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2017


On the Design of {LQR} Kernels for Efficient Controller Learning
On the Design of LQR Kernels for Efficient Controller Learning

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

Proceedings of the 56th IEEE Annual Conference on Decision and Control (CDC), pages: 5193-5200, IEEE, IEEE Conference on Decision and Control, December 2017 (conference)

Abstract
Finding optimal feedback controllers for nonlinear dynamic systems from data is hard. Recently, Bayesian optimization (BO) has been proposed as a powerful framework for direct controller tuning from experimental trials. For selecting the next query point and finding the global optimum, BO relies on a probabilistic description of the latent objective function, typically a Gaussian process (GP). As is shown herein, GPs with a common kernel choice can, however, lead to poor learning outcomes on standard quadratic control problems. For a first-order system, we construct two kernels that specifically leverage the structure of the well-known Linear Quadratic Regulator (LQR), yet retain the flexibility of Bayesian nonparametric learning. Simulations of uncertain linear and nonlinear systems demonstrate that the LQR kernels yield superior learning performance.

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arXiv PDF On the Design of LQR Kernels for Efficient Controller Learning - CDC presentation DOI Project Page [BibTex]

2017


arXiv PDF On the Design of LQR Kernels for Efficient Controller Learning - CDC presentation DOI Project Page [BibTex]


Optimizing Long-term Predictions for Model-based Policy Search
Optimizing Long-term Predictions for Model-based Policy Search

Doerr, A., Daniel, C., Nguyen-Tuong, D., Marco, A., Schaal, S., Toussaint, M., Trimpe, S.

Proceedings of 1st Annual Conference on Robot Learning (CoRL), 78, pages: 227-238, (Editors: Sergey Levine and Vincent Vanhoucke and Ken Goldberg), 1st Annual Conference on Robot Learning, November 2017 (conference)

Abstract
We propose a novel long-term optimization criterion to improve the robustness of model-based reinforcement learning in real-world scenarios. Learning a dynamics model to derive a solution promises much greater data-efficiency and reusability compared to model-free alternatives. In practice, however, modelbased RL suffers from various imperfections such as noisy input and output data, delays and unmeasured (latent) states. To achieve higher resilience against such effects, we propose to optimize a generative long-term prediction model directly with respect to the likelihood of observed trajectories as opposed to the common approach of optimizing a dynamics model for one-step-ahead predictions. We evaluate the proposed method on several artificial and real-world benchmark problems and compare it to PILCO, a model-based RL framework, in experiments on a manipulation robot. The results show that the proposed method is competitive compared to state-of-the-art model learning methods. In contrast to these more involved models, our model can directly be employed for policy search and outperforms a baseline method in the robot experiment.

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PDF Project Page [BibTex]

PDF Project Page [BibTex]


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From Monocular SLAM to Autonomous Drone Exploration

von Stumberg, L., Usenko, V., Engel, J., Stueckler, J., Cremers, D.

In European Conference on Mobile Robots (ECMR), September 2017 (inproceedings)

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[BibTex]

[BibTex]


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Event-based State Estimation: An Emulation-based Approach

Trimpe, S.

IET Control Theory & Applications, 11(11):1684-1693, July 2017 (article)

Abstract
An event-based state estimation approach for reducing communication in a networked control system is proposed. Multiple distributed sensor agents observe a dynamic process and sporadically transmit their measurements to estimator agents over a shared bus network. Local event-triggering protocols ensure that data is transmitted only when necessary to meet a desired estimation accuracy. The event-based design is shown to emulate the performance of a centralised state observer design up to guaranteed bounds, but with reduced communication. The stability results for state estimation are extended to the distributed control system that results when the local estimates are used for feedback control. Results from numerical simulations and hardware experiments illustrate the effectiveness of the proposed approach in reducing network communication.

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arXiv Supplementary material PDF DOI Project Page [BibTex]

arXiv Supplementary material PDF DOI Project Page [BibTex]


Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers
Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers

Doerr, A., Nguyen-Tuong, D., Marco, A., Schaal, S., Trimpe, S.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages: 5295-5301, IEEE, Piscataway, NJ, USA, IEEE International Conference on Robotics and Automation (ICRA), May 2017 (inproceedings)

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PDF arXiv DOI Project Page [BibTex]

PDF arXiv DOI Project Page [BibTex]


Virtual vs. {R}eal: Trading Off Simulations and Physical Experiments in Reinforcement Learning with {B}ayesian Optimization
Virtual vs. Real: Trading Off Simulations and Physical Experiments in Reinforcement Learning with Bayesian Optimization

Marco, A., Berkenkamp, F., Hennig, P., Schoellig, A. P., Krause, A., Schaal, S., Trimpe, S.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages: 1557-1563, IEEE, Piscataway, NJ, USA, IEEE International Conference on Robotics and Automation (ICRA), May 2017 (inproceedings)

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PDF arXiv ICRA 2017 Spotlight presentation Virtual vs. Real - Video explanation DOI Project Page [BibTex]

PDF arXiv ICRA 2017 Spotlight presentation Virtual vs. Real - Video explanation DOI Project Page [BibTex]


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Multi-View Deep Learning for Consistent Semantic Mapping with RGB-D Cameras

Ma, L., Stueckler, J., Kerl, C., Cremers, D.

In IEEE International Conference on Intelligent Robots and Systems (IROS), Vancouver, Canada, 2017 (inproceedings)

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[BibTex]

[BibTex]


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Accurate depth and normal maps from occlusion-aware focal stack symmetry

Strecke, M., Alperovich, A., Goldluecke, B.

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Semi-Supervised Deep Learning for Monocular Depth Map Prediction

Kuznietsov, Y., Stueckler, J., Leibe, B.

In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (inproceedings)

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[BibTex]

[BibTex]


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Shadow and Specularity Priors for Intrinsic Light Field Decomposition

Alperovich, A., Johannsen, O., Strecke, M., Goldluecke, B.

In Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2017 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Keyframe-Based Visual-Inertial Online SLAM with Relocalization

Kasyanov, A., Engelmann, F., Stueckler, J., Leibe, B.

In IEEE/RSJ Int. Conference on Intelligent Robots and Systems, IROS, 2017 (inproceedings)

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[BibTex]

[BibTex]


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SAMP: Shape and Motion Priors for 4D Vehicle Reconstruction

Engelmann, F., Stueckler, J., Leibe, B.

In IEEE Winter Conference on Applications of Computer Vision, WACV, 2017 (inproceedings)

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[BibTex]

[BibTex]

2010


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Accelerometer-based Tilt Estimation of a Rigid Body with only Rotational Degrees of Freedom

Trimpe, S., D’Andrea, R.

In Proceedings of the IEEE International Conference on Robotics and Automation, 2010 (inproceedings)

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PDF DOI [BibTex]

2010


PDF DOI [BibTex]


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Combining depth and color cues for scale- and viewpoint-invariant object segmentation and recognition using Random Forests

Stueckler, J., Behnke, S.

In Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), pages: 4566-4571, October 2010 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Intuitive Multimodal Interaction for Domestic Service Robots

Nieuwenhuisen, M., Stueckler, J., Behnke, S.

In Proc. of the ISR/ROBOTIK, VDE Verlag, 2010 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Improving People Awareness of Service Robots by Semantic Scene Knowledge

Stueckler, J., Behnke, S.

In RobuCup, 6556, pages: 157-168, Lecture Notes in Computer Science, Springer, 2010 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Towards Semantic Scene Analysis with Time-of-flight Cameras

Holz, D., Schnabel, R., Droeschel, D., Stueckler, J., Behnke, S.

In RobuCup, 6556, pages: 121-132, Lecture Notes in Computer Science, Springer, 2010 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Utilizing the Structure of Field Lines for Efficient Soccer Robot Localization

Schulz, H., Liu, W., Stueckler, J., Behnke, S.

In RobuCup, 6556, pages: 397-408, Lecture Notes in Computer Science, Springer, 2010 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Improving indoor navigation of autonomous robots by an explicit representation of doors

Nieuwenhuisen, M., Stueckler, J., Behnke, S.

In Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), pages: 4895-4901, May 2010 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Improving imitated grasping motions through interactive expected deviation learning

Gräve, K., Stueckler, J., Behnke, S.

In Proc. of the 10th IEEE-RAS Int. Conf. on Humanoid Robots (Humanoids), pages: 397-404, December 2010 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Learning Motion Skills from Expert Demonstrations and Own Experience using Gaussian Process Regression

Gräve, K., Stueckler, J., Behnke, S.

In Proc. of the ISR/ROBOTIK, pages: 1-8, VDE Verlag, 2010 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Using Time-of-Flight cameras with active gaze control for 3D collision avoidance

Droeschel, D., Holz, D., Stueckler, J., Behnke, S.

In Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), pages: 4035-4040, May 2010 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]