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Oliver Schrempf

Dipl.-Inform.

Academic Career

Research Interests

Systems Theory, Estimation Methods, Artificial Intelligence, Model Based Kognition

Publications

Informatics in Control, Automation and Robotics -- Selected Papers from the International Conference on Informatics in Control, Automation and Robotics 2007, 24:287-300, Springer, September, 2008.

URL

Author : Oliver C. Schrempf, Uwe D. Hanebeck
Title : Dirac Mixture Approximation for Nonlinear Stochastic Filtering
In : Informatics in Control, Automation and Robotics -- Selected Papers from the International Conference on Informatics in Control, Automation and Robotics 2007
Date : September 2008

Abstract

This work presents a filter for estimating the state of
nonlinear dynamic systems. It is based on optimal recursive approximation
the state densities by means of Dirac mixture functions in order to allow
for a closed form solution of the prediction and filter step. The
approximation approach is based on a systematic minimization of a distance
measure and is hence optimal and deterministic. In contrast to non-deterministic
methods we are able to determine the optimal number of components in the Dirac
mixture. A further benefit of the proposed approach is the consideration of
measurements during the approximation process in order to avoid parameter degradation.

Proceedings of the 2007 IEEE Conference on Decision and Control (CDC 2007), pp. 3065-3071, New Orleans, Louisiana, USA, December, 2007.

Author : Uwe D. Hanebeck, Oliver C. Schrempf
Title : Greedy Algorithms for Dirac Mixture Approximation of Arbitrary Probability Density Functions
In : Proceedings of the 2007 IEEE Conference on Decision and Control (CDC 2007)
Date : December 2007

Abstract

Greedy procedures for suboptimal Dirac mixture approximation of an
arbitrary probability density function are proposed, which approach
the desired density by sequentially adding one component at a time.
Similar to the batch solutions proposed earlier, a distance measure
between the corresponding cumulative distributions is minimized by
selecting the corresponding density parameters. This is due to the
fact, that a distance between the densities is typically not well
defined for Dirac mixtures. This paper focuses on the Cramer-von
Mises distance, a weighted integral quadratic distance measure between
the true distribution and its approximation. In contrast to the batch
solutions, the computational complexity is much lower and grows only
linearly with the number of components. Computational savings are
even greater, when the required number of components, e.g., the minimum
number of components for achieving a given quality measure, is not
a priori known and must be searched for as well. The performance
of the proposed sequential approximation approach is compared to
that of the optimal batch solution.

Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2007), pp. 1429-1434, San Diego, California, USA, November, 2007.

Author : Oliver C. Schrempf, David Albrecht, Uwe D. Hanebeck
Title : Tractable Probabilistic Models for Intention Recognition Based on Expert Knowledge
In : Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2007)
Date : November 2007

Abstract

Intention recognition is an important topic in human-robot cooperation
that can be tackled using probabilistic model-based methods. A popular
instance of such methods are Bayesian networks where the dependencies
between random variables are modeled by means of a directed graph.
Bayesian networks are very efficient for treating networks with conditionally
independent parts. Unfortunately, such independence sometimes has
to be constructed by introducing so called hidden variables with
an intractably large state space. An example are human actions which
depend on human intentions and on other human actions. Our goal in
this paper is to find models for intention-action mapping with a
reduced state space in order to allow for tractable on-line evaluation.
We present a systematic derivation of the reduced model and experimental
results of recognizing the intention of a real human in a virtual
environment.

Proceedings of the 2007 American Control Conference (ACC 2007), pp. 1768-1774, New York, New York, USA, July, 2007.

Author : Oliver C. Schrempf, Uwe D. Hanebeck
Title : Recursive Prediction of Stochastic Nonlinear Systems Based on Optimal Dirac Mixture Approximations
In : Proceedings of the 2007 American Control Conference (ACC 2007)
Date : July 2007

Abstract

This paper introduces a new approach to the recursive propagation
of probability density functions through discrete-time stochastic
nonlinear dynamic systems. An efficient recursive procedure is proposed
that is based on the optimal approximation of the posterior densities
after each prediction step by means of Dirac mixtures. The parameters
of the individual components are selected by systematically minimizing
a suitable distance measure in such a way that the future evolution
of the approximate densities is as close to the exact densities as
possible.

Proceedings of the 10th International Conference on Information Fusion (Fusion 2007), Quebec, Canada, July, 2007.

Author : Anne Hanselmann, Oliver C. Schrempf, Uwe D. Hanebeck
Title : Optimal Parametric Density Estimation by Minimizing an Analytic Distance Measure
In : Proceedings of the 10th International Conference on Information Fusion (Fusion 2007)
Date : July 2007

Abstract

In this paper, we present a novel approach to parametric density estimation
from given samples. The samples are treated as a parametric density
function by means of a Dirac mixture, which allows for applying analytic
optimization techniques. The method is based on minimizing a distance
measure between the integral of the approximation function and the
empirical cumulative distribution function (EDF) of the given samples,
where the EDF is represented by the integral of the Dirac mixture.
Since this minimization problem cannot be solved directly in general,
a progression technique is applied. Increased performance of the
approach in comparison to iterative maximum likelihood approaches
is shown in simulations.

Proceedings of the 4th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2007), SPSMC:54-61, Angers, France, May, 2007.

Author : Oliver C. Schrempf, Uwe D. Hanebeck
Title : A State Estimator for Nonlinear Stochastic Systems Based on Dirac Mixture Approximations
In : Proceedings of the 4th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2007)
Date : May 2007

Abstract

This paper presents a filter approach for estimating the state of
nonlinear dynamic systems based on recursive approximation of posterior
densities by means of Dirac mixture functions. The filter consists
of a prediction step and a filter step. The approximation approach
is based on a systematic minimization of a distance measure and is
hence optimal and deterministic. In contrast to non-deterministic
methods we are able to determine the optimal number of components
in the Dirac mixture. A further benefit of the proposed approach
is the consideration of measurements during the approximation process
in order to avoid parameter degradation.

Proceedings of the 2006 IEEE Conference on Decision and Control (CDC 2006), San Diego, California, USA, December, 2006.

Author : Oliver C. Schrempf, Dietrich Brunn, Uwe D. Hanebeck
Title : Density Approximation Based on Dirac Mixtures with Regard to Nonlinear Estimation and Filtering
In : Proceedings of the 2006 IEEE Conference on Decision and Control (CDC 2006)
Date : December 2006

Abstract

A deterministic procedure for optimal approximation of arbitrary probability
density functions by means of Dirac mixtures with equal weights is
proposed. The optimality of this approximation is guaranteed by minimizing
the distance of the approximation from the true density. For this
purpose a distance measure is required, which is in general not well
defined for Dirac mixtures. Hence, a key contribution is to compare
the corresponding cumulative distribution functions. This paper concentrates
on the simple and intuitive integral quadratic distance measure.
For the special case of a Dirac mixture with equally weighted components,
closed-form solutions for special types of densities like uniform
and Gaussian densities are obtained. Closed-form solution of the
given optimization problem is not possible in general. Hence, another
key contribution is an efficient solution procedure for arbitrary
true densities based on a homotopy continuation approach. In contrast
to standard Monte Carlo techniques like particle filters that are
based on random sampling, the proposed approach is deterministic
and ensures an optimal approximation with respect to a given distance
measure. In addition, the number of required components (particles)
can easily be deduced by application of the proposed distance measure.
The resulting approximations can be used as basis for recursive nonlinear
filtering mechanism alternative to Monte Carlo methods.

2nd International Workshop on Human Centered Robotic Systems (HCRS 2006), pp. 85-89, Munich, Germany, October, 2006.

Author : Patrick Rößler, Oliver C. Schrempf, Uwe D. Hanebeck
Title : Stochastic Prediction of Waypoints for Extended-Range Telepresence Applications
In : 2nd International Workshop on Human Centered Robotic Systems (HCRS 2006)
Date : October 2006

Abstract

The Motion Compression framework for extended range telepresence
applications consists of three functional modules:
path prediction, path transformation, and user guidance. This
paper presents a new path prediction module for known environments
that exploits the property, that humans typically
walk on straight paths toward discrete goal objects. In order
to estimate the user’s goal object out of a set of possible goals,
we derived a Bayesian filter that gives this discrete estimate
based on continuous measurements of the user’s head pose.

2nd International Workshop on Human Centered Robotic Systems (HCRS 2006), pp. 7-12, Munich, Germany, October, 2006.

Author : Andreas J. Schmid, Oliver C. Schrempf, Heinz Wörn, Uwe D. Hanebeck
Title : Towards Intuitive Human-Robot Cooperation
In : 2nd International Workshop on Human Centered Robotic Systems (HCRS 2006)
Date : October 2006

Abstract

Human-robot cooperation calls for the treatment of human-machine
communication channels, especially if humanoid robots
are involved. In this paper, we consider implicit nonverbal
channels given by recognizing the partner’s intention
and proactive execution of tasks. We propose a method that
keeps the human in the loop and allows for the systematic reduction
of uncertainty inherent in implicit cooperation. We
present a benchmark scenario as well as preliminary implementation
results.

Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006), pp. 512-517, Heidelberg, Germany, September, 2006.

Author : Oliver C. Schrempf, Dietrich Brunn, Uwe D. Hanebeck
Title : Dirac Mixture Density Approximation Based on Minimization of the Weighted Cramér-von Mises Distance
In : Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006)
Date : September 2006

Abstract

This paper proposes a systematic procedure for approximating arbitrary
probability density functions by means of Dirac mixtures. For that
purpose, a distance measure is required, which is in general not
well defined for Dirac mixture densities. Hence, a distance measure
comparing the corresponding cumulative distribution functions is
employed. Here, we focus on the weighted Cramer-von Mises distance,
a weighted integral quadratic distance measure, which is simple and
intuitive. Since a closed-form solution of the given optimization
problem is not possible in general, an efficient solution procedure
based on a homotopy continuation approach is proposed. Compared to
a standard particle approximation, the proposed procedure ensures
an optimal approximation with respect to a given distance measure.
Although useful in their own respect, the results also provide the
basis for a recursive nonlinear filtering mechanism as an alternative
to the popular particle filters.

Proceedings of the 9th International Conference on Information Fusion (Fusion 2006), Florence, Italy, July, 2006.

Author : Oliver C. Schrempf, Anne Hanselmann, Uwe D. Hanebeck
Title : Efficient Representation and Fusion of Hybrid Joint Densities for Clusters in Nonlinear Hybrid Bayesian Networks
In : Proceedings of the 9th International Conference on Information Fusion (Fusion 2006)
Date : July 2006

Abstract

Undirected cycles in Bayesian networks are often treated by using
clustering methods. This results in networks with nodes characterized
by joint probability densities instead of marginal densities. An
efficient representation of these hybrid joint densities is essential
especially in nonlinear hybrid networks containing continuous as
well as discrete variables. In this article we present a unified
representation of continuous, discrete, and hybrid joint densities.
This representation is based on Gaussian and Dirac mixtures and allows
for analytic evaluation of arbitrary hybrid networks without loosing
structural information, even for networks containing clusters. Furthermore
we derive update formulae for marginal and joint densities from a
system theoretic point of view by treating a Bayesian network as
a system of cascaded subsystems. Together with the presented mixture
representation of densities this yields an exact analytic updating
scheme.

Proceedings of the 2nd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2005), 3:251-256, Barcelona, Spain, September, 2005.

Author : Oliver C. Schrempf, Uwe D. Hanebeck
Title : A Generic Model for Estimating User Intentions in Human-Robot Cooperation
In : Proceedings of the 2nd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2005)
Date : September 2005

Abstract

The recognition of user intentions is an important feature for humanoid
robots to make implicit and human-like interactions possible. In
this paper, we introduce a formal view on user-intentions in human-machine
interaction and how they can be estimated by observing user actions.
We use Hybrid Dynamic Bayesian Networks to develop a generic model
that includes connections between intentions, actions, and sensor
measurements. This model can be used to extend arbitrary human-machine
applications by intention recognition.

Proceedings of the 2005 IEEE International Workshop on Robot and Human Interactive Communication (ROMAN 2005), pp. 555-560, Nashville, Tennessee, USA, August, 2005.

Author : Oliver C. Schrempf, Uwe D. Hanebeck, Andreas J. Schmid., Heinz Wörn
Title : A Novel Approach to Proactive Human-Robot Cooperation
In : Proceedings of the 2005 IEEE International Workshop on Robot and Human Interactive Communication (ROMAN 2005)
Date : August 2005

Abstract

This paper introduces the concept of proactive execution of robot
tasks in the context of human-robot cooperation with uncertain knowledge
of the human\'s intentions. We present a system architecture that
defines the necessary modules of the robot and their interactions
with each other. The two key modules are the intention recognition
that determines the human user\'s intentions and the planner that
executes the appropriate tasks based on those intentions. We show
how planning conflicts due to the uncertainty of the intention information
are resolved by proactive execution of the corresponding task that
optimally reduces the system\'s uncertainly. Finally, we present an
algorithm for selecting this task and suggest a benchmark scenario.

Proceedings of the 8th International Conference on Information Fusion (Fusion 2005), 1:85-92, Philadelphia, Pennsylvania, USA, July, 2005.

Author : Oliver C. Schrempf, O. Feiermann, Uwe D. Hanebeck
Title : Optimal Mixture Approximation of the Product of Mixtures
In : Proceedings of the 8th International Conference on Information Fusion (Fusion 2005)
Date : July 2005

Abstract

Gaussian mixture densities are very common today to describe arbitrary
structured uncertainties in various applications. Many of these applications
have to deal with the fusion of uncertainties, an operation that
is usually performed by multiplication of these densities. The product
of Gaussian mixtures can be calculated exactly, but the number of
mixture components in the resulting mixture increases in an exponential
way. Hence, it is essential to approximate this resulting mixture
with less components, to keep it tractable for further processing
steps. This paper introduces an approach to approximate the exact
product with a mixture that uses less components. The maximum approximation
error can be chosen by the user. This choice allows to trade accuracy
of the approximation for the number of mixture components used. This
is possible due to the usage of a progressive processing scheme that
calculates the product operation by means of a system of ordinary
differential equations. The solution of this system yields the parameters
of the desired Gaussian mixture.

Proceedings of the 16th IFAC World Congress (IFAC 2005), Prague, Czech Republic, July, 2005.

Author : Oliver C. Schrempf, Uwe D. Hanebeck
Title : Evaluation of Hybrid Bayesian Networks using Analytical Density Representations
In : Proceedings of the 16th IFAC World Congress (IFAC 2005)
Date : July 2005

Abstract

In this article, a new mechanism is described for modeling and evaluating
Hybrid Dynamic Bayesian networks. The approach uses Gaussian mixtures
and Dirac mixtures as messages to calculate marginal densities. As
these densities are approximated by means of Gaussian mixtures, any
desired precision is possible. The presented approach removes the
restrictions of sample based evaluation of Bayesian networks since
it uses an analytical approximation scheme for probability densities
which systematically minimizes the distance between the exact and
the approximate density.

Proceedings of the 6th International Workshop on Computer Science and Information Technologies (CSIT 2004), 1:32-37, Budapest, Hungary, October, 2004.

Author : Oliver C. Schrempf, Uwe D. Hanebeck
Title : A New Approach for Hybrid Bayesian Networks Using Full Densities
In : Proceedings of the 6th International Workshop on Computer Science and Information Technologies (CSIT 2004)
Date : October 2004

Abstract

In this article, a new mechanism is described for modeling and evaluating
hybrid Bayesian networks. The approach uses Gaussian mixtures and
Dirac mixtures as messages to calculate marginal densities. The mechanism
is proven to be exact, hence the accuracy of resulting marginals
is only dependending on the accuracy of the conditional densities.
As these densities are approximated by means of Gaussian mixtures,
any desired precision can be achieved. The presented approach removes
the restrictions concerning the ancestry of discrete nodes often
made in literature. Hence it enables the designer to model arbitrary
parent-child relationships using continuous and discrete variables.