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2015


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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]


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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.

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PDF DOI Project Page [BibTex]

PDF DOI Project Page [BibTex]


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Gaussian Process Optimization for Self-Tuning Control

Marco, A.

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

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PDF Project Page [BibTex]

PDF Project Page [BibTex]


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Adaptive and Learning Concepts in Hydraulic Force Control

Doerr, A.

University of Stuttgart, September 2015 (mastersthesis)

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[BibTex]

[BibTex]


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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.

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PDF Video Project Page [BibTex]

PDF Video Project Page [BibTex]


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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.

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PDF DOI Project Page [BibTex]

PDF DOI Project Page [BibTex]


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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)

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PDF DOI Project Page [BibTex]

PDF DOI Project Page [BibTex]


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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)

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PDF DOI Project Page [BibTex]

PDF DOI Project Page [BibTex]


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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.

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PDF DOI Project Page [BibTex]

PDF DOI Project Page [BibTex]


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Lernende Roboter

Trimpe, S.

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

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link (url) [BibTex]

link (url) [BibTex]


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Policy Search for Imitation Learning

Doerr, A.

University of Stuttgart, January 2015 (thesis)

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link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Real-Time Object Detection, Localization and Verification for Fast Robotic Depalletizing

Holz, D., Topalidou-Kyniazopoulou, A., Stueckler, J., Behnke, S.

In IEEE International Conference on Intelligent Robots and Systems (IROS), 2015 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Dense Continuous-Time Tracking and Mapping with Rolling Shutter RGB-D Cameras

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

In IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 2015, {[video][supplementary][datasets]} (inproceedings)

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[BibTex]

[BibTex]


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Perception of Deformable Objects and Compliant Manipulation for Service Robots

Stueckler, J., Behnke, S.

In Soft Robotics: From Theory to Applications, Springer, 2015 (inbook)

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link (url) [BibTex]

link (url) [BibTex]


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Large-Scale Direct SLAM with Stereo Cameras

Engel, J., Stueckler, J., Cremers, D.

In IEEE International Conference on Intelligent Robots and Systems (IROS), 2015 (inproceedings)

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[BibTex]

[BibTex]


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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.

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Web PDF Project Page [BibTex]


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Efficient Dense Rigid-Body Motion Segmentation and Estimation in RGB-D Video

Stueckler, J., Behnke, S.

International Journal of Computer Vision (IJCV), 113(3):233-245, 2015 (article)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Motion Cooperation: Smooth Piece-Wise Rigid Scene Flow from RGB-D Images

Jaimez, M., Souiai, M., Stueckler, J., Gonzalez-Jimenez, J., Cremers, D.

In Proc. of the Int. Conference on 3D Vision (3DV), October 2015, {[video]} (inproceedings)

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[BibTex]

[BibTex]


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Super-Resolution Keyframe Fusion for 3D Modeling with High-Quality Textures

Maier, R., Stueckler, J., Cremers, D.

In International Conference on 3D Vision (3DV), October 2015, {[slides] [poster]} (inproceedings)

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[BibTex]

[BibTex]


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Reconstructing Street-Scenes in Real-Time From a Driving Car

Usenko, V., Engel, J., Stueckler, J., Cremers, D.

In Proc. of the Int. Conference on 3D Vision (3DV), October 2015 (inproceedings)

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[BibTex]

[BibTex]


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Efficient 3D Object Perception and Grasp Planning for Mobile Manipulation in Domestic Environments

Stueckler, J., Steffens, R., Holz, D., Behnke, S.

Robotics and Autonomous Systems (RAS), 61(10):1106-1115, 2013 (article)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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NimbRo@Home: Winning Team of the RoboCup@Home Competition 2012

Stueckler, J., Badami, I., Droeschel, D., Gräve, K., Holz, D., McElhone, M., Nieuwenhuisen, M., Schreiber, M., Schwarz, M., Behnke, S.

In RoboCup 2012, Robot Soccer World Cup XVI, pages: 94-105, Springer, 2013 (inbook)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Efficient Dense 3D Rigid-Body Motion Segmentation in RGB-D Video

Stueckler, J., Behnke, S.

In Proc. of the British Machine Vision Conference (BMVC), 2013 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Mobile bin picking with an anthropomorphic service robot

Nieuwenhuisen, M., Droeschel, D., Holz, D., Stueckler, J., Berner, A., Li, J., Klein, R., Behnke, S.

In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), pages: 2327-2334, May 2013 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Multi-resolution surfel mapping and real-time pose tracking using a continuously rotating 2D laser scanner

Schadler, M., Stueckler, J., Behnke, S.

In Proc. of the IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pages: 1-6, October 2013 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Joint detection and pose tracking of multi-resolution surfel models in RGB-D

McElhone, M., Stueckler, J., Behnke, S.

In Proc. of the European Conference on Mobile Robots (ECMR), pages: 131-137, IEEE, 2013 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Distinctive 3D surface entropy features for place recognition.

Fiolka, T., Stueckler, J., Klein, D. A., Schulz, D., Behnke, S.

In Proc. of the European Conference on Mobile Robots (ECMR), pages: 204-209, IEEE, 2013 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Combining contour and shape primitives for object detection and pose estimation of prefabricated parts

Berner, A., Li, J., Holz, D., Stueckler, J., Behnke, S., Klein, R.

In Proc. of the 20th IEEE International Conference on Image Processing (ICIP), pages: 3326-3330, sep 2013 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Hierarchical Object Discovery and Dense Modelling From Motion Cues in RGB-D Video

Stueckler, J., Behnke, S.

In Proc. of the 23rd International Joint Conference on Artificial Intelligence (IJCAI), IJCAI/AAAI, 2013 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]

2006


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See, walk, and kick: Humanoid robots start to play soccer

Behnke, S., Schreiber, M., Stueckler, J., Renner, R., Strasdat, H.

In Proc. of the IEEE-RAS Int. Conf. on Humanoid Robots (Humanoids), pages: 497-503, December 2006 (inproceedings)

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link (url) DOI [BibTex]

2006


link (url) DOI [BibTex]