Header logo is


2016


Thumb xl nonlinear approximate vs exact
A New Perspective and Extension of the Gaussian Filter

Wüthrich, M., Trimpe, S., Garcia Cifuentes, C., Kappler, D., Schaal, S.

The International Journal of Robotics Research, 35(14):1731-1749, December 2016 (article)

Abstract
The Gaussian Filter (GF) is one of the most widely used filtering algorithms; instances are the Extended Kalman Filter, the Unscented Kalman Filter and the Divided Difference Filter. The GF represents the belief of the current state by a Gaussian distribution, whose mean is an affine function of the measurement. We show that this representation can be too restrictive to accurately capture the dependences in systems with nonlinear observation models, and we investigate how the GF can be generalized to alleviate this problem. To this end, we view the GF as the solution to a constrained optimization problem. From this new perspective, the GF is seen as a special case of a much broader class of filters, obtained by relaxing the constraint on the form of the approximate posterior. On this basis, we outline some conditions which potential generalizations have to satisfy in order to maintain the computational efficiency of the GF. We propose one concrete generalization which corresponds to the standard GF using a pseudo measurement instead of the actual measurement. Extending an existing GF implementation in this manner is trivial. Nevertheless, we show that this small change can have a major impact on the estimation accuracy.

am ics

PDF DOI Project Page [BibTex]

2016


PDF DOI Project Page [BibTex]


no image
Predictive and Self Triggering for Event-based State Estimation

Trimpe, S.

In Proceedings of the 55th IEEE Conference on Decision and Control (CDC), pages: 3098-3105, Las Vegas, NV, USA, December 2016 (inproceedings)

am ics

arXiv PDF DOI Project Page [BibTex]

arXiv PDF DOI Project Page [BibTex]


Thumb xl screen shot 2015 12 04 at 15.11.43
Robust Gaussian Filtering using a Pseudo Measurement

Wüthrich, M., Garcia Cifuentes, C., Trimpe, S., Meier, F., Bohg, J., Issac, J., Schaal, S.

In Proceedings of the American Control Conference (ACC), Boston, MA, USA, July 2016 (inproceedings)

Abstract
Most widely-used state estimation algorithms, such as the Extended Kalman Filter and the Unscented Kalman Filter, belong to the family of Gaussian Filters (GF). Unfortunately, GFs fail if the measurement process is modelled by a fat-tailed distribution. This is a severe limitation, because thin-tailed measurement models, such as the analytically-convenient and therefore widely-used Gaussian distribution, are sensitive to outliers. In this paper, we show that mapping the measurements into a specific feature space enables any existing GF algorithm to work with fat-tailed measurement models. We find a feature function which is optimal under certain conditions. Simulation results show that the proposed method allows for robust filtering in both linear and nonlinear systems with measurements contaminated by fat-tailed noise.

am ics

Web link (url) DOI Project Page [BibTex]

Web link (url) DOI Project Page [BibTex]


Thumb xl screen shot 2016 01 19 at 14.48.37
Automatic LQR Tuning Based on Gaussian Process Global Optimization

Marco, A., Hennig, P., Bohg, J., Schaal, S., Trimpe, S.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages: 270-277, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (inproceedings)

Abstract
This paper proposes an automatic controller tuning framework based on linear optimal control combined with Bayesian optimization. With this framework, an initial set of controller gains is automatically improved according to a pre-defined performance objective evaluated from experimental data. The underlying Bayesian optimization algorithm is Entropy Search, which represents the latent objective as a Gaussian process and constructs an explicit belief over the location of the objective minimum. This is used to maximize the information gain from each experimental evaluation. Thus, this framework shall yield improved controllers with fewer evaluations compared to alternative approaches. A seven-degree- of-freedom robot arm balancing an inverted pole is used as the experimental demonstrator. Results of a two- and four- dimensional tuning problems highlight the method’s potential for automatic controller tuning on robotic platforms.

am ics pn

Video PDF DOI Project Page [BibTex]

Video PDF DOI Project Page [BibTex]


Thumb xl screen shot 2016 01 19 at 14.56.20
Depth-based Object Tracking Using a Robust Gaussian Filter

Issac, J., Wüthrich, M., Garcia Cifuentes, C., Bohg, J., Trimpe, S., Schaal, S.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2016, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (inproceedings)

Abstract
We consider the problem of model-based 3D- tracking of objects given dense depth images as input. Two difficulties preclude the application of a standard Gaussian filter to this problem. First of all, depth sensors are characterized by fat-tailed measurement noise. To address this issue, we show how a recently published robustification method for Gaussian filters can be applied to the problem at hand. Thereby, we avoid using heuristic outlier detection methods that simply reject measurements if they do not match the model. Secondly, the computational cost of the standard Gaussian filter is prohibitive due to the high-dimensional measurement, i.e. the depth image. To address this problem, we propose an approximation to reduce the computational complexity of the filter. In quantitative experiments on real data we show how our method clearly outperforms the standard Gaussian filter. Furthermore, we compare its performance to a particle-filter-based tracking method, and observe comparable computational efficiency and improved accuracy and smoothness of the estimates.

am ics

Video Bayesian Object Tracking Library Bayesian Filtering Framework Object Tracking Dataset link (url) DOI Project Page [BibTex]

Video Bayesian Object Tracking Library Bayesian Filtering Framework Object Tracking Dataset link (url) DOI Project Page [BibTex]


no image
Communication Rate Analysis for Event-based State Estimation

(Best student paper finalist)

Ebner, S., Trimpe, S.

In Proceedings of the 13th International Workshop on Discrete Event Systems, May 2016 (inproceedings)

am ics

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
Supplemental material for ’Communication Rate Analysis for Event-based State Estimation’

Ebner, S., Trimpe, S.

Max Planck Institute for Intelligent Systems, January 2016 (techreport)

am ics

PDF [BibTex]

PDF [BibTex]


no image
Sustainable effects of simulator-based training on ecological driving

Lüderitz, C., Wirzberger, M., Karrer-Gauß, K.

In Advances in Ergonomic Design of Systems, Products and Processes. Proceedings of the Annual Meeting of the GfA 2015, pages: 463-475, Springer, 2016 (inbook)

Abstract
Simulation-based driver training offers a promising way to teach ecological driving behavior under controlled, comparable conditions. In a study with 23 professional drivers, we tested the effectiveness of such training. The driving behavior of a training group in a simulated drive with and without instructions were compared. Ten weeks later, a repetition drive tested the long-term effect training. Driving data revealed reduced fuel consumption by ecological driving in both the guided and repetition drives. Driving time decreased significantly in the training and did not differ from driving time after 10 weeks. Results did not achieve significance for transfer to test drives in real traffic situations. This may be due to the small sample size and biased data as a result of unusual driving behavior. Finally, recent and promising approaches to support drivers in maintaining eco-driving styles beyond training situations are outlined.

re

DOI [BibTex]

DOI [BibTex]


no image
One for all?! Simultaneous examination of load-inducing factors for advancing media-related instructional research

Wirzberger, M., Beege, M., Schneider, S., Nebel, S., Rey, G. D.

Computers {\&} Education, 100, pages: 18-31, Elsevier BV, 2016 (article)

Abstract
In multimedia learning settings, limitations in learners' mental resource capacities need to be considered to avoid impairing effects on learning performance. Based on the prominent and often quoted Cognitive Load Theory, this study investigates the potential of a single experimental approach to provide simultaneous and separate measures for the postulated load-inducing factors. Applying a basal letter-learning task related to the process of working memory updating, intrinsic cognitive load (by varying task complexity), extraneous cognitive load (via inducing split-attention demands) and germane cognitive load (by varying the presence of schemata) were manipulated within a 3 × 2 × 2-factorial full repeated-measures design. The performance of a student sample (N = 96) was inspected regarding reaction times and errors in updating and recall steps. Approaching the results with linear mixed models, the effect of complexity gained substantial strength, whereas the other factors received at least partial significant support. Additionally, interactions between two or all load-inducing factors occurred. Despite various open questions, the study comprises a promising step for the empirical investigation of existing construction yards in cognitive load research.

re

DOI [BibTex]

DOI [BibTex]


no image
Event-based Sampling for Reducing Communication Load in Realtime Human Motion Analysis by Wireless Inertial Sensor Networks

Laidig, D., Trimpe, S., Seel, T.

Current Directions in Biomedical Engineering, 2(1):711-714, De Gruyter, 2016 (article)

am ics

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
Examining load-inducing factors in instructional design: An ACT-R approach

Wirzberger, M., Rey, G. D.

In Proceedings of the 14th International Conference on Cognitive Modeling (ICCM 2016), pages: 223-224, University Park, PA, Penn State, 2016 (inproceedings)

re

[BibTex]

[BibTex]


no image
Helping people make better decisions using optimal gamification

Lieder, F., Griffiths, T. L.

In Proceedings of the 38th Annual Conference of the Cognitive Science Society, 2016 (inproceedings)

re

Project Page [BibTex]

Project Page [BibTex]


no image
CLT meets ACT-R: Modeling load-inducing factors in instructional design

Wirzberger, M., Rey, G. D.

In Abstracts of the 58th Conference of Experimental Psychologists, pages: 377, Pabst Science Publishers, Lengerich, 2016 (inproceedings)

re

[BibTex]

[BibTex]


no image
Modeling load factors in multimedia learning: An ACT-R approach

Wirzberger, M.

In Dagstuhl 2016. Proceedings of the 10th Joint Workshop of the German Research Training Groups in Computer Science, pages: 98, Universitätsverlag Chemnitz, Chemnitz, 2016 (inproceedings)

re

[BibTex]

[BibTex]


no image
Separating cognitive load facets in a working memory updating task: An experimental approach

Wirzberger, M., Beege, M., Schneider, S., Nebel, S., Rey, G. D.

In International Meeting of the Psychonomic Society, Granada – Spain, May 5-8, 2016, Abstract Book, pages: 211-212, 2016 (inproceedings)

re

[BibTex]

[BibTex]


no image
CLT meets WMU: Simultaneous experimental manipulation of load factors in a basal working memory task

Wirzberger, M., Beege, M., Schneider, S., Nebel, S., Rey, G. D.

In 9th International Cognitive Load Theory Conference, June 22nd to 24th, 2016, Bochum, Germany, Abstracts, pages: 19, 2016 (inproceedings)

re

[BibTex]

[BibTex]


no image
Bedingt räumliche Nähe bessere Lernergebnisse? Die Rolle der Distanz und Integration beim Lernen mit multiplen Informationsquellen

Beege, M., Nebel, S., Schneider, S., Wirzberger, M., Schmidt, N., Rey, G. D.

In 50th Conference of the German Psychological Society. Abstracts, pages: 540, Pabst Science Publishers, Lengerich, 2016 (inproceedings)

re

[BibTex]

[BibTex]

2015


no image
Distributed Event-based State Estimation

Trimpe, S.

Max Planck Institute for Intelligent Systems, November 2015 (techreport)

Abstract
An event-based state estimation approach for reducing communication in a networked control system is proposed. Multiple distributed sensor-actuator-agents observe a dynamic process and sporadically exchange their measurements and inputs over a bus network. Based on these data, each agent estimates the full state of the dynamic system, which may exhibit arbitrary inter-agent couplings. Local event-based protocols ensure that data is transmitted only when necessary to meet a desired estimation accuracy. This event-based scheme is shown to mimic a centralized Luenberger observer design up to guaranteed bounds, and stability is proven in the sense of bounded estimation errors for bounded disturbances. The stability result extends to the distributed control system that results when the local state estimates are used for distributed feedback control. Simulation results highlight the benefit of the event-based approach over classical periodic ones in reducing communication requirements.

am ics

arXiv [BibTex]

2015


arXiv [BibTex]


Thumb xl posterior
Automatic LQR Tuning Based on Gaussian Process Optimization: Early Experimental Results

Marco, A., Hennig, P., Bohg, J., Schaal, S., Trimpe, S.

Machine Learning in Planning and Control of Robot Motion Workshop at the IEEE/RSJ International Conference on Intelligent Robots and Systems (iROS), pages: , , Machine Learning in Planning and Control of Robot Motion Workshop, October 2015 (conference)

Abstract
This paper proposes an automatic controller tuning framework based on linear optimal control combined with Bayesian optimization. With this framework, an initial set of controller gains is automatically improved according to a pre-defined performance objective evaluated from experimental data. The underlying Bayesian optimization algorithm is Entropy Search, which represents the latent objective as a Gaussian process and constructs an explicit belief over the location of the objective minimum. This is used to maximize the information gain from each experimental evaluation. Thus, this framework shall yield improved controllers with fewer evaluations compared to alternative approaches. A seven-degree-of-freedom robot arm balancing an inverted pole is used as the experimental demonstrator. Preliminary results of a low-dimensional tuning problem highlight the method’s potential for automatic controller tuning on robotic platforms.

am ei ics pn

PDF DOI Project Page [BibTex]

PDF DOI Project Page [BibTex]


Thumb xl mt cover
Gaussian Process Optimization for Self-Tuning Control

Marco, A.

Polytechnic University of Catalonia (BarcelonaTech), October 2015 (mastersthesis)

am ics

PDF Project Page [BibTex]

PDF Project Page [BibTex]


no image
Adaptive and Learning Concepts in Hydraulic Force Control

Doerr, A.

University of Stuttgart, September 2015 (mastersthesis)

am ics

[BibTex]

[BibTex]


Thumb xl screen shot 2015 08 22 at 21.47.37
Direct Loss Minimization Inverse Optimal Control

Doerr, A., Ratliff, N., Bohg, J., Toussaint, M., Schaal, S.

In Proceedings of Robotics: Science and Systems, Rome, Italy, Robotics: Science and Systems XI, July 2015 (inproceedings)

Abstract
Inverse Optimal Control (IOC) has strongly impacted the systems engineering process, enabling automated planner tuning through straightforward and intuitive demonstration. The most successful and established applications, though, have been in lower dimensional problems such as navigation planning where exact optimal planning or control is feasible. In higher dimensional systems, such as humanoid robots, research has made substantial progress toward generalizing the ideas to model free or locally optimal settings, but these systems are complicated to the point where demonstration itself can be difficult. Typically, real-world applications are restricted to at best noisy or even partial or incomplete demonstrations that prove cumbersome in existing frameworks. This work derives a very flexible method of IOC based on a form of Structured Prediction known as Direct Loss Minimization. The resulting algorithm is essentially Policy Search on a reward function that rewards similarity to demonstrated behavior (using Covariance Matrix Adaptation (CMA) in our experiments). Our framework blurs the distinction between IOC, other forms of Imitation Learning, and Reinforcement Learning, enabling us to derive simple, versatile, and practical algorithms that blend imitation and reinforcement signals into a unified framework. Our experiments analyze various aspects of its performance and demonstrate its efficacy on conveying preferences for motion shaping and combined reach and grasp quality optimization.

am ics

PDF Video Project Page [BibTex]

PDF Video Project Page [BibTex]


no image
LMI-Based Synthesis for Distributed Event-Based State Estimation

Muehlebach, M., Trimpe, S.

In Proceedings of the American Control Conference, July 2015 (inproceedings)

Abstract
This paper presents an LMI-based synthesis procedure for distributed event-based state estimation. Multiple agents observe and control a dynamic process by sporadically exchanging data over a broadcast network according to an event-based protocol. In previous work [1], the synthesis of event-based state estimators is based on a centralized design. In that case three different types of communication are required: event-based communication of measurements, periodic reset of all estimates to their joint average, and communication of inputs. The proposed synthesis problem eliminates the communication of inputs as well as the periodic resets (under favorable circumstances) by accounting explicitly for the distributed structure of the control system.

am ics

PDF DOI Project Page [BibTex]

PDF DOI Project Page [BibTex]


no image
Guaranteed H2 Performance in Distributed Event-Based State Estimation

Muehlebach, M., Trimpe, S.

In Proceeding of the First International Conference on Event-based Control, Communication, and Signal Processing, June 2015 (inproceedings)

am ics

PDF DOI Project Page [BibTex]

PDF DOI Project Page [BibTex]


no image
On the Choice of the Event Trigger in Event-based Estimation

Trimpe, S., Campi, M.

In Proceeding of the First International Conference on Event-based Control, Communication, and Signal Processing, June 2015 (inproceedings)

am ics

PDF DOI Project Page [BibTex]

PDF DOI Project Page [BibTex]


no image
Event-based Estimation and Control for Remote Robot Operation with Reduced Communication

Trimpe, S., Buchli, J.

In Proceedings of the IEEE International Conference on Robotics and Automation, May 2015 (inproceedings)

Abstract
An event-based communication framework for remote operation of a robot via a bandwidth-limited network is proposed. The robot sends state and environment estimation data to the operator, and the operator transmits updated control commands or policies to the robot. Event-based communication protocols are designed to ensure that data is transmitted only when required: the robot sends new estimation data only if this yields a significant information gain at the operator, and the operator transmits an updated control policy only if this comes with a significant improvement in control performance. The developed framework is modular and can be used with any standard estimation and control algorithms. Simulation results of a robotic arm highlight its potential for an efficient use of limited communication resources, for example, in disaster response scenarios such as the DARPA Robotics Challenge.

am ics

PDF DOI Project Page [BibTex]

PDF DOI Project Page [BibTex]


no image
Lernende Roboter

Trimpe, S.

In Jahrbuch der Max-Planck-Gesellschaft, Max Planck Society, May 2015, (popular science article in German) (inbook)

am ics

link (url) [BibTex]

link (url) [BibTex]


no image
Policy Search for Imitation Learning

Doerr, A.

University of Stuttgart, January 2015 (thesis)

am ics

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


no image
Modeling interruption and resumption in a smartphone task: An ACT-R approach

Wirzberger, M., Russwinkel, N.

i-com, 14(2), Walter de Gruyter GmbH, 2015 (article)

Abstract
This research aims to inspect human cognition when being interrupted while performing a smartphone task with varying levels of mental demand. Due to its benefits especially in the early stages of interface development, a cognitive modeling approach is used. It applies the cognitive architecture ACT-R to shed light on task-related cognitive processing. The inspected task setting involves a shopping scenario, manipulating interruption via product advertisements and mental demands by the respective number of people shopping is done for. Model predictions are validated through a corresponding experimental setting with 62 human participants. Comparing model and human data in a defined set of performance-related parameters displays mixed results that indicate an acceptable fit – at least in some cases. Potential explanations for the observed differences are discussed at the end.

re

DOI [BibTex]

DOI [BibTex]


no image
A New Perspective and Extension of the Gaussian Filter

Wüthrich, M., Trimpe, S., Kappler, D., Schaal, S.

In Robotics: Science and Systems, 2015 (inproceedings)

Abstract
The Gaussian Filter (GF) is one of the most widely used filtering algorithms; instances are the Extended Kalman Filter, the Unscented Kalman Filter and the Divided Difference Filter. GFs represent the belief of the current state by a Gaussian with the mean being an affine function of the measurement. We show that this representation can be too restrictive to accurately capture the dependencies in systems with nonlinear observation models, and we investigate how the GF can be generalized to alleviate this problem. To this end we view the GF from a variational-inference perspective, and analyze how restrictions on the form of the belief can be relaxed while maintaining simplicity and efficiency. This analysis provides a basis for generalizations of the GF. We propose one such generalization which coincides with a GF using a virtual measurement, obtained by applying a nonlinear function to the actual measurement. Numerical experiments show that the proposed Feature Gaussian Filter (FGF) can have a substantial performance advantage over the standard GF for systems with nonlinear observation models.

am ics

Web PDF Project Page [BibTex]


no image
Learning from others: Adult and child strategies in assessing conflicting ratings

Hu, J., Lieder, F., Griffiths, T. L., Xu, F.

In Biennial Meeting of the Society for Research in Child Development, Philadelphia, Pennsylvania, USA, 2015 (inproceedings)

re

[BibTex]

[BibTex]


no image
The optimism bias may support rational action

Lieder, F., Goel, S., Kwan, R., Griffiths, T. L.

NIPS 2015 Workshop on Bounded Optimality and Rational Metareasoning, 2015 (article)

re

[BibTex]

[BibTex]


no image
Rational use of cognitive resources: Levels of analysis between the computational and the algorithmic

Griffiths, T. L., Lieder, F., Goodman, N. D.

Topics in Cognitive Science, 7(2):217-229, Wiley, 2015 (article)

re

[BibTex]

[BibTex]


no image
When to use which heuristic: A rational solution to the strategy selection problem

Lieder, F., Griffiths, T. L.

In Proceedings of the 37th Annual Conference of the Cognitive Science Society, 2015 (inproceedings)

re

Project Page [BibTex]

Project Page [BibTex]


no image
Children and adults differ in their strategies for social learning

Lieder, F., Sim, Z., Hu, J., Griffiths, T., Xu, F.

In Proceedings of the 37th Annual Conference of the Cognitive Science Society, 2015 (inproceedings)

re

[BibTex]

[BibTex]


no image
Utility-weighted sampling in decisions from experience

Lieder, F., Griffiths, T. L., Hsu, M.

In The 2nd Multidisciplinary Conference on Reinforcement Learning and Decision Making, 2015 (inproceedings)

re

[BibTex]

[BibTex]


no image
Nachhaltige Effekte simulatorbasierten Trainings auf eine ökologische Fahrweise [Sustainable effects of simulator-based training on ecological driving]

Lüderitz, C., Wirzberger, M., Karrer-Gauß, K.

In VerANTWORTung für die Arbeit der Zukunft, 61st Conference of the Society for Ergonomics and Work Science, GfA Press, Dortmund, 2015 (inproceedings)

re

[BibTex]

[BibTex]


no image
Model-based strategy selection learning

Lieder, F., Griffiths, T. L.

The 2nd Multidisciplinary Conference on Reinforcement Learning and Decision Making, 2015 (article)

re

Project Page [BibTex]

Project Page [BibTex]


no image
Cognitive modeling meets instructional design: Exploring Cognitive Load Theory with ACT-R

Wirzberger, M., Rey, G. D.

In Trends in Neuroergonomics. Proceedings of the 11th Berlin Workshop Human-Machine Systems, pages: 190-193, Universitätsverlag der TU Berlin, Berlin, 2015 (inproceedings)

re

DOI [BibTex]

DOI [BibTex]

2007


no image
Less Conservative Polytopic LPV Models for Charge Control by Combining Parameter Set Mapping and Set Intersection

Kwiatkowski, A., Trimpe, S., Werner, H.

In Proceedings of the 46th IEEE Conference on Decision and Control, 2007 (inproceedings)

am ics

DOI [BibTex]

2007


DOI [BibTex]