Header logo is


2020


A little damping goes a long way: a simulation study of how damping influences task-level stability in running
A little damping goes a long way: a simulation study of how damping influences task-level stability in running

Heim, S., Millard, M., Mouel, C. L., Badri-Spröwitz, A.

Biology Letters, 16(9), September 2020 (article)

Abstract
It is currently unclear if damping plays a functional role in legged locomotion, and simple models often do not include damping terms. We present a new model with a damping term that is isolated from other parameters: that is, the damping term can be adjusted without retuning other model parameters for nominal motion. We systematically compare how increased damping affects stability in the face of unexpected ground-height perturbations. Unlike most studies, we focus on task-level stability: instead of observing whether trajectories converge towards a nominal limit-cycle, we quantify the ability to avoid falls using a recently developed mathematical measure. This measure allows trajectories to be compared quantitatively instead of only being separated into a binary classification of ‘stable' or ‘unstable'. Our simulation study shows that increased damping contributes significantly to task-level stability; however, this benefit quickly plateaus after only a small amount of damping. These results suggest that the low intrinsic damping values observed experimentally may have stability benefits and are not simply minimized for energetic reasons. All Python code and data needed to generate our results are available open source.

dlg ics

link (url) DOI [BibTex]

2020


link (url) DOI [BibTex]


Event-triggered Learning
Event-triggered Learning

Solowjow, F., Trimpe, S.

Automatica, 117, Elsevier, July 2020 (article)

ics

arXiv PDF DOI Project Page [BibTex]

arXiv PDF DOI Project Page [BibTex]


Data-efficient Auto-tuning with Bayesian Optimization: An Industrial Control Study
Data-efficient Auto-tuning with Bayesian Optimization: An Industrial Control Study

Neumann-Brosig, M., Marco, A., Schwarzmann, D., Trimpe, S.

IEEE Transactions on Control Systems Technology, 28(3):730-740, May 2020 (article)

Abstract
Bayesian optimization is proposed for automatic learning of optimal controller parameters from experimental data. A probabilistic description (a Gaussian process) is used to model the unknown function from controller parameters to a user-defined cost. The probabilistic model is updated with data, which is obtained by testing a set of parameters on the physical system and evaluating the cost. In order to learn fast, the Bayesian optimization algorithm selects the next parameters to evaluate in a systematic way, for example, by maximizing information gain about the optimum. The algorithm thus iteratively finds the globally optimal parameters with only few experiments. Taking throttle valve control as a representative industrial control example, the proposed auto-tuning method is shown to outperform manual calibration: it consistently achieves better performance with a low number of experiments. The proposed auto-tuning framework is flexible and can handle different control structures and objectives.

ics

arXiv (PDF) DOI Project Page [BibTex]

arXiv (PDF) DOI Project Page [BibTex]


no image
Sliding Mode Control with Gaussian Process Regression for Underwater Robots

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

Journal of Intelligent & Robotic Systems, January 2020 (article)

ics

DOI [BibTex]

DOI [BibTex]


Hierarchical Event-triggered Learning for Cyclically Excited Systems with Application to Wireless Sensor Networks
Hierarchical Event-triggered Learning for Cyclically Excited Systems with Application to Wireless Sensor Networks

Beuchert, J., Solowjow, F., Raisch, J., Trimpe, S., Seel, T.

IEEE Control Systems Letters, 4(1):103-108, January 2020 (article)

ics

arXiv PDF DOI Project Page [BibTex]

arXiv PDF DOI Project Page [BibTex]


Control-guided Communication: Efficient Resource Arbitration and Allocation in Multi-hop Wireless Control Systems
Control-guided Communication: Efficient Resource Arbitration and Allocation in Multi-hop Wireless Control Systems

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

IEEE Control Systems Letters, 4(1):127-132, January 2020 (article)

ics

arXiv PDF DOI [BibTex]

arXiv PDF DOI [BibTex]


no image
Semi-Supervised Learning of Multi-Object 3D Scene Representations

Elich, C., Oswald, M. R., Pollefeys, M., Stueckler, J.

CoRR, abs/2010.04030, 2020 (article)

Abstract
Representing scenes at the granularity of objects is a prerequisite for scene understanding and decision making. We propose a novel approach for learning multi-object 3D scene representations from images. A recurrent encoder regresses a latent representation of 3D shapes, poses and texture of each object from an input RGB image. The 3D shapes are represented continuously in function-space as signed distance functions (SDF) which we efficiently pre-train from example shapes in a supervised way. By differentiable rendering we then train our model to decompose scenes self-supervised from RGB-D images. Our approach learns to decompose images into the constituent objects of the scene and to infer their shape, pose and texture from a single view. We evaluate the accuracy of our model in inferring the 3D scene layout and demonstrate its generative capabilities.

ev

link (url) [BibTex]

link (url) [BibTex]


no image
Analytical classical density functionals from an equation learning network

Lin, S., Martius, G., Oettel, M.

The Journal of Chemical Physics, 152(2):021102, 2020, arXiv preprint \url{https://arxiv.org/abs/1910.12752} (article)

al

Preprint_PDF DOI [BibTex]

Preprint_PDF DOI [BibTex]


Wearable and Stretchable Strain Sensors: Materials, Sensing Mechanisms, and Applications
Wearable and Stretchable Strain Sensors: Materials, Sensing Mechanisms, and Applications

Souri, H., Banerjee, H., Jusufi, A., Radacsi, N., Stokes, A. A., Park, I., Sitti, M., Amjadi, M.

Advanced Intelligent Systems, 2020 (article)

bio pi

link (url) DOI [BibTex]

link (url) DOI [BibTex]


Wireless Control for Smart Manufacturing: Recent Approaches and Open Challenges
Wireless Control for Smart Manufacturing: Recent Approaches and Open Challenges

Baumann, D., Mager, F., Wetzker, U., Thiele, L., Zimmerling, M., Trimpe, S.

Proceedings of the IEEE, 2020 (article) Accepted

ics

[BibTex]

[BibTex]


no image
Visual-Inertial Mapping with Non-Linear Factor Recovery

Usenko, V., Demmel, N., Schubert, D., Stückler, J., Cremers, D.

IEEE Robotics and Automation Letters (RA-L), 5, 2020, accepted for presentation at IEEE International Conference on Robotics and Automation (ICRA) 2020, to appear, arXiv:1904.06504 (article)

Abstract
Cameras and inertial measurement units are complementary sensors for ego-motion estimation and environment mapping. Their combination makes visual-inertial odometry (VIO) systems more accurate and robust. For globally consistent mapping, however, combining visual and inertial information is not straightforward. To estimate the motion and geometry with a set of images large baselines are required. Because of that, most systems operate on keyframes that have large time intervals between each other. Inertial data on the other hand quickly degrades with the duration of the intervals and after several seconds of integration, it typically contains only little useful information. In this paper, we propose to extract relevant information for visual-inertial mapping from visual-inertial odometry using non-linear factor recovery. We reconstruct a set of non-linear factors that make an optimal approximation of the information on the trajectory accumulated by VIO. To obtain a globally consistent map we combine these factors with loop-closing constraints using bundle adjustment. The VIO factors make the roll and pitch angles of the global map observable, and improve the robustness and the accuracy of the mapping. In experiments on a public benchmark, we demonstrate superior performance of our method over the state-of-the-art approaches.

ev

[BibTex]

[BibTex]


Spatial Scheduling of Informative Meetings for Multi-Agent Persistent Coverage
Spatial Scheduling of Informative Meetings for Multi-Agent Persistent Coverage

Haksar, R. N., Trimpe, S., Schwager, M.

IEEE Robotics and Automation Letters, 2020 (article) Accepted

ics

DOI [BibTex]

DOI [BibTex]


no image
Fish-like aquatic propulsion studied using a pneumatically-actuated soft-robotic model

Wolf, Z., Jusufi, A., Vogt, D. M., Lauder, G. V.

Bioinspiration & Biomimetics, 15(4):046008, Inst. of Physics, London, 2020 (article)

bio

DOI [BibTex]

DOI [BibTex]


Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control
Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control

Nubert, J., Koehler, J., Berenz, V., Allgower, F., Trimpe, S.

IEEE Robotics and Automation Letters, 2020 (article) Accepted

Abstract
Fast feedback control and safety guarantees are essential in modern robotics. We present an approach that achieves both by combining novel robust model predictive control (MPC) with function approximation via (deep) neural networks (NNs). The result is a new approach for complex tasks with nonlinear, uncertain, and constrained dynamics as are common in robotics. Specifically, we leverage recent results in MPC research to propose a new robust setpoint tracking MPC algorithm, which achieves reliable and safe tracking of a dynamic setpoint while guaranteeing stability and constraint satisfaction. The presented robust MPC scheme constitutes a one-layer approach that unifies the often separated planning and control layers, by directly computing the control command based on a reference and possibly obstacle positions. As a separate contribution, we show how the computation time of the MPC can be drastically reduced by approximating the MPC law with a NN controller. The NN is trained and validated from offline samples of the MPC, yielding statistical guarantees, and used in lieu thereof at run time. Our experiments on a state-of-the-art robot manipulator are the first to show that both the proposed robust and approximate MPC schemes scale to real-world robotic systems.

am ics

arXiv PDF DOI [BibTex]

arXiv PDF DOI [BibTex]


Event-triggered Learning for Linear Quadratic Control
Event-triggered Learning for Linear Quadratic Control

Schlüter, H., Solowjow, F., Trimpe, S.

IEEE Transactions on Automatic Control, 2020 (article) Accepted

ics

arXiv [BibTex]

arXiv [BibTex]

2019


Fast Feedback Control over Multi-hop Wireless Networks with Mode Changes and Stability Guarantees
Fast Feedback Control over Multi-hop Wireless Networks with Mode Changes and Stability Guarantees

Baumann, D., Mager, F., Jacob, R., Thiele, L., Zimmerling, M., Trimpe, S.

ACM Transactions on Cyber-Physical Systems, 4(2):18, November 2019 (article)

ics

arXiv PDF DOI [BibTex]

2019


arXiv PDF DOI [BibTex]


Resource-aware IoT Control: Saving Communication through Predictive Triggering
Resource-aware IoT Control: Saving Communication through Predictive Triggering

Trimpe, S., Baumann, D.

IEEE Internet of Things Journal, 6(3):5013-5028, June 2019 (article)

Abstract
The Internet of Things (IoT) interconnects multiple physical devices in large-scale networks. When the 'things' coordinate decisions and act collectively on shared information, feedback is introduced between them. Multiple feedback loops are thus closed over a shared, general-purpose network. Traditional feedback control is unsuitable for design of IoT control because it relies on high-rate periodic communication and is ignorant of the shared network resource. Therefore, recent event-based estimation methods are applied herein for resource-aware IoT control allowing agents to decide online whether communication with other agents is needed, or not. While this can reduce network traffic significantly, a severe limitation of typical event-based approaches is the need for instantaneous triggering decisions that leave no time to reallocate freed resources (e.g., communication slots), which hence remain unused. To address this problem, novel predictive and self triggering protocols are proposed herein. From a unified Bayesian decision framework, two schemes are developed: self triggers that predict, at the current triggering instant, the next one; and predictive triggers that check at every time step, whether communication will be needed at a given prediction horizon. The suitability of these triggers for feedback control is demonstrated in hardware experiments on a cart-pole, and scalability is discussed with a multi-vehicle simulation.

ics

PDF arXiv DOI [BibTex]

PDF arXiv DOI [BibTex]


Autonomous Identification and Goal-Directed Invocation of Event-Predictive Behavioral Primitives
Autonomous Identification and Goal-Directed Invocation of Event-Predictive Behavioral Primitives

Gumbsch, C., Butz, M. V., Martius, G.

IEEE Transactions on Cognitive and Developmental Systems, 2019 (article)

Abstract
Voluntary behavior of humans appears to be composed of small, elementary building blocks or behavioral primitives. While this modular organization seems crucial for the learning of complex motor skills and the flexible adaption of behavior to new circumstances, the problem of learning meaningful, compositional abstractions from sensorimotor experiences remains an open challenge. Here, we introduce a computational learning architecture, termed surprise-based behavioral modularization into event-predictive structures (SUBMODES), that explores behavior and identifies the underlying behavioral units completely from scratch. The SUBMODES architecture bootstraps sensorimotor exploration using a self-organizing neural controller. While exploring the behavioral capabilities of its own body, the system learns modular structures that predict the sensorimotor dynamics and generate the associated behavior. In line with recent theories of event perception, the system uses unexpected prediction error signals, i.e., surprise, to detect transitions between successive behavioral primitives. We show that, when applied to two robotic systems with completely different body kinematics, the system manages to learn a variety of complex behavioral primitives. Moreover, after initial self-exploration the system can use its learned predictive models progressively more effectively for invoking model predictive planning and goal-directed control in different tasks and environments.

al

arXiv PDF video link (url) DOI Project Page [BibTex]


no image
Even Delta-Matroids and the Complexity of Planar Boolean CSPs

Kazda, A., Kolmogorov, V., Rolinek, M.

ACM Transactions on Algorithms, 15(2, Special Issue on Soda'17 and Regular Papers):Article Number 22, 2019 (article)

al

DOI [BibTex]

DOI [BibTex]


no image
Machine Learning for Haptics: Inferring Multi-Contact Stimulation From Sparse Sensor Configuration

Sun, H., Martius, G.

Frontiers in Neurorobotics, 13, pages: 51, 2019 (article)

Abstract
Robust haptic sensation systems are essential for obtaining dexterous robots. Currently, we have solutions for small surface areas such as fingers, but affordable and robust techniques for covering large areas of an arbitrary 3D surface are still missing. Here, we introduce a general machine learning framework to infer multi-contact haptic forces on a 3D robot’s limb surface from internal deformation measured by only a few physical sensors. The general idea of this framework is to predict first the whole surface deformation pattern from the sparsely placed sensors and then to infer number, locations and force magnitudes of unknown contact points. We show how this can be done even if training data can only be obtained for single-contact points using transfer learning at the example of a modified limb of the Poppy robot. With only 10 strain-gauge sensors we obtain a high accuracy also for multiple-contact points. The method can be applied to arbitrarily shaped surfaces and physical sensor types, as long as training data can be obtained.

al

link (url) DOI [BibTex]


no image
Co-Contraction facilitates Body Stiffness Modulation during Swimming with Sensory Feedback in a Soft Biorobotic Physical Model

Jusufi, A., Vogt, D., Wood, R. J.

Integrative and Comparative Biology, 59(Supplement 1):E116-E116, Society of Integrative and Comparative Biology, McLean, VA, 2019 (article)

bio

DOI [BibTex]

DOI [BibTex]

2017


no image
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.

am ics

arXiv Supplementary material PDF DOI Project Page [BibTex]

2017


arXiv Supplementary material PDF DOI Project Page [BibTex]


no image
Self-Organized Behavior Generation for Musculoskeletal Robots

Der, R., Martius, G.

Frontiers in Neurorobotics, 11, pages: 8, 2017 (article)

al

link (url) DOI [BibTex]

link (url) DOI [BibTex]