov Decision Processes (MDPs). We define abstracting translations
into a classical planning formalism and fully observable nondeterministic (FOND)
planning. Our goal is to give insight into how state-of-the-art systems perform on
different MDP planning domains. We evaluate those MDPs by running state-of-the-art
planning systems on the abstracted formalisms.
2013
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Christian Dornhege, Andreas Hertle and Bernhard Nebel.
Lazy Evaluation and Subsumption Caching for Search-Based Integrated Task and Motion Planning.
In
Proceedings of the IROS workshop on AI-based robotics.
2013.
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State of the art classical planning systems can efficiently solve large symbolic problem instances. Applying classical planning techniques to robotics is possible by in- tegrating geometric reasoning in the planning process. The problems that are solvable in this way are significantly smaller than purely logical formulations as many costly geometric calculations are requested by a planner. Therefore we aim to avoid those calculations while preserving correctness.
We address this problem with efficient caching techniques. Subsumption caching avoids costly computations by caching geometric queries and beyond answering the same queries also considers less or more constrained ones. Additionally, we describe a lazy evaluation technique that pushes applicability checks for successor states performing geometric queries to a later point. As we are interested in the performance of our planner not as a standalone component, but as part of an intelligent robotic system, we evaluate those techniques embedded in an integrated system during real-world mobile manipulation experiments.
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Tim Niemueller, Nichola Abdo, Andreas Hertle, Gerhard Lakemeyer, Wolfram Burgard and Bernhard Nebel.
Towards Deliberative Active Perception using Persistent Memory.
In
Proceedings of the IROS workshop on AI-based robotics.
2013.
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Task coordination for autonomous mobile service robots typically involves a substantial amount of background knowledge and explicit action sequences to acquire the relevant information nowadays. We strive for a system which, given a task, is capable of reasoning about task-relevant knowledge to automatically determine whether that knowledge is sufficient. If missing or uncertain, the robot shall decide autonomously on the actions to gain or improve that knowledge. In this paper we present our baseline system implementing the foundations for these capabilities. The robot has to analyze a tabletop scene and increase its object type confidence. It plans motions to observe the scene from multiple perspectives, combines the acquired data, and performs a recognition step on the merged input.
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Andreas Hertle and Christian Dornhege.
Efficient Extensible Path Planning on 3D Terrain Using Behavior Modules.
In
Proceedings of the European Conference on Mobile Robotics (ECMR).
2013.
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Bernhard Nebel, Christian Dornhege and Andreas Hertle.
How Much Does a Household Robot Need To Know In Order To Tidy
Up Your Home?
In
AAAI Workshop on Intelligent Robotic Systems.
AAAI Press 2013.
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Although planning for the tasks a household robot has to perform appears to be easy, there exists the problem that the robot is usually uncertain about the state of the household when starting to plan. For example, when getting the order of tidying up the kitchen, the robot does not know what objects it will have to put away and whether there are actually any objects that need to be put away. Furthermore, while sensing operations can provide more information about the environment, things can go wrong when executing an action.
In this paper, we try to identify conditions under which classical planning can be used in a replanning loop in order to solve the planning problem in nondeterminis- tic partially observable open domains. In particular, we will define completeness and soundness of replanning with respect to nondeterministic planning and we will identify a PSPACE-checkable condition that guarantees soundness.
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Christian Dornhege and Andreas Hertle.
Integrated Symbolic Planning in the Tidyup-Robot Project.
In
AAAI Spring Symposium - Designing Intelligent Robots: Reintegrating AI II.
AAAI Press 2013.
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2012
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Stefan Kohlbrecher, Karen Petersen, Gerald Steinbauer, Johannes Maurer, Peter Lepej, Suzana Uran, Rodrigo Ventura, Christian Dornhege, Andreas Hertle, Raymond Sheh and Johannes Pellenz.
Community-Driven Development of Standard Software Modules for Search and Rescue Robots.
In
Safety, Security and Rescue Robotics (SSRR).
2012.
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Andreas Hertle, Christian Dornhege, Thomas Keller and Bernhard Nebel.
Planning with Semantic Attachments: An Object-Oriented View.
In
Proceedings of the European Conference on Artificial Intelligence (ECAI).
2012.
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In recent years, domain-independent planning has been applied to
a rising number of real-world applications. Usually, the
description language of choice is PDDL. However, PDDL is not
suited to model all challenges imposed by real-world
applications. Dornhege et al. proposed semantic attachments to
allow the computation of Boolean fluents by external processes
called modules during planning. To acquire state information
from the planning system a module developer must perform manual
requests through a callback interface which is both
inefficient and error-prone.
In this paper, we present the Object-oriented Planning Language
OPL, which incorporates the structure and advantages of modern
object-oriented programming languages. We demonstrate how a
domain-specific module interface that allows to directly access
the planner state using object member functions is automatically
gen- erated from an OPL planning task. The generated
domain-specific interface allows for a safe and less error-prone
implementation of modules. We show experimentally that this
interface is more efficient than the PDDL-based module interface
of TFD/M.
