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2018


Deep Reinforcement Learning for Event-Triggered Control
Deep Reinforcement Learning for Event-Triggered Control

Baumann, D., Zhu, J., Martius, G., Trimpe, S.

In Proceedings of the 57th IEEE International Conference on Decision and Control (CDC), pages: 943-950, 57th IEEE International Conference on Decision and Control (CDC), December 2018 (inproceedings)

al ics

arXiv PDF DOI Project Page Project Page [BibTex]

2018


arXiv PDF DOI Project Page Project Page [BibTex]


Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds
Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds

Reeb, D., Doerr, A., Gerwinn, S., Rakitsch, B.

In Proceedings Neural Information Processing Systems, Neural Information Processing Systems (NIPS) , December 2018 (inproceedings)

Abstract
Gaussian Processes (GPs) are a generic modelling tool for supervised learning. While they have been successfully applied on large datasets, their use in safety critical applications is hindered by the lack of good performance guarantees. To this end, we propose a method to learn GPs and their sparse approximations by directly optimizing a PAC-Bayesian bound on their generalization performance, instead of maximizing the marginal likelihood. Besides its theoretical appeal, we find in our evaluation that our learning method is robust and yields significantly better generalization guarantees than other common GP approaches on several regression benchmark datasets.

ics

[BibTex]

[BibTex]


Efficient Encoding of Dynamical Systems through Local Approximations
Efficient Encoding of Dynamical Systems through Local Approximations

Solowjow, F., Mehrjou, A., Schölkopf, B., Trimpe, S.

In Proceedings of the 57th IEEE International Conference on Decision and Control (CDC), pages: 6073 - 6079 , Miami, Fl, USA, December 2018 (inproceedings)

ei ics

arXiv PDF DOI Project Page [BibTex]

arXiv PDF DOI Project Page [BibTex]


Depth Control of Underwater Robots using Sliding Modes and Gaussian Process Regression
Depth Control of Underwater Robots using Sliding Modes and Gaussian Process Regression

Lima, G. S., Bessa, W. M., Trimpe, S.

In Proceeding of the 15th Latin American Robotics Symposium, João Pessoa, Brazil, 15th Latin American Robotics Symposium, November 2018 (inproceedings)

Abstract
The development of accurate control systems for underwater robotic vehicles relies on the adequate compensation for hydrodynamic effects. In this work, a new robust control scheme is presented for remotely operated underwater vehicles. In order to meet both robustness and tracking requirements, sliding mode control is combined with Gaussian process regression. The convergence properties of the closed-loop signals are analytically proven. Numerical results confirm the stronger improved performance of the proposed control scheme.

ics

[BibTex]

[BibTex]


Gait learning for soft microrobots controlled by light fields
Gait learning for soft microrobots controlled by light fields

Rohr, A. V., Trimpe, S., Marco, A., Fischer, P., Palagi, S.

In International Conference on Intelligent Robots and Systems (IROS) 2018, pages: 6199-6206, International Conference on Intelligent Robots and Systems 2018, October 2018 (inproceedings)

Abstract
Soft microrobots based on photoresponsive materials and controlled by light fields can generate a variety of different gaits. This inherent flexibility can be exploited to maximize their locomotion performance in a given environment and used to adapt them to changing environments. However, because of the lack of accurate locomotion models, and given the intrinsic variability among microrobots, analytical control design is not possible. Common data-driven approaches, on the other hand, require running prohibitive numbers of experiments and lead to very sample-specific results. Here we propose a probabilistic learning approach for light-controlled soft microrobots based on Bayesian Optimization (BO) and Gaussian Processes (GPs). The proposed approach results in a learning scheme that is highly data-efficient, enabling gait optimization with a limited experimental budget, and robust against differences among microrobot samples. These features are obtained by designing the learning scheme through the comparison of different GP priors and BO settings on a semisynthetic data set. The developed learning scheme is validated in microrobot experiments, resulting in a 115% improvement in a microrobot’s locomotion performance with an experimental budget of only 20 tests. These encouraging results lead the way toward self-adaptive microrobotic systems based on lightcontrolled soft microrobots and probabilistic learning control.

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arXiv IEEE Xplore DOI Project Page [BibTex]

arXiv IEEE Xplore DOI Project Page [BibTex]


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Learning-Based Robust Model Predictive Control with State-Dependent Uncertainty

Soloperto, R., Müller, M. A., Trimpe, S., Allgöwer, F.

In Proceedings of the IFAC Conference on Nonlinear Model Predictive Control (NMPC), Madison, Wisconsin, USA, 6th IFAC Conference on Nonlinear Model Predictive Control, August 2018 (inproceedings)

ics

PDF [BibTex]

PDF [BibTex]


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Assessment Of Atypical Motor Development In Infants Through Toy-Stimulated Play And Center Of Pressure Analysis

Zhao, S., Mohan, M., Torres, W. O., Bogen, D. K., Shofer, F. S., Prosser, L., Loeb, H., Johnson, M. J.

In Proceedings of the Annual Rehabilitation Engineering and Assistive Technology Society of North America (RESNA) Conference, Arlington, USA, July 2018 (inproceedings)

Abstract
There is a need to identify measures and create systems to assess motor development at an early stage. Center of Pressure (CoP) is a quantifiable metric that has been used to investigate postural control in healthy young children [6], children with CP [7], and infants just beginning to sit [8]. It was found that infants born prematurely exhibit different patterns of CoP movement than infants born full-term when assessing development impairments relating to postural control [9]. Preterm infants exhibited greater CoP excursions but had greater variability in their movements than fullterm infants. Our solution, the Play And Neuro-Development Assessment (PANDA) Gym, is a sensorized environment that aims to provide early diagnosis of neuromotor disorder in infants and improve current screening processes by providing quantitative measures rather than subjective ones, and promoting natural play with the stimulus of toys. Previous studies have documented stages in motor development in infants [10, 11], and developmental delays could become more apparent through toy interactions. This study examines the sensitivity of the pressure-sensitive mat subsystem to detect differences in CoP movement patterns for preterm and fullterm infants less than 6 months of age, with varying risk levels. This study aims to distinguish between typical and atypical motor development through assessment of the CoP data of infants in a natural play environment, in conditions where movement may be further stimulated with the presence of a toy.

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link (url) [BibTex]

link (url) [BibTex]


Probabilistic Recurrent State-Space Models
Probabilistic Recurrent State-Space Models

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

In Proceedings of the International Conference on Machine Learning (ICML), International Conference on Machine Learning (ICML), July 2018 (inproceedings)

Abstract
State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification. Deterministic versions of SSMs (e.g., LSTMs) proved extremely successful in modeling complex time-series data. Fully probabilistic SSMs, however, unfortunately often prove hard to train, even for smaller problems. To overcome this limitation, we propose a scalable initialization and training algorithm based on doubly stochastic variational inference and Gaussian processes. In the variational approximation we propose in contrast to related approaches to fully capture the latent state temporal correlations to allow for robust training.

am ics

arXiv pdf Project Page [BibTex]

arXiv pdf Project Page [BibTex]


Event-triggered Learning for Resource-efficient Networked Control
Event-triggered Learning for Resource-efficient Networked Control

Solowjow, F., Baumann, D., Garcke, J., Trimpe, S.

In Proceedings of the American Control Conference (ACC), pages: 6506 - 6512, American Control Conference, June 2018 (inproceedings)

ics

arXiv PDF DOI Project Page [BibTex]

arXiv PDF DOI Project Page [BibTex]


Evaluating Low-Power Wireless Cyber-Physical Systems
Evaluating Low-Power Wireless Cyber-Physical Systems

Baumann, D., Mager, F., Singh, H., Zimmerling, M., Trimpe, S.

In Proceedings of the IEEE Workshop on Benchmarking Cyber-Physical Networks and Systems (CPSBench), pages: 13-18, IEEE Workshop on Benchmarking Cyber-Physical Networks and Systems (CPSBench), April 2018 (inproceedings)

ics

arXiv PDF DOI Project Page [BibTex]

arXiv PDF DOI Project Page [BibTex]


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Travelling Ultrasonic Wave Enhances Keyclick Sensation

Gueorguiev, D., Kaci, A., Amberg, M., Giraud, F., Lemaire-Semail, B.

In Haptics: Science, Technology, and Applications, pages: 302-312, Springer International Publishing, Cham, 2018 (inproceedings)

Abstract
A realistic keyclick sensation is a serious challenge for haptic feedback since vibrotactile rendering faces the limitation of the absence of contact force as experienced on physical buttons. It has been shown that creating a keyclick sensation is possible with stepwise ultrasonic friction modulation. However, the intensity of the sensation is limited by the impedance of the fingertip and by the absence of a lateral force component external to the finger. In our study, we compare this technique to rendering with an ultrasonic travelling wave, which exerts a lateral force on the fingertip. For both techniques, participants were asked to report the detection (or not) of a keyclick during a forced choice one interval procedure. In experiment 1, participants could press the surface as many time as they wanted for a given trial. In experiment 2, they were constrained to press only once. The results show a lower perceptual threshold for travelling waves. Moreover, participants pressed less times per trial and exerted smaller normal force on the surface. The subjective quality of the sensation was found similar for both techniques. In general, haptic feedback based on travelling ultrasonic waves is promising for applications without lateral motion of the finger.

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[BibTex]

[BibTex]


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On Time Optimization of Centroidal Momentum Dynamics

Ponton, B., Herzog, A., Del Prete, A., Schaal, S., Righetti, L.

In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages: 5776-5782, IEEE, Brisbane, Australia, 2018 (inproceedings)

Abstract
Recently, the centroidal momentum dynamics has received substantial attention to plan dynamically consistent motions for robots with arms and legs in multi-contact scenarios. However, it is also non convex which renders any optimization approach difficult and timing is usually kept fixed in most trajectory optimization techniques to not introduce additional non convexities to the problem. But this can limit the versatility of the algorithms. In our previous work, we proposed a convex relaxation of the problem that allowed to efficiently compute momentum trajectories and contact forces. However, our approach could not minimize a desired angular momentum objective which seriously limited its applicability. Noticing that the non-convexity introduced by the time variables is of similar nature as the centroidal dynamics one, we propose two convex relaxations to the problem based on trust regions and soft constraints. The resulting approaches can compute time-optimized dynamically consistent trajectories sufficiently fast to make the approach realtime capable. The performance of the algorithm is demonstrated in several multi-contact scenarios for a humanoid robot. In particular, we show that the proposed convex relaxation of the original problem finds solutions that are consistent with the original non-convex problem and illustrate how timing optimization allows to find motion plans that would be difficult to plan with fixed timing † †Implementation details and demos can be found in the source code available at https://git-amd.tuebingen.mpg.de/bponton/timeoptimization.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Exploring Fingers’ Limitation of Texture Density Perception on Ultrasonic Haptic Displays

Kalantari, F., Gueorguiev, D., Lank, E., Bremard, N., Grisoni, L.

In Haptics: Science, Technology, and Applications, pages: 354-365, Springer International Publishing, Cham, 2018 (inproceedings)

Abstract
Recent research in haptic feedback is motivated by the crucial role that tactile perception plays in everyday touch interactions. In this paper, we describe psychophysical experiments to investigate the perceptual threshold of individual fingers on both the right and left hand of right-handed participants using active dynamic touch for spatial period discrimination of both sinusoidal and square-wave gratings on ultrasonic haptic touchscreens. Both one-finger and multi-finger touch were studied and compared. Our results indicate that users' finger identity (index finger, middle finger, etc.) significantly affect the perception of both gratings in the case of one-finger exploration. We show that index finger and thumb are the most sensitive in all conditions whereas little finger followed by ring are the least sensitive for haptic perception. For multi-finger exploration, the right hand was found to be more sensitive than the left hand for both gratings. Our findings also demonstrate similar perception sensitivity between multi-finger exploration and the index finger of users' right hands (i.e. dominant hand in our study), while significant difference was found between single and multi-finger perception sensitivity for the left hand.

hi

[BibTex]

[BibTex]


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Unsupervised Contact Learning for Humanoid Estimation and Control

Rotella, N., Schaal, S., Righetti, L.

In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages: 411-417, IEEE, Brisbane, Australia, 2018 (inproceedings)

Abstract
This work presents a method for contact state estimation using fuzzy clustering to learn contact probability for full, six-dimensional humanoid contacts. The data required for training is solely from proprioceptive sensors - endeffector contact wrench sensors and inertial measurement units (IMUs) - and the method is completely unsupervised. The resulting cluster means are used to efficiently compute the probability of contact in each of the six endeffector degrees of freedom (DoFs) independently. This clustering-based contact probability estimator is validated in a kinematics-based base state estimator in a simulation environment with realistic added sensor noise for locomotion over rough, low-friction terrain on which the robot is subject to foot slip and rotation. The proposed base state estimator which utilizes these six DoF contact probability estimates is shown to perform considerably better than that which determines kinematic contact constraints purely based on measured normal force.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Learning Task-Specific Dynamics to Improve Whole-Body Control

Gams, A., Mason, S., Ude, A., Schaal, S., Righetti, L.

In Hua, IEEE, Beijing, China, November 2018 (inproceedings)

Abstract
In task-based inverse dynamics control, reference accelerations used to follow a desired plan can be broken down into feedforward and feedback trajectories. The feedback term accounts for tracking errors that are caused from inaccurate dynamic models or external disturbances. On underactuated, free-floating robots, such as humanoids, high feedback terms can be used to improve tracking accuracy; however, this can lead to very stiff behavior or poor tracking accuracy due to limited control bandwidth. In this paper, we show how to reduce the required contribution of the feedback controller by incorporating learned task-space reference accelerations. Thus, we i) improve the execution of the given specific task, and ii) offer the means to reduce feedback gains, providing for greater compliance of the system. With a systematic approach we also reduce heuristic tuning of the model parameters and feedback gains, often present in real-world experiments. In contrast to learning task-specific joint-torques, which might produce a similar effect but can lead to poor generalization, our approach directly learns the task-space dynamics of the center of mass of a humanoid robot. Simulated and real-world results on the lower part of the Sarcos Hermes humanoid robot demonstrate the applicability of the approach.

am mg

link (url) [BibTex]

link (url) [BibTex]


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An MPC Walking Framework With External Contact Forces

Mason, S., Rotella, N., Schaal, S., Righetti, L.

In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages: 1785-1790, IEEE, Brisbane, Australia, May 2018 (inproceedings)

Abstract
In this work, we present an extension to a linear Model Predictive Control (MPC) scheme that plans external contact forces for the robot when given multiple contact locations and their corresponding friction cone. To this end, we set up a two-step optimization problem. In the first optimization, we compute the Center of Mass (CoM) trajectory, foot step locations, and introduce slack variables to account for violating the imposed constraints on the Zero Moment Point (ZMP). We then use the slack variables to trigger the second optimization, in which we calculate the optimal external force that compensates for the ZMP tracking error. This optimization considers multiple contacts positions within the environment by formulating the problem as a Mixed Integer Quadratic Program (MIQP) that can be solved at a speed between 100-300 Hz. Once contact is created, the MIQP reduces to a single Quadratic Program (QP) that can be solved in real-time ({\textless}; 1kHz). Simulations show that the presented walking control scheme can withstand disturbances 2-3× larger with the additional force provided by a hand contact.

am mg

link (url) DOI [BibTex]

link (url) DOI [BibTex]

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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Qualitative User Reactions to a Hand-Clapping Humanoid Robot

Fitter, N. T., Kuchenbecker, K. J.

In Social Robotics: 8th International Conference, ICSR 2016, Kansas City, MO, USA, November 1-3, 2016 Proceedings, 9979, pages: 317-327, Lecture Notes in Artificial Intelligence, Springer International Publishing, November 2016, Oral presentation given by Fitter (inproceedings)

hi

[BibTex]

[BibTex]


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Designing and Assessing Expressive Open-Source Faces for the Baxter Robot

Fitter, N. T., Kuchenbecker, K. J.

In Social Robotics: 8th International Conference, ICSR 2016, Kansas City, MO, USA, November 1-3, 2016 Proceedings, 9979, pages: 340-350, Lecture Notes in Artificial Intelligence, Springer International Publishing, November 2016, Oral presentation given by Fitter (inproceedings)

hi

[BibTex]

[BibTex]


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Rhythmic Timing in Playful Human-Robot Social Motor Coordination

Fitter, N. T., Hawkes, D. T., Kuchenbecker, K. J.

In Social Robotics: 8th International Conference, ICSR 2016, Kansas City, MO, USA, November 1-3, 2016 Proceedings, 9979, pages: 296-305, Lecture Notes in Artificial Intelligence, Springer International Publishing, November 2016, Oral presentation given by Fitter (inproceedings)

hi

[BibTex]

[BibTex]


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Using IMU Data to Demonstrate Hand-Clapping Games to a Robot

Fitter, N. T., Kuchenbecker, K. J.

In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pages: 851 - 856, October 2016, Interactive presentation given by Fitter (inproceedings)

hi

[BibTex]

[BibTex]


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ProtonPack: A Visuo-Haptic Data Acquisition System for Robotic Learning of Surface Properties

Burka, A., Hu, S., Helgeson, S., Krishnan, S., Gao, Y., Hendricks, L. A., Darrell, T., Kuchenbecker, K. J.

In Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pages: 58-65, 2016, Oral presentation given by Burka (inproceedings)

hi

Project Page [BibTex]

Project Page [BibTex]


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Equipping the Baxter Robot with Human-Inspired Hand-Clapping Skills

Fitter, N. T., Kuchenbecker, K. J.

In Proceedings of the IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pages: 105-112, 2016 (inproceedings)

hi

[BibTex]

[BibTex]


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Comparison of vibro-acoustic performance metrics in the design and optimization of stiffened composite fuselages

Serhat, G., Basdogan, I.

In Proceedings of International Congress and Exposition of Noise Control Engineering (INTER-NOISE), Hamburg, Germany, August 2016 (inproceedings)

Abstract
In this paper, a comparison of preliminary design methodologies for optimization of stiffened, fiber-reinforced composite fuselages for vibro-acoustic requirements is presented. Fuselage stiffness properties are modelled using lamination parameters and their effect on the vibro-acoustic performance is investigated using two different approaches. First method, only considers the structural model in order to explore the effect of design variables on fuselage vibrations. The simplified estimation of the acoustic behavior without considering fluid-structure interaction brings certain advantages such as reduced modelling effort and computational cost. In this case, the performance metric is chosen as equivalent radiated power (ERP) which is a well-known criterion in the prediction of structure-born noise. Second method, utilizes coupled vibro-acoustic models to predict the sound pressure levels (SPL) inside the fuselage. ERP is calculated both for bay panels and fuselage section and then compared with the SPL results. The response surfaces of each metric are determined as a function of lamination parameters and their overall difference is quantified. ERP approach proves its merit provided that a sufficiently accurate model is used. The results demonstrate the importance of the simplifications made in the modelling and the selection of analysis approach in vibro-acoustic design of fuselages.

hi

[BibTex]

[BibTex]


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Reproducing a Laser Pointer Dot on a Secondary Projected Screen

Hu, S., Kuchenbecker, K. J.

In Proceedings of the IEEE International Conference on Advanced Intelligent Mechatronics (AIM), pages: 1645-1650, 2016, Oral presentation given by Hu (inproceedings)

hi

[BibTex]

[BibTex]


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Effect of Aspect Ratio and Boundary Conditions on the Eigenfrequency Optimization of Composite Panels Using Lamination Parameters

Serhat, G., Basdogan, I.

In Proceedings of the ASMO UK International Conference on Numerical Optimisation Methods for Engineering Design, pages: 160–168, Munich, Germany, July 2016 (inproceedings)

Abstract
Eigenfrequency optimization of laminated composite panels is a common engineering problem. This process mostly involves designing stiffness properties of the structure. Optimal results can differ significantly depending on the values of the model parameters and the metrics used for the optimization. Building the know-how on this matter is crucial for choosing the appropriate design methodologies as well as validation and justification of prospective results. In this paper, effects of aspect ratio and boundary conditions on eigenfrequency optimization of composite panels by altering stiffness properties are investigated. Lamination parameters are chosen as design variables which are used in the modeling of stiffness tensors. This technique enables representation of overall stiffness characteristics and provides a convex design space. Fundamental frequency and difference between fundamental and second natural frequencies are maximized as design objectives. Optimization studies incorporating different models and responses are performed. Optimal lamination parameters and response values are provided for each case and the effects of model parameters on the solutions are quantified. The results indicate that trends of the optima change for different aspect ratio ranges and boundary conditions. Moreover, convergence occurs beyond certain critical values of the model parameters which may cause an optimization study to be redundant.

hi

[BibTex]

[BibTex]


Robust Gaussian Filtering using a Pseudo Measurement
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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Multi-objective optimization of stiffened, fiber-reinforced composite fuselages for mechanical and vibro-acoustic requirements

Serhat, G., Faria, T. G., Basdogan, I.

In Proceedings of AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Washington, USA, June 2016 (inproceedings)

Abstract
In this paper, a preliminary design methodology for optimization of stiffened, fiber-reinforced composite fuselages for combined mechanical and vibro-acoustic requirements is presented. Laminate stiffness distributions are represented using the method called lamination parameters which is known to provide a convex solution space. Single-objective and multi-objective optimization studies are carried out in order to find optimal stiffness distributions. Performance metrics for acoustical behavior are chosen as maximum fundamental frequency and minimum equivalent radiated power. The mechanical performance metric is chosen as the maximum stiffness. The results show that the presented methodology works effectively and it can be used to improve load-carrying and acoustical performances simultaneously.

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DOI [BibTex]

DOI [BibTex]


Automatic LQR Tuning Based on Gaussian Process Global Optimization
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.

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Video - Automatic LQR Tuning Based on Gaussian Process Global Optimization - ICRA 2016 Video - Automatic Controller Tuning on a Two-legged Robot PDF DOI Project Page [BibTex]

Video - Automatic LQR Tuning Based on Gaussian Process Global Optimization - ICRA 2016 Video - Automatic Controller Tuning on a Two-legged Robot PDF DOI Project Page [BibTex]


Depth-based Object Tracking Using a Robust Gaussian Filter
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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Deep Learning for Tactile Understanding From Visual and Haptic Data

Gao, Y., Hendricks, L. A., Kuchenbecker, K. J., Darrell, T.

In Proceedings of the IEEE International Conference on Robotics and Automation, pages: 536-543, May 2016, Oral presentation given by Gao (inproceedings)

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[BibTex]

[BibTex]


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Robust Tactile Perception of Artificial Tumors Using Pairwise Comparisons of Sensor Array Readings

Hui, J. C. T., Block, A. E., Taylor, C. J., Kuchenbecker, K. J.

In Proceedings of the IEEE Haptics Symposium, pages: 305-312, Philadelphia, Pennsylvania, USA, April 2016, Oral presentation given by Hui (inproceedings)

hi

[BibTex]

[BibTex]


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Data-Driven Comparison of Four Cutaneous Displays for Pinching Palpation in Robotic Surgery

Brown, J. D., Ibrahim, M., Chase, E. D. Z., Pacchierotti, C., Kuchenbecker, K. J.

In Proceedings of the IEEE Haptics Symposium, pages: 147-154, Philadelphia, Pennsylvania, USA, April 2016, Oral presentation given by Brown (inproceedings)

hi

[BibTex]

[BibTex]


Multisensory Robotic Therapy through Motion Capture and Imitation for Children with ASD
Multisensory Robotic Therapy through Motion Capture and Imitation for Children with ASD

Burns, R., Nizambad, S., Park, C. H., Jeon, M., Howard, A.

Proceedings of the American Society of Engineering Education, Mid-Atlantic Section, Spring Conference, April 2016 (conference)

Abstract
It is known that children with autism have difficulty with emotional communication. As the population of children with autism increases, it is crucial we create effective therapeutic programs that will improve their communication skills. We present an interactive robotic system that delivers emotional and social behaviors for multi­sensory therapy for children with autism spectrum disorders. Our framework includes emotion­-based robotic gestures and facial expressions, as well as tracking and understanding the child’s responses through Kinect motion capture.

hi

link (url) [BibTex]

link (url) [BibTex]


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Design and Implementation of a Visuo-Haptic Data Acquisition System for Robotic Learning of Surface Properties

Burka, A., Hu, S., Helgeson, S., Krishnan, S., Gao, Y., Hendricks, L. A., Darrell, T., Kuchenbecker, K. J.

In Proceedings of the IEEE Haptics Symposium, pages: 350-352, April 2016, Work-in-progress paper. Poster presentation given by Burka (inproceedings)

hi

Project Page [BibTex]

Project Page [BibTex]


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Psychophysical Power Optimization of Friction Modulation for Tactile Interfaces

Sednaoui, T., Vezzoli, E., Gueorguiev, D., Amberg, M., Chappaz, C., Lemaire-Semail, B.

In Haptics: Perception, Devices, Control, and Applications, pages: 354-362, Springer International Publishing, Cham, 2016 (inproceedings)

Abstract
Ultrasonic vibration and electrovibration can modulate the friction between a surface and a sliding finger. The power consumption of these devices is critical to their integration in modern mobile devices such as smartphones. This paper presents a simple control solution to reduce up to 68.8 {\%} this power consumption by taking advantage of the human perception limits.

hi

[BibTex]

[BibTex]


Effect of Waveform in Haptic Perception of Electrovibration on Touchscreens
Effect of Waveform in Haptic Perception of Electrovibration on Touchscreens

Vardar, Y., Güçlü, B., Basdogan, C.

In Haptics: Perception, Devices, Control, and Applications, pages: 190-203, Springer International Publishing, Cham, 2016 (inproceedings)

Abstract
The perceived intensity of electrovibration can be altered by modulating the amplitude, frequency, and waveform of the input voltage signal applied to the conductive layer of a touchscreen. Even though the effect of the first two has been already investigated for sinusoidal signals, we are not aware of any detailed study investigating the effect of the waveform on our haptic perception in the domain of electrovibration. This paper investigates how input voltage waveform affects our haptic perception of electrovibration on touchscreens. We conducted absolute detection experiments using square wave and sinusoidal input signals at seven fundamental frequencies (15, 30, 60, 120, 240, 480 and 1920 Hz). Experimental results depicted the well-known U-shaped tactile sensitivity across frequencies. However, the sensory thresholds were lower for the square wave than the sinusoidal wave at fundamental frequencies less than 60 Hz while they were similar at higher frequencies. Using an equivalent circuit model of a finger-touchscreen system, we show that the sensation difference between the waveforms at low fundamental frequencies can be explained by frequency-dependent electrical properties of human skin and the differential sensitivity of mechanoreceptor channels to individual frequency components in the electrostatic force. As a matter of fact, when the electrostatic force waveforms are analyzed in the frequency domain based on human vibrotactile sensitivity data from the literature [15], the electrovibration stimuli caused by square-wave input signals at all the tested frequencies in this study are found to be detected by the Pacinian psychophysical channel.

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vardar_eurohaptics_2016 [BibTex]

vardar_eurohaptics_2016 [BibTex]


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Phase transitions and optimal algorithms in high-dimensional Gaussian mixture clustering

Lesieur, T., De Bacco, C., Banks, J., Krzakala, F., Moore, C., Zdeborová, L.

In Communication, Control, and Computing (Allerton), 2016 54th Annual Allerton Conference on, pages: 601-608, 2016 (inproceedings)

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Preprint link (url) [BibTex]

Preprint link (url) [BibTex]


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On the Effects of Measurement Uncertainty in Optimal Control of Contact Interactions

Ponton, B., Schaal, S., Righetti, L.

In The 12th International Workshop on the Algorithmic Foundations of Robotics WAFR, Berkeley, USA, 2016 (inproceedings)

Abstract
Stochastic Optimal Control (SOC) typically considers noise only in the process model, i.e. unknown disturbances. However, in many robotic applications involving interaction with the environment, such as locomotion and manipulation, uncertainty also comes from lack of precise knowledge of the world, which is not an actual disturbance. We analyze the effects of also considering noise in the measurement model, by devel- oping a SOC algorithm based on risk-sensitive control, that includes the dynamics of an observer in such a way that the control law explicitly de- pends on the current measurement uncertainty. In simulation results on a simple 2D manipulator, we have observed that measurement uncertainty leads to low impedance behaviors, a result in contrast with the effects of process noise that creates stiff behaviors. This suggests that taking into account measurement uncertainty could be a potentially very interesting way to approach problems involving uncertain contact interactions.

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link (url) [BibTex]

link (url) [BibTex]


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A Convex Model of Momentum Dynamics for Multi-Contact Motion Generation

Ponton, B., Herzog, A., Schaal, S., Righetti, L.

In 2016 IEEE-RAS 16th International Conference on Humanoid Robots Humanoids, pages: 842-849, IEEE, Cancun, Mexico, 2016 (inproceedings)

Abstract
Linear models for control and motion generation of humanoid robots have received significant attention in the past years, not only due to their well known theoretical guarantees, but also because of practical computational advantages. However, to tackle more challenging tasks and scenarios such as locomotion on uneven terrain, a more expressive model is required. In this paper, we are interested in contact interaction-centered motion optimization based on the momentum dynamics model. This model is non-linear and non-convex; however, we find a relaxation of the problem that allows us to formulate it as a single convex quadratically-constrained quadratic program (QCQP) that can be very efficiently optimized and is useful for multi-contact planning. This convex model is then coupled to the optimization of end-effector contact locations using a mixed integer program, which can also be efficiently solved. This becomes relevant e.g. to recover from external pushes, where a predefined stepping plan is likely to fail and an online adaptation of the contact location is needed. The performance of our algorithm is demonstrated in several multi-contact scenarios for a humanoid robot.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Inertial Sensor-Based Humanoid Joint State Estimation

Rotella, N., Mason, S., Schaal, S., Righetti, L.

In 2016 IEEE International Conference on Robotics and Automation (ICRA), pages: 1825-1831, IEEE, Stockholm, Sweden, 2016 (inproceedings)

Abstract
This work presents methods for the determination of a humanoid robot's joint velocities and accelerations directly from link-mounted Inertial Measurement Units (IMUs) each containing a three-axis gyroscope and a three-axis accelerometer. No information about the global pose of the floating base or its links is required and precise knowledge of the link IMU poses is not necessary due to presented calibration routines. Additionally, a filter is introduced to fuse gyroscope angular velocities with joint position measurements and compensate the computed joint velocities for time-varying gyroscope biases. The resulting joint velocities are subject to less noise and delay than filtered velocities computed from numerical differentiation of joint potentiometer signals, leading to superior performance in joint feedback control as demonstrated in experiments performed on a SARCOS hydraulic humanoid.

am mg

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Stepping Stabilization Using a Combination of DCM Tracking and Step Adjustment

Khadiv, M., Kleff, S., Herzog, A., Moosavian, S. A. A., Schaal, S., Righetti, L.

In 2016 4th International Conference on Robotics and Mechatronics (ICROM), pages: 130-135, IEEE, Teheran, Iran, 2016 (inproceedings)

Abstract
In this paper, a method for stabilizing biped robots stepping by a combination of Divergent Component of Motion (DCM) tracking and step adjustment is proposed. In this method, the DCM trajectory is generated, consistent with the predefined footprints. Furthermore, a swing foot trajectory modification strategy is proposed to adapt the landing point, using DCM measurement. In order to apply the generated trajectories to the full robot, a Hierarchical Inverse Dynamics (HID) is employed. The HID enables us to use different combinations of the DCM tracking and step adjustment for stabilizing different biped robots. Simulation experiments on two scenarios for two different simulated robots, one with active ankles and the other with passive ankles, are carried out. Simulation results demonstrate the effectiveness of the proposed method for robots with both active and passive ankles.

am mg

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Structured contact force optimization for kino-dynamic motion generation

Herzog, A., Schaal, S., Righetti, L.

In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages: 2703-2710, IEEE, Daejeon, South Korea, 2016 (inproceedings)

Abstract
Optimal control approaches in combination with trajectory optimization have recently proven to be a promising control strategy for legged robots. Computationally efficient and robust algorithms were derived using simplified models of the contact interaction between robot and environment such as the linear inverted pendulum model (LIPM). However, as humanoid robots enter more complex environments, less restrictive models become increasingly important. As we leave the regime of linear models, we need to build dedicated solvers that can compute interaction forces together with consistent kinematic plans for the whole-body. In this paper, we address the problem of planning robot motion and interaction forces for legged robots given predefined contact surfaces. The motion generation process is decomposed into two alternating parts computing force and motion plans in coherence. We focus on the properties of the momentum computation leading to sparse optimal control formulations to be exploited by a dedicated solver. In our experiments, we demonstrate that our motion generation algorithm computes consistent contact forces and joint trajectories for our humanoid robot. We also demonstrate the favorable time complexity due to our formulation and composition of the momentum equations.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Balancing and Walking Using Full Dynamics LQR Control With Contact Constraints

Mason, S., Rotella, N., Schaal, S., Righetti, L.

In 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), pages: 63-68, IEEE, Cancun, Mexico, 2016 (inproceedings)

Abstract
Torque control algorithms which consider robot dynamics and contact constraints are important for creating dynamic behaviors for humanoids. As computational power increases, algorithms tend to also increase in complexity. However, it is not clear how much complexity is really required to create controllers which exhibit good performance. In this paper, we study the capabilities of a simple approach based on contact consistent LQR controllers designed around key poses to control various tasks on a humanoid robot. We present extensive experimental results on a hydraulic, torque controlled humanoid performing balancing and stepping tasks. This feedback control approach captures the necessary synergies between the DoFs of the robot to guarantee good control performance. We show that for the considered tasks, it is only necessary to re-linearize the dynamics of the robot at different contact configurations and that increasing the number of LQR controllers along desired trajectories does not improve performance. Our result suggest that very simple controllers can yield good performance competitive with current state of the art, but more complex, optimization-based whole-body controllers. A video of the experiments can be found at https://youtu.be/5T08CNKV1hw.

am mg

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Step Timing Adjustement: a Step toward Generating Robust Gaits

Khadiv, M., Herzog, A., Moosavian, S. A. A., Righetti, L.

In 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), pages: 35-42, IEEE, Cancun, Mexico, 2016 (inproceedings)

Abstract
Step adjustment for humanoid robots has been shown to improve robustness in gaits. However, step duration adaptation is often neglected in control strategies. In this paper, we propose an approach that combines both step location and timing adjustment for generating robust gaits. In this approach, step location and step timing are decided, based on feedback from the current state of the robot. The proposed approach is comprised of two stages. In the first stage, the nominal step location and step duration for the next step or a previewed number of steps are specified. In this stage which is done at the start of each step, the main goal is to specify the best step length and step duration for a desired walking speed. The second stage deals with finding the best landing point and landing time of the swing foot at each control cycle. In this stage, stability of the gaits is preserved by specifying a desired offset between the swing foot landing point and the Divergent Component of Motion (DCM) at the end of current step. After specifying the landing point of the swing foot at a desired time, the swing foot trajectory is regenerated at each control cycle to realize desired landing properties. Simulation on different scenarios shows the robustness of the generated gaits from our proposed approach compared to the case where no timing adjustment is employed.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]

2015


Automatic LQR Tuning Based on Gaussian Process Optimization: Early Experimental Results
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]

2015


PDF DOI Project Page [BibTex]


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Assessing human-human therapy kinematics for retargeting to human-robot therapy

Johnson, M. J., Christopher, S. M., Mohan, M., Mendonca, R.

In Proceedings of the IEEE International Conference on Rehabilitation Robotics (ICORR), Singapore, August 2015 (inproceedings)

Abstract
In this paper, we present experiments examining the accuracy of data collected from a Kinect sensor for capturing close interactive actions of a therapist with a patient during stroke rehabilitation. Our long-term goal is to map human-human interactions such as these patient-therapist ones onto human-robot interactions. In many robot interaction scenarios, the robot does not mimic interaction between two or more humans, which is a major part of stroke therapy. The Kinect works for functional tasks such as a reaching task where the interaction to be retargeted by the robot is minimal to none; though this data is not good for a functional task involving touching another person. We demonstrate that the noisy data from Kinect does not produce a system robust enough to be for remapping to a humanoid robot a therapit's movements when in contact with a person.

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DOI [BibTex]

DOI [BibTex]


Direct Loss Minimization Inverse Optimal Control
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]


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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.

am ics

PDF DOI Project Page [BibTex]

PDF DOI Project Page [BibTex]