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2016


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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]

2016


arXiv PDF DOI Project Page [BibTex]


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Dynamical self-consistency leads to behavioral development and emergent social interactions in robots.

Der, R., Martius, G.

In Proc. IEEE Int. Conf. on Development and Learning and Epigenetic Robotics, pages: 49-56, IEEE, September 2016, in press (inproceedings)

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DOI [BibTex]

DOI [BibTex]


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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]


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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]


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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]


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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]


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Compliant control for soft robots: emergent behavior of a tendon driven anthropomorphic arm.

Martius, G., Hostettler, R., Knoll, A., Der, R.

In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages: 767-773, 2016 (inproceedings)

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DOI [BibTex]

DOI [BibTex]

2008


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Emergence of Interaction Among Adaptive Agents

Martius, G., Nolfi, S., Herrmann, J. M.

In Proc. From Animals to Animats 10 (SAB 2008), 5040, pages: 457-466, LNCS, Springer, 2008 (inproceedings)

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DOI [BibTex]

2008


DOI [BibTex]


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Structure from Behavior in Autonomous Agents

Martius, G., Fiedler, K., Herrmann, J.

In Proc. IEEE Intl. Conf. Intelligent Robots and Systems (IROS 2008), pages: 858 - 862, 2008 (inproceedings)

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DOI [BibTex]

DOI [BibTex]

2007


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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]


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Guided Self-organisation for Autonomous Robot Development

Martius, G., Herrmann, J. M., Der, R.

In Advances in Artificial Life 9th European Conference, ECAL 2007, 4648, pages: 766-775, LNCS, Springer, 2007 (inproceedings)

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[BibTex]

[BibTex]