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2019


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Selecting causal brain features with a single conditional independence test per feature

Mastakouri, A., Schölkopf, B., Janzing, D.

Advances in Neural Information Processing Systems 32, pages: 12532-12543, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei

link (url) [BibTex]

2019


link (url) [BibTex]


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Practical and Consistent Estimation of f-Divergences

Rubenstein, P. K., Bousquet, O., Djolonga, J., Riquelme, C., Tolstikhin, I.

Advances in Neural Information Processing Systems 32, pages: 4072-4082, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Invert to Learn to Invert

Putzky, P., Welling, M.

Advances in Neural Information Processing Systems 32, pages: 444-454, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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On the Fairness of Disentangled Representations

Locatello, F., Abbati, G., Rainforth, T., Bauer, S., Schölkopf, B., Bachem, O.

Advances in Neural Information Processing Systems 32, pages: 14584-14597, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Limitations of the empirical Fisher approximation for natural gradient descent

Kunstner, F., Hennig, P., Balles, L.

Advances in Neural Information Processing Systems 32, pages: 4158-4169, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

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link (url) [BibTex]

link (url) [BibTex]


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A Model to Search for Synthesizable Molecules

Bradshaw, J., Paige, B., Kusner, M. J., Segler, M., Hernández-Lobato, J. M.

Advances in Neural Information Processing Systems 32, pages: 7935-7947, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Kernel Stein Tests for Multiple Model Comparison

Lim, J. N., Yamada, M., Schölkopf, B., Jitkrittum, W.

Advances in Neural Information Processing Systems 32, pages: 2240-2250, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset

Gondal, M. W., Wuthrich, M., Miladinovic, D., Locatello, F., Breidt, M., Volchkov, V., Akpo, J., Bachem, O., Schölkopf, B., Bauer, S.

Advances in Neural Information Processing Systems 32, pages: 15714-15725, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

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link (url) [BibTex]

link (url) [BibTex]


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Convergence Guarantees for Adaptive Bayesian Quadrature Methods

Kanagawa, M., Hennig, P.

Advances in Neural Information Processing Systems 32, pages: 6234-6245, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei pn

link (url) [BibTex]

link (url) [BibTex]


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Are Disentangled Representations Helpful for Abstract Visual Reasoning?

van Steenkiste, S., Locatello, F., Schmidhuber, J., Bachem, O.

Advances in Neural Information Processing Systems 32, pages: 14222-14235, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Perceiving the arrow of time in autoregressive motion

Meding, K., Janzing, D., Schölkopf, B., Wichmann, F. A.

Advances in Neural Information Processing Systems 32, pages: 2303-2314, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Stochastic Frank-Wolfe for Composite Convex Minimization

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

Advances in Neural Information Processing Systems 32, pages: 14246-14256, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Fisher Efficient Inference of Intractable Models

Liu, S., Kanamori, T., Jitkrittum, W., Chen, Y.

Advances in Neural Information Processing Systems 32, pages: 8790-8800, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei

arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


Learning to Explore in Motion and Interaction Tasks
Learning to Explore in Motion and Interaction Tasks

Bogdanovic, M., Righetti, L.

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, November 2019 (conference)

Abstract
Model free reinforcement learning suffers from the high sampling complexity inherent to robotic manipulation or locomotion tasks. Most successful approaches typically use random sampling strategies which leads to slow policy convergence. In this paper we present a novel approach for efficient exploration that leverages previously learned tasks. We exploit the fact that the same system is used across many tasks and build a generative model for exploration based on data from previously solved tasks to improve learning new tasks. The approach also enables continuous learning of improved exploration strategies as novel tasks are learned. Extensive simulations on a robot manipulator performing a variety of motion and contact interaction tasks demonstrate the capabilities of the approach. In particular, our experiments suggest that the exploration strategy can more than double learning speed, especially when rewards are sparse. Moreover, the algorithm is robust to task variations and parameter tuning, making it beneficial for complex robotic problems.

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arXiv [BibTex]

arXiv [BibTex]


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Neural Signatures of Motor Skill in the Resting Brain

Ozdenizci, O., Meyer, T., Wichmann, F., Peters, J., Schölkopf, B., Cetin, M., Grosse-Wentrup, M.

Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC 2019), October 2019 (conference) Accepted

ei

[BibTex]

[BibTex]


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Robust Humanoid Locomotion Using Trajectory Optimization and Sample-Efficient Learning

Yeganegi, M. H., Khadiv, M., Moosavian, S. A. A., Zhu, J., Prete, A. D., Righetti, L.

Proceedings International Conference on Humanoid Robots, IEEE, 2019 IEEE-RAS International Conference on Humanoid Robots, October 2019 (conference)

Abstract
Trajectory optimization (TO) is one of the most powerful tools for generating feasible motions for humanoid robots. However, including uncertainties and stochasticity in the TO problem to generate robust motions can easily lead to intractable problems. Furthermore, since the models used in TO have always some level of abstraction, it can be hard to find a realistic set of uncertainties in the model space. In this paper we leverage a sample-efficient learning technique (Bayesian optimization) to robustify TO for humanoid locomotion. The main idea is to use data from full-body simulations to make the TO stage robust by tuning the cost weights. To this end, we split the TO problem into two phases. The first phase solves a convex optimization problem for generating center of mass (CoM) trajectories based on simplified linear dynamics. The second stage employs iterative Linear-Quadratic Gaussian (iLQG) as a whole-body controller to generate full body control inputs. Then we use Bayesian optimization to find the cost weights to use in the first stage that yields robust performance in the simulation/experiment, in the presence of different disturbance/uncertainties. The results show that the proposed approach is able to generate robust motions for different sets of disturbances and uncertainties.

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https://arxiv.org/abs/1907.04616 link (url) [BibTex]

https://arxiv.org/abs/1907.04616 link (url) [BibTex]


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Convolutional neural networks: A magic bullet for gravitational-wave detection?

Gebhard, T., Kilbertus, N., Harry, I., Schölkopf, B.

Physical Review D, 100(6):063015, American Physical Society, September 2019 (article)

ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Data scarcity, robustness and extreme multi-label classification

Babbar, R., Schölkopf, B.

Machine Learning, 108(8):1329-1351, September 2019, Special Issue of the ECML PKDD 2019 Journal Track (article)

ei

DOI [BibTex]

DOI [BibTex]


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Beta Power May Mediate the Effect of Gamma-TACS on Motor Performance

Mastakouri, A., Schölkopf, B., Grosse-Wentrup, M.

41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages: 5902-5908, July 2019 (conference)

ei

arXiv PDF link (url) [BibTex]

arXiv PDF link (url) [BibTex]


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Coordinating Users of Shared Facilities via Data-driven Predictive Assistants and Game Theory

Geiger, P., Besserve, M., Winkelmann, J., Proissl, C., Schölkopf, B.

Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), pages: 49, (Editors: Amir Globerson and Ricardo Silva), AUAI Press, July 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


Learning Variable Impedance Control for Contact Sensitive Tasks
Learning Variable Impedance Control for Contact Sensitive Tasks

Bogdanovic, M., Khadiv, M., Righetti, L.

arXiv preprint, arXiv:1907.07500, July 2019 (article)

Abstract
Reinforcement learning algorithms have shown great success in solving different problems ranging from playing video games to robotics. However, they struggle to solve delicate robotic problems, especially those involving contact interactions. Though in principle a policy outputting joint torques should be able to learn these tasks, in practice we see that they have difficulty to robustly solve the problem without any structure in the action space. In this paper, we investigate how the choice of action space can give robust performance in presence of contact uncertainties. We propose to learn a policy that outputs impedance and desired position in joint space as a function of system states without imposing any other structure to the problem. We compare the performance of this approach to torque and position control policies under different contact uncertainties. Extensive simulation results on two different systems, a hopper (floating-base) with intermittent contacts and a manipulator (fixed-base) wiping a table, show that our proposed approach outperforms policies outputting torque or position in terms of both learning rate and robustness to environment uncertainty.

mg

[BibTex]


Objective and Subjective Assessment of Algorithms for Reducing Three-Axis Vibrations to One-Axis Vibrations
Objective and Subjective Assessment of Algorithms for Reducing Three-Axis Vibrations to One-Axis Vibrations

Park, G., Kuchenbecker, K. J.

In Proceedings of the IEEE World Haptics Conference, pages: 467-472, July 2019 (inproceedings)

Abstract
A typical approach to creating realistic vibrotactile feedback is reducing 3D vibrations recorded by an accelerometer to 1D signals that can be played back on a haptic actuator, but some of the information is often lost in this dimensional reduction process. This paper describes seven representative algorithms and proposes four metrics based on the spectral match, the temporal match, and the average value and the variability of them across 3D rotations. These four performance metrics were applied to four texture recordings, and the method utilizing the discrete fourier transform (DFT) was found to be the best regardless of the sensing axis. We also recruited 16 participants to assess the perceptual similarity achieved by each algorithm in real time. We found the four metrics correlated well with the subjectively rated similarities for the six dimensional reduction algorithms, with the exception of taking the 3D vector magnitude, which was perceived to be good despite its low spectral and temporal match metrics.

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DOI [BibTex]

DOI [BibTex]


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The Sensitivity of Counterfactual Fairness to Unmeasured Confounding

Kilbertus, N., Ball, P. J., Kusner, M. J., Weller, A., Silva, R.

Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), pages: 213, (Editors: Amir Globerson and Ricardo Silva), AUAI Press, July 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


Fingertip Interaction Metrics Correlate with Visual and Haptic Perception of Real Surfaces
Fingertip Interaction Metrics Correlate with Visual and Haptic Perception of Real Surfaces

Vardar, Y., Wallraven, C., Kuchenbecker, K. J.

In Proceedings of the IEEE World Haptics Conference (WHC), pages: 395-400, Tokyo, Japan, July 2019 (inproceedings)

Abstract
Both vision and touch contribute to the perception of real surfaces. Although there have been many studies on the individual contributions of each sense, it is still unclear how each modality’s information is processed and integrated. To fill this gap, we investigated the similarity of visual and haptic perceptual spaces, as well as how well they each correlate with fingertip interaction metrics. Twenty participants interacted with ten different surfaces from the Penn Haptic Texture Toolkit by either looking at or touching them and judged their similarity in pairs. By analyzing the resulting similarity ratings using multi-dimensional scaling (MDS), we found that surfaces are similarly organized within the three-dimensional perceptual spaces of both modalities. Also, between-participant correlations were significantly higher in the haptic condition. In a separate experiment, we obtained the contact forces and accelerations acting on one finger interacting with each surface in a controlled way. We analyzed the collected fingertip interaction data in both the time and frequency domains. Our results suggest that the three perceptual dimensions for each modality can be represented by roughness/smoothness, hardness/softness, and friction, and that these dimensions can be estimated by surface vibration power, tap spectral centroid, and kinetic friction coefficient, respectively.

hi

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA

Gresele*, L., Rubenstein*, P. K., Mehrjou, A., Locatello, F., Schölkopf, B.

Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), pages: 53, (Editors: Amir Globerson and Ricardo Silva), AUAI Press, July 2019, *equal contribution (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning

Peharz, R., Vergari, A., Stelzner, K., Molina, A., Shao, X., Trapp, M., Kersting, K., Ghahramani, Z.

Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), pages: 124, (Editors: Amir Globerson and Ricardo Silva), AUAI Press, July 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Kernel Mean Matching for Content Addressability of GANs

Jitkrittum*, W., Sangkloy*, P., Gondal, M. W., Raj, A., Hays, J., Schölkopf, B.

Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 3140-3151, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019, *equal contribution (conference)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

Locatello, F., Bauer, S., Lucic, M., Raetsch, G., Gelly, S., Schölkopf, B., Bachem, O.

Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 4114-4124, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


Local Temporal Bilinear Pooling for Fine-grained Action Parsing
Local Temporal Bilinear Pooling for Fine-grained Action Parsing

Zhang, Y., Tang, S., Muandet, K., Jarvers, C., Neumann, H.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2019, June 2019 (inproceedings)

Abstract
Fine-grained temporal action parsing is important in many applications, such as daily activity understanding, human motion analysis, surgical robotics and others requiring subtle and precise operations in a long-term period. In this paper we propose a novel bilinear pooling operation, which is used in intermediate layers of a temporal convolutional encoder-decoder net. In contrast to other work, our proposed bilinear pooling is learnable and hence can capture more complex local statistics than the conventional counterpart. In addition, we introduce exact lower-dimension representations of our bilinear forms, so that the dimensionality is reduced with neither information loss nor extra computation. We perform intensive experiments to quantitatively analyze our model and show the superior performances to other state-of-the-art work on various datasets.

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Code video demo pdf link (url) [BibTex]

Code video demo pdf link (url) [BibTex]


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Generate Semantically Similar Images with Kernel Mean Matching

Jitkrittum*, W., Sangkloy*, P., Gondal, M. W., Raj, A., Hays, J., Schölkopf, B.

6th Workshop Women in Computer Vision (WiCV) (oral presentation), June 2019, *equal contribution (conference)

ei

[BibTex]

[BibTex]


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Projections for Approximate Policy Iteration Algorithms

Akrour, R., Pajarinen, J., Peters, J., Neumann, G.

Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 181-190, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


Implementation of a 6-{DOF} Parallel Continuum Manipulator for Delivering Fingertip Tactile Cues
Implementation of a 6-DOF Parallel Continuum Manipulator for Delivering Fingertip Tactile Cues

Young, E. M., Kuchenbecker, K. J.

IEEE Transactions on Haptics, 12(3):295-306, June 2019 (article)

Abstract
Existing fingertip haptic devices can deliver different subsets of tactile cues in a compact package, but we have not yet seen a wearable six-degree-of-freedom (6-DOF) display. This paper presents the Fuppeteer (short for Fingertip Puppeteer), a device that is capable of controlling the position and orientation of a flat platform, such that any combination of normal and shear force can be delivered at any location on any human fingertip. We build on our previous work of designing a parallel continuum manipulator for fingertip haptics by presenting a motorized version in which six flexible Nitinol wires are actuated via independent roller mechanisms and proportional-derivative controllers. We evaluate the settling time and end-effector vibrations observed during system responses to step inputs. After creating a six-dimensional lookup table and adjusting simulated inputs using measured Jacobians, we show that the device can make contact with all parts of the fingertip with a mean error of 1.42 mm. Finally, we present results from a human-subject study. A total of 24 users discerned 9 evenly distributed contact locations with an average accuracy of 80.5%. Translational and rotational shear cues were identified reasonably well near the center of the fingertip and more poorly around the edges.

hi

DOI Project Page [BibTex]


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Switching Linear Dynamics for Variational Bayes Filtering

Becker-Ehmck, P., Peters, J., van der Smagt, P.

Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 553-562, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness

Suter, R., Miladinovic, D., Schölkopf, B., Bauer, S.

Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 6056-6065, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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First-Order Adversarial Vulnerability of Neural Networks and Input Dimension

Simon-Gabriel, C., Ollivier, Y., Bottou, L., Schölkopf, B., Lopez-Paz, D.

Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 5809-5817, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models

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

In Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 2931-2940, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (inproceedings)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities

Ganea, O., Gelly, S., Becigneul, G., Severyn, A.

Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 2073-2082, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Meta learning variational inference for prediction

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

7th International Conference on Learning Representations (ICLR), May 2019 (conference)

ei

arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


Haptipedia: Accelerating Haptic Device Discovery to Support Interaction & Engineering Design
Haptipedia: Accelerating Haptic Device Discovery to Support Interaction & Engineering Design

Seifi, H., Fazlollahi, F., Oppermann, M., Sastrillo, J. A., Ip, J., Agrawal, A., Park, G., Kuchenbecker, K. J., MacLean, K. E.

In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI), Glasgow, Scotland, May 2019 (inproceedings)

Abstract
Creating haptic experiences often entails inventing, modifying, or selecting specialized hardware. However, experience designers are rarely engineers, and 30 years of haptic inventions are buried in a fragmented literature that describes devices mechanically rather than by potential purpose. We conceived of Haptipedia to unlock this trove of examples: Haptipedia presents a device corpus for exploration through metadata that matter to both device and experience designers. It is a taxonomy of device attributes that go beyond physical description to capture potential utility, applied to a growing database of 105 grounded force-feedback devices, and accessed through a public visualization that links utility to morphology. Haptipedia's design was driven by both systematic review of the haptic device literature and rich input from diverse haptic designers. We describe Haptipedia's reception (including hopes it will redefine device reporting standards) and our plans for its sustainability through community participation.

hi

Project Page [BibTex]

Project Page [BibTex]


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Internal Array Electrodes Improve the Spatial Resolution of Soft Tactile Sensors Based on Electrical Resistance Tomography

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

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages: 5411-5417, Montreal, Canada, May 2019, Hyosang Lee and Kyungseo Park contributed equally to this publication (inproceedings)

hi

link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [BibTex]


Improving Haptic Adjective Recognition with Unsupervised Feature Learning
Improving Haptic Adjective Recognition with Unsupervised Feature Learning

Richardson, B. A., Kuchenbecker, K. J.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages: 3804-3810, Montreal, Canada, May 2019 (inproceedings)

Abstract
Humans can form an impression of how a new object feels simply by touching its surfaces with the densely innervated skin of the fingertips. Many haptics researchers have recently been working to endow robots with similar levels of haptic intelligence, but these efforts almost always employ hand-crafted features, which are brittle, and concrete tasks, such as object recognition. We applied unsupervised feature learning methods, specifically K-SVD and Spatio-Temporal Hierarchical Matching Pursuit (ST-HMP), to rich multi-modal haptic data from a diverse dataset. We then tested the learned features on 19 more abstract binary classification tasks that center on haptic adjectives such as smooth and squishy. The learned features proved superior to traditional hand-crafted features by a large margin, almost doubling the average F1 score across all adjectives. Additionally, particular exploratory procedures (EPs) and sensor channels were found to support perception of certain haptic adjectives, underlining the need for diverse interactions and multi-modal haptic data.

hi

link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [BibTex]


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Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning

Lutter, M., Ritter, C., Peters, J.

7th International Conference on Learning Representations (ICLR), May 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


Leveraging Contact Forces for Learning to Grasp
Leveraging Contact Forces for Learning to Grasp

Merzic, H., Bogdanovic, M., Kappler, D., Righetti, L., Bohg, J.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2019, IEEE, International Conference on Robotics and Automation, May 2019 (inproceedings)

Abstract
Grasping objects under uncertainty remains an open problem in robotics research. This uncertainty is often due to noisy or partial observations of the object pose or shape. To enable a robot to react appropriately to unforeseen effects, it is crucial that it continuously takes sensor feedback into account. While visual feedback is important for inferring a grasp pose and reaching for an object, contact feedback offers valuable information during manipulation and grasp acquisition. In this paper, we use model-free deep reinforcement learning to synthesize control policies that exploit contact sensing to generate robust grasping under uncertainty. We demonstrate our approach on a multi-fingered hand that exhibits more complex finger coordination than the commonly used two- fingered grippers. We conduct extensive experiments in order to assess the performance of the learned policies, with and without contact sensing. While it is possible to learn grasping policies without contact sensing, our results suggest that contact feedback allows for a significant improvement of grasping robustness under object pose uncertainty and for objects with a complex shape.

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video arXiv [BibTex]

video arXiv [BibTex]


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DeepOBS: A Deep Learning Optimizer Benchmark Suite

Schneider, F., Balles, L., Hennig, P.

7th International Conference on Learning Representations (ICLR), May 2019 (conference)

ei pn

link (url) [BibTex]

link (url) [BibTex]


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Disentangled State Space Models: Unsupervised Learning of Dynamics across Heterogeneous Environments

Miladinović*, D., Gondal*, M. W., Schölkopf, B., Buhmann, J. M., Bauer, S.

Deep Generative Models for Highly Structured Data Workshop at ICLR, May 2019, *equal contribution (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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SOM-VAE: Interpretable Discrete Representation Learning on Time Series

Fortuin, V., Hüser, M., Locatello, F., Strathmann, H., Rätsch, G.

7th International Conference on Learning Representations (ICLR), May 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Resampled Priors for Variational Autoencoders

Bauer, M., Mnih, A.

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89, pages: 66-75, Proceedings of Machine Learning Research, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

ei

arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features
Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features

von Kügelgen, J., Mey, A., Loog, M.

In Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89, pages: 1361-1369, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (inproceedings)

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PDF Poster link (url) [BibTex]

PDF Poster link (url) [BibTex]


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Sobolev Descent

Mroueh, Y., Sercu, T., Raj, A.

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89, pages: 2976-2985, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

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PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Fast and Robust Shortest Paths on Manifolds Learned from Data

Arvanitidis, G., Hauberg, S., Hennig, P., Schober, M.

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89, pages: 1506-1515, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

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PDF link (url) [BibTex]

PDF link (url) [BibTex]