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2018


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Non-factorised Variational Inference in Dynamical Systems

Ialongo, A. D., Van Der Wilk, M., Hensman, J., Rasmussen, C. E.

1st Symposion on Advances in Approximate Bayesian Inference, December 2018 (conference)

ei

PDF link (url) [BibTex]

2018


PDF link (url) [BibTex]


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Enhancing the Accuracy and Fairness of Human Decision Making

Valera, I., Singla, A., Gomez Rodriguez, M.

Advances in Neural Information Processing Systems 31, pages: 1774-1783, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 32nd Annual Conference on Neural Information Processing Systems, December 2018 (conference)

ei

arXiv link (url) Project Page [BibTex]

arXiv link (url) Project Page [BibTex]


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]

arXiv PDF DOI Project Page Project Page [BibTex]


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Consolidating the Meta-Learning Zoo: A Unifying Perspective as Posterior Predictive Inference

Gordon*, J., Bronskill*, J., Bauer*, M., Nowozin, S., Turner, R. E.

Workshop on Meta-Learning (MetaLearn 2018) at the 32nd Conference on Neural Information Processing Systems, December 2018, *equal contribution (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Versa: Versatile and Efficient Few-shot Learning

Gordon*, J., Bronskill*, J., Bauer*, M., Nowozin, S., Turner, R. E.

Third Workshop on Bayesian Deep Learning at the 32nd Conference on Neural Information Processing Systems, December 2018, *equal contribution (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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DP-MAC: The Differentially Private Method of Auxiliary Coordinates for Deep Learning

Harder, F., Köhler, J., Welling, M., Park, M.

Workshop on Privacy Preserving Machine Learning at the 32nd Conference on Neural Information Processing Systems, December 2018 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Boosting Black Box Variational Inference

Locatello*, F., Dresdner*, G., R., K., Valera, I., Rätsch, G.

Advances in Neural Information Processing Systems 31, pages: 3405-3415, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 32nd Annual Conference on Neural Information Processing Systems, December 2018, *equal contribution (conference)

ei

arXiv link (url) Project Page [BibTex]

arXiv link (url) Project Page [BibTex]


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Deep Nonlinear Non-Gaussian Filtering for Dynamical Systems

Mehrjou, A., Schölkopf, B.

Workshop: Infer to Control: Probabilistic Reinforcement Learning and Structured Control at the 32nd Conference on Neural Information Processing Systems, December 2018 (conference)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Resampled Priors for Variational Autoencoders

Bauer, M., Mnih, A.

Third Workshop on Bayesian Deep Learning at the 32nd Conference on Neural Information Processing Systems, December 2018 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Learning Invariances using the Marginal Likelihood

van der Wilk, M., Bauer, M., John, S. T., Hensman, J.

Advances in Neural Information Processing Systems 31, pages: 9960-9970, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 32nd Annual Conference on Neural Information Processing Systems, December 2018 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Data-Efficient Hierarchical Reinforcement Learning

Nachum, O., Gu, S., Lee, H., Levine, S.

Advances in Neural Information Processing Systems 31, pages: 3307-3317, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 32nd Annual Conference on Neural Information Processing Systems, December 2018 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Generalisation in humans and deep neural networks

Geirhos, R., Temme, C. R. M., Rauber, J., Schütt, H., Bethge, M., Wichmann, F. A.

Advances in Neural Information Processing Systems 31, pages: 7549-7561, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 32nd Annual Conference on Neural Information Processing Systems, December 2018 (conference)

ei

link (url) [BibTex]

link (url) [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]


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Parallel and functionally segregated processing of task phase and conscious content in the prefrontal cortex

Kapoor, V., Besserve, M., Logothetis, N. K., Panagiotaropoulos, T. I.

Communications Biology, 1(215):1-12, December 2018 (article)

ei

link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [BibTex]


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A Computational Camera with Programmable Optics for Snapshot High Resolution Multispectral Imaging

Chen, J., Hirsch, M., Eberhardt, B., Lensch, H. P. A.

Computer Vision - ACCV 2018 - 14th Asian Conference on Computer Vision, December 2018 (conference) Accepted

ei

[BibTex]

[BibTex]


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Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models

Neitz, A., Parascandolo, G., Bauer, S., Schölkopf, B.

Advances in Neural Information Processing Systems 31, pages: 9838-9848, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 32nd Annual Conference on Neural Information Processing Systems, December 2018 (conference)

ei

arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


Assessing Generative Models via Precision and Recall
Assessing Generative Models via Precision and Recall

Sajjadi, M. S. M., Bachem, O., Lucic, M., Bousquet, O., Gelly, S.

Advances in Neural Information Processing Systems 31, pages: 5234-5243, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 32nd Annual Conference on Neural Information Processing Systems, December 2018 (conference)

ei

arXiv link (url) [BibTex]

arXiv link (url) [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]


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Flex-Convolution (Million-Scale Point-Cloud Learning Beyond Grid-Worlds)

Groh*, F., Wieschollek*, P., Lensch, H. P. A.

Computer Vision - ACCV 2018 - 14th Asian Conference on Computer Vision, December 2018, *equal contribution (conference) Accepted

ei

[BibTex]

[BibTex]


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Bayesian Nonparametric Hawkes Processes

Kapoor, J., Vergari, A., Gomez Rodriguez, M., Valera, I.

Bayesian Nonparametrics workshop at the 32nd Conference on Neural Information Processing Systems, December 2018 (conference)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Informative Features for Model Comparison

Jitkrittum, W., Kanagawa, H., Sangkloy, P., Hays, J., Schölkopf, B., Gretton, A.

Advances in Neural Information Processing Systems 31, pages: 816-827, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 32nd Annual Conference on Neural Information Processing Systems, December 2018 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Hyperbolic Neural Networks

Ganea*, O., Becigneul*, G., Hofmann, T.

Advances in Neural Information Processing Systems 31, pages: 5350-5360, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 32nd Annual Conference on Neural Information Processing Systems, December 2018, *equal contribution (conference)

ei

link (url) [BibTex]

link (url) [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]


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Reducing 3D Vibrations to 1D in Real Time

Park, G., Kuchenbecker, K. J.

Hands-on demonstration (4 pages) presented at AsiaHaptics, Incheon, South Korea, November 2018 (misc)

Abstract
For simple and realistic vibrotactile feedback, 3D accelerations from real contact interactions are usually rendered using a single-axis vibration actuator; this dimensional reduction can be performed in many ways. This demonstration implements a real-time conversion system that simultaneously measures 3D accelerations and renders corresponding 1D vibrations using a two-pen interface. In the demonstration, a user freely interacts with various objects using an In-Pen that contains a 3-axis accelerometer. The captured accelerations are converted to a single-axis signal, and an Out-Pen renders the reduced signal for the user to feel. We prepared seven conversion methods from the simple use of a single-axis signal to applying principal component analysis (PCA) so that users can compare the performance of each conversion method in this demonstration.

hi

Project Page [BibTex]

Project Page [BibTex]


A Large-Scale Fabric-Based Tactile Sensor Using Electrical Resistance Tomography
A Large-Scale Fabric-Based Tactile Sensor Using Electrical Resistance Tomography

Lee, H., Park, K., Kim, J., Kuchenbecker, K. J.

Hands-on demonstration (3 pages) presented at AsiaHaptics, Incheon, South Korea, November 2018 (misc)

Abstract
Large-scale tactile sensing is important for household robots and human-robot interaction because contacts can occur all over a robot’s body surface. This paper presents a new fabric-based tactile sensor that is straightforward to manufacture and can cover a large area. The tactile sensor is made of conductive and non-conductive fabric layers, and the electrodes are stitched with conductive thread, so the resulting device is flexible and stretchable. The sensor utilizes internal array electrodes and a reconstruction method called electrical resistance tomography (ERT) to achieve a high spatial resolution with a small number of electrodes. The developed sensor shows that only 16 electrodes can accurately estimate single and multiple contacts over a square that measures 20 cm by 20 cm.

hi

Project Page [BibTex]

Project Page [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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Regularizing Reinforcement Learning with State Abstraction

Akrour, R., Veiga, F., Peters, J., Neuman, G.

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2018 (conference) Accepted

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Learning to Categorize Bug Reports with LSTM Networks

Gondaliya, K., Peters, J., Rueckert, E.

Proceedings of the 10th International Conference on Advances in System Testing and Validation Lifecycle (VALID), pages: 7-12, October 2018 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Domain Randomization for Simulation-Based Policy Optimization with Transferability Assessment

Muratore, F., Treede, F., Gienger, M., Peters, J.

2nd Annual Conference on Robot Learning (CoRL), 87, pages: 700-713, Proceedings of Machine Learning Research, PMLR, October 2018 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Reinforcement Learning of Phase Oscillators for Fast Adaptation to Moving Targets

Maeda, G., Koc, O., Morimoto, J.

Proceedings of The 2nd Conference on Robot Learning (CoRL), 87, pages: 630-640, (Editors: Aude Billard, Anca Dragan, Jan Peters, Jun Morimoto ), PMLR, October 2018 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


Control of Musculoskeletal Systems using Learned Dynamics Models
Control of Musculoskeletal Systems using Learned Dynamics Models

Büchler, D., Calandra, R., Schölkopf, B., Peters, J.

IEEE Robotics and Automation Letters, Robotics and Automation Letters, 3(4):3161-3168, IEEE, 2018 (article)

Abstract
Controlling musculoskeletal systems, especially robots actuated by pneumatic artificial muscles, is a challenging task due to nonlinearities, hysteresis effects, massive actuator de- lay and unobservable dependencies such as temperature. Despite such difficulties, muscular systems offer many beneficial prop- erties to achieve human-comparable performance in uncertain and fast-changing tasks. For example, muscles are backdrivable and provide variable stiffness while offering high forces to reach high accelerations. In addition, the embodied intelligence deriving from the compliance might reduce the control demands for specific tasks. In this paper, we address the problem of how to accurately control musculoskeletal robots. To address this issue, we propose to learn probabilistic forward dynamics models using Gaussian processes and, subsequently, to employ these models for control. However, Gaussian processes dynamics models cannot be set-up for our musculoskeletal robot as for traditional motor- driven robots because of unclear state composition etc. We hence empirically study and discuss in detail how to tune these approaches to complex musculoskeletal robots and their specific challenges. Moreover, we show that our model can be used to accurately control an antagonistic pair of pneumatic artificial muscles for a trajectory tracking task while considering only one- step-ahead predictions of the forward model and incorporating model uncertainty.

ei

RAL18final link (url) DOI Project Page [BibTex]

RAL18final link (url) DOI Project Page [BibTex]


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Constraint-Space Projection Direct Policy Search

Akrour, R., Peters, J., Neuman, G.

14th European Workshop on Reinforcement Learning (EWRL), October 2018 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


Softness, Warmth, and Responsiveness Improve Robot Hugs
Softness, Warmth, and Responsiveness Improve Robot Hugs

Block, A. E., Kuchenbecker, K. J.

International Journal of Social Robotics, 11(1):49-64, October 2018 (article)

Abstract
Hugs are one of the first forms of contact and affection humans experience. Due to their prevalence and health benefits, roboticists are naturally interested in having robots one day hug humans as seamlessly as humans hug other humans. This project's purpose is to evaluate human responses to different robot physical characteristics and hugging behaviors. Specifically, we aim to test the hypothesis that a soft, warm, touch-sensitive PR2 humanoid robot can provide humans with satisfying hugs by matching both their hugging pressure and their hugging duration. Thirty relatively young and rather technical participants experienced and evaluated twelve hugs with the robot, divided into three randomly ordered trials that focused on physical robot characteristics (single factor, three levels) and nine randomly ordered trials with low, medium, and high hug pressure and duration (two factors, three levels each). Analysis of the results showed that people significantly prefer soft, warm hugs over hard, cold hugs. Furthermore, users prefer hugs that physically squeeze them and release immediately when they are ready for the hug to end. Taking part in the experiment also significantly increased positive user opinions of robots and robot use.

hi

link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [BibTex]


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Multi-objective Optimization of Nonconventional Laminated Composite Panels

Serhat, G.

Koc University, October 2018 (phdthesis)

Abstract
Laminated composite panels are extensively used in various industries due to their high stiffness-to-weight ratio and directional properties that allow optimization of stiffness characteristics for specific applications. With the recent improvements in the manufacturing techniques, the technology trend has been shifting towards the development of nonconventional composites. This work aims to develop new methods for the design and optimization of nonconventional laminated composites. Lamination parameters method is used to characterize laminate stiffness matrices in a compact form. An optimization framework based on finite element analysis was developed to calculate the solutions for different panel geometries, boundary conditions and load cases. The first part of the work addresses the multi-objective optimization of composite laminates to maximize dynamic and load-carrying performances simultaneously. Conforming and conflicting behaviors of multiple objective functions are investigated by determining Pareto-optimal solutions, which provide a valuable insight for multi-objective optimization problems. In the second part, design of curved laminated panels for optimal dynamic response is studied in detail. Firstly, the designs yielding maximum fundamental frequency values are computed. Next, optimal designs minimizing equivalent radiated power are obtained for the panels under harmonic pressure excitation, and their effective frequency bands are shown. The relationship between these two design sets is investigated to study the effectiveness of the frequency maximization technique. In the last part, a new method based on lamination parameters is proposed for the design of variable-stiffness composite panels. The results demonstrate that the proposed method provides manufacturable designs with smooth fiber paths that outperform the constant-stiffness laminates, while utilizing the advantages of lamination parameters formulation.

hi

Multi-objective Optimization of Nonconventional Laminated Composite Panels DOI [BibTex]


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Spatio-temporal Transformer Network for Video Restoration

Kim, T. H., Sajjadi, M. S. M., Hirsch, M., Schölkopf, B.

15th European Conference on Computer Vision (ECCV), Part III, 11207, pages: 111-127, Lecture Notes in Computer Science, (Editors: Vittorio Ferrari, Martial Hebert,Cristian Sminchisescu and Yair Weiss), Springer, September 2018 (conference)

ei

DOI [BibTex]

DOI [BibTex]


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Separating Reflection and Transmission Images in the Wild

Wieschollek, P., Gallo, O., Gu, J., Kautz, J.

European Conference on Computer Vision (ECCV), September 2018 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Risk-Sensitivity in Simulation Based Online Planning

Schmid, K., Belzner, L., Kiermeier, M., Neitz, A., Phan, T., Gabor, T., Linnhoff, C.

KI 2018: Advances in Artificial Intelligence - 41st German Conference on AI, pages: 229-240, (Editors: F. Trollmann and A. Y. Turhan), Springer, Cham, September 2018 (conference)

ei

DOI [BibTex]

DOI [BibTex]


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The Unreasonable Effectiveness of Texture Transfer for Single Image Super-resolution

Gondal, M. W., Schölkopf, B., Hirsch, M.

Workshop and Challenge on Perceptual Image Restoration and Manipulation (PIRM) at the 15th European Conference on Computer Vision (ECCV), September 2018 (conference)

ei

arXiv URL [BibTex]

arXiv URL [BibTex]


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Complexity, Rate, and Scale in Sliding Friction Dynamics Between a Finger and Textured Surface

Khojasteh, B., Janko, M., Visell, Y.

Nature Scientific Reports, 8(13710), September 2018 (article)

Abstract
Sliding friction between the skin and a touched surface is highly complex, but lies at the heart of our ability to discriminate surface texture through touch. Prior research has elucidated neural mechanisms of tactile texture perception, but our understanding of the nonlinear dynamics of frictional sliding between the finger and textured surfaces, with which the neural signals that encode texture originate, is incomplete. To address this, we compared measurements from human fingertips sliding against textured counter surfaces with predictions of numerical simulations of a model finger that resembled a real finger, with similar geometry, tissue heterogeneity, hyperelasticity, and interfacial adhesion. Modeled and measured forces exhibited similar complex, nonlinear sliding friction dynamics, force fluctuations, and prominent regularities related to the surface geometry. We comparatively analysed measured and simulated forces patterns in matched conditions using linear and nonlinear methods, including recurrence analysis. The model had greatest predictive power for faster sliding and for surface textures with length scales greater than about one millimeter. This could be attributed to the the tendency of sliding at slower speeds, or on finer surfaces, to complexly engage fine features of skin or surface, such as fingerprints or surface asperities. The results elucidate the dynamical forces felt during tactile exploration and highlight the challenges involved in the biological perception of surface texture via touch.

hi

DOI [BibTex]

DOI [BibTex]


Statistical Modelling of Fingertip Deformations and Contact Forces during Tactile Interaction
Statistical Modelling of Fingertip Deformations and Contact Forces during Tactile Interaction

Gueorguiev, D., Tzionas, D., Pacchierotti, C., Black, M. J., Kuchenbecker, K. J.

Extended abstract presented at the Hand, Brain and Technology conference (HBT), Ascona, Switzerland, August 2018 (misc)

Abstract
Little is known about the shape and properties of the human finger during haptic interaction, even though these are essential parameters for controlling wearable finger devices and deliver realistic tactile feedback. This study explores a framework for four-dimensional scanning (3D over time) and modelling of finger-surface interactions, aiming to capture the motion and deformations of the entire finger with high resolution while simultaneously recording the interfacial forces at the contact. Preliminary results show that when the fingertip is actively pressing a rigid surface, it undergoes lateral expansion and proximal/distal bending, deformations that cannot be captured by imaging of the contact area alone. Therefore, we are currently capturing a dataset that will enable us to create a statistical model of the finger’s deformations and predict the contact forces induced by tactile interaction with objects. This technique could improve current methods for tactile rendering in wearable haptic devices, which rely on general physical modelling of the skin’s compliance, by developing an accurate model of the variations in finger properties across the human population. The availability of such a model will also enable a more realistic simulation of virtual finger behaviour in virtual reality (VR) environments, as well as the ability to accurately model a specific user’s finger from lower resolution data. It may also be relevant for inferring the physical properties of the underlying tissue from observing the surface mesh deformations, as previously shown for body tissues.

hi

Project Page [BibTex]

Project Page [BibTex]


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Instrumentation, Data, and Algorithms for Visually Understanding Haptic Surface Properties

Burka, A. L.

University of Pennsylvania, Philadelphia, USA, August 2018, Department of Electrical and Systems Engineering (phdthesis)

Abstract
Autonomous robots need to efficiently walk over varied surfaces and grasp diverse objects. We hypothesize that the association between how such surfaces look and how they physically feel during contact can be learned from a database of matched haptic and visual data recorded from various end-effectors' interactions with hundreds of real-world surfaces. Testing this hypothesis required the creation of a new multimodal sensing apparatus, the collection of a large multimodal dataset, and development of a machine-learning pipeline. This thesis begins by describing the design and construction of the Portable Robotic Optical/Tactile ObservatioN PACKage (PROTONPACK, or Proton for short), an untethered handheld sensing device that emulates the capabilities of the human senses of vision and touch. Its sensory modalities include RGBD vision, egomotion, contact force, and contact vibration. Three interchangeable end-effectors (a steel tooling ball, an OptoForce three-axis force sensor, and a SynTouch BioTac artificial fingertip) allow for different material properties at the contact point and provide additional tactile data. We then detail the calibration process for the motion and force sensing systems, as well as several proof-of-concept surface discrimination experiments that demonstrate the reliability of the device and the utility of the data it collects. This thesis then presents a large-scale dataset of multimodal surface interaction recordings, including 357 unique surfaces such as furniture, fabrics, outdoor fixtures, and items from several private and public material sample collections. Each surface was touched with one, two, or three end-effectors, comprising approximately one minute per end-effector of tapping and dragging at various forces and speeds. We hope that the larger community of robotics researchers will find broad applications for the published dataset. Lastly, we demonstrate an algorithm that learns to estimate haptic surface properties given visual input. Surfaces were rated on hardness, roughness, stickiness, and temperature by the human experimenter and by a pool of purely visual observers. Then we trained an algorithm to perform the same task as well as infer quantitative properties calculated from the haptic data. Overall, the task of predicting haptic properties from vision alone proved difficult for both humans and computers, but a hybrid algorithm using a deep neural network and a support vector machine achieved a correlation between expected and actual regression output between approximately ρ = 0.3 and ρ = 0.5 on previously unseen surfaces.

hi

Project Page [BibTex]

Project Page [BibTex]


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From Deterministic ODEs to Dynamic Structural Causal Models

Rubenstein, P. K., Bongers, S., Schölkopf, B., Mooij, J. M.

Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence (UAI), August 2018 (conference)

ei

Arxiv link (url) [BibTex]

Arxiv link (url) [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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Design of curved composite panels for optimal dynamic response using lamination parameters

Serhat, G., Basdogan, I.

Composites Part B: Engineering, 147, pages: 135–146, August 2018 (article)

Abstract
In this paper, dynamic response of composite panels is investigated using lamination parameters as design variables. Finite element analyses are performed to observe the individual and combined effects of different panel aspect ratios, curvatures and boundary conditions on the dynamic responses. Fundamental frequency contours for curved panels are obtained in lamination parameters domain and optimal points yielding maximum values are found. Subsequently, forced dynamic analyses are carried out to calculate equivalent radiated power (ERP) for the panels under harmonic pressure excitation. ERP contours at the maximum fundamental frequency are presented. Optimal lamination parameters providing minimum ERP are determined for different excitation frequencies and their effective frequency bands are shown. The relationship between the designs optimized for maximum fundamental frequency and minimum ERP responses is investigated to study the effectiveness of the frequency maximization technique. The results demonstrate the potential of using lamination parameters technique in the design of curved composite panels for optimal dynamic response and provide valuable insight on the effect of various design parameters.

hi

DOI [BibTex]

DOI [BibTex]


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Generalized Score Functions for Causal Discovery

Huang, B., Zhang, K., Lin, Y., Schölkopf, B., Glymour, C.

Proceedings of the 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pages: 1551-1560, (Editors: Yike Guo and Faisal Farooq), ACM, August 2018 (conference)

ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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A Robust Soft Lens for Tunable Camera Application Using Dielectric Elastomer Actuators

Nam, S., Yun, S., Yoon, J. W., Park, S., Park, S. K., Mun, S., Park, B., Kyung, K.

Soft robotics, Mary Ann Liebert, Inc., August 2018 (article)

Abstract
Developing tunable lenses, an expansion-based mechanism for dynamic focus adjustment can provide a larger focal length tuning range than a contraction-based mechanism. Here, we develop an expansion-tunable soft lens module using a disk-type dielectric elastomer actuator (DEA) that creates axially symmetric pulling forces on a soft lens. Adopted from a biological accommodation mechanism in human eyes, a soft lens at the annular center of a disk-type DEA pair is efficiently stretched to change the focal length in a highly reliable manner. A soft lens with a diameter of 3mm shows a 65.7% change in the focal length (14.3–23.7mm) under a dynamic driving voltage signal control. We confirm a quadratic relation between lens expansion and focal length that leads to large focal length tunability obtainable in the proposed approach. The fabricated tunable lens module can be used for soft, lightweight, and compact vision components in robots, drones, vehicles, and so on.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Yurtsever, A., Fercoq, O., Locatello, F., Cevher, V.

Proceedings of the 35th International Conference on Machine Learning (ICML), 80, pages: 5713-5722, Proceedings of Machine Learning Research, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, July 2018 (conference)

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link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Kilbertus, N., Gascon, A., Kusner, M., Veale, M., Gummadi, K., Weller, A.

Proceedings of the 35th International Conference on Machine Learning (ICML), 80, pages: 2635-2644, Proceedings of Machine Learning Research, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, July 2018 (conference)

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link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Detecting non-causal artifacts in multivariate linear regression models

Janzing, D., Schölkopf, B.

Proceedings of the 35th International Conference on Machine Learning (ICML), 80, pages: 2250-2258, Proceedings of Machine Learning Research, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, July 2018 (conference)

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link (url) [BibTex]

link (url) [BibTex]


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Learning-based solution to phase error correction in T2*-weighted GRE scans

Loktyushin, A., Ehses, P., Schölkopf, B., Scheffler, K.

1st International conference on Medical Imaging with Deep Learning (MIDL), July 2018 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]