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2019


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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]

2019


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)

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link (url) [BibTex]

link (url) [BibTex]


A Learnable Safety Measure
A Learnable Safety Measure

Heim, S., Rohr, A. V., Trimpe, S., Badri-Spröwitz, A.

Conference on Robot Learning, November 2019 (conference) Accepted

dlg ics

Arxiv [BibTex]

Arxiv [BibTex]


Trunk Pitch Oscillations for Joint Load Redistribution in Humans and Humanoid Robots
Trunk Pitch Oscillations for Joint Load Redistribution in Humans and Humanoid Robots

Drama, Ö., Badri-Spröwitz, A.

Proceedings International Conference on Humanoid Robots, Humanoids, September 2019 (conference) Accepted

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link (url) [BibTex]

link (url) [BibTex]


Predictive Triggering for Distributed Control of Resource Constrained Multi-agent Systems
Predictive Triggering for Distributed Control of Resource Constrained Multi-agent Systems

Mastrangelo, J. M., Baumann, D., Trimpe, S.

In Proceedings of the 8th IFAC Workshop on Distributed Estimation and Control in Networked Systems, pages: 79-84, 8th IFAC Workshop on Distributed Estimation and Control in Networked Systems (NecSys), September 2019 (inproceedings)

ics

arXiv PDF DOI [BibTex]

arXiv PDF DOI [BibTex]


The positive side of damping
The positive side of damping

Heim, S., Millard, M., Le Mouel, C., Sproewitz, A.

Proceedings of AMAM, The 9th International Symposium on Adaptive Motion of Animals and Machines, August 2019 (conference) Accepted

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[BibTex]

[BibTex]


Event-triggered Pulse Control with Model Learning (if Necessary)
Event-triggered Pulse Control with Model Learning (if Necessary)

Baumann, D., Solowjow, F., Johansson, K. H., Trimpe, S.

In Proceedings of the American Control Conference, pages: 792-797, American Control Conference (ACC), July 2019 (inproceedings)

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arXiv PDF [BibTex]

arXiv PDF [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]


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.

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DOI Project Page [BibTex]

DOI Project Page [BibTex]


Data-driven inference of passivity properties via Gaussian process optimization
Data-driven inference of passivity properties via Gaussian process optimization

Romer, A., Trimpe, S., Allgöwer, F.

In Proceedings of the European Control Conference, European Control Conference (ECC), June 2019 (inproceedings)

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PDF [BibTex]

PDF [BibTex]


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Actively Learning Gaussian Process Dynamics

Buisson-Fenet, M., Solowjow, F., Trimpe, S.

2019 (techreport) Submitted

Abstract
Despite the availability of ever more data enabled through modern sensor and computer technology, it still remains an open problem to learn dynamical systems in a sample-efficient way. We propose active learning strategies that leverage information-theoretical properties arising naturally during Gaussian process regression, while respecting constraints on the sampling process imposed by the system dynamics. Sample points are selected in regions with high uncertainty, leading to exploratory behavior and data-efficient training of the model. All results are verified in an extensive numerical benchmark.

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ArXiv [BibTex]


Trajectory-Based Off-Policy Deep Reinforcement Learning
Trajectory-Based Off-Policy Deep Reinforcement Learning

Doerr, A., Volpp, M., Toussaint, M., Trimpe, S., Daniel, C.

In Proceedings of the International Conference on Machine Learning (ICML), International Conference on Machine Learning (ICML), June 2019 (inproceedings)

Abstract
Policy gradient methods are powerful reinforcement learning algorithms and have been demonstrated to solve many complex tasks. However, these methods are also data-inefficient, afflicted with high variance gradient estimates, and frequently get stuck in local optima. This work addresses these weaknesses by combining recent improvements in the reuse of off-policy data and exploration in parameter space with deterministic behavioral policies. The resulting objective is amenable to standard neural network optimization strategies like stochastic gradient descent or stochastic gradient Hamiltonian Monte Carlo. Incorporation of previous rollouts via importance sampling greatly improves data-efficiency, whilst stochastic optimization schemes facilitate the escape from local optima. We evaluate the proposed approach on a series of continuous control benchmark tasks. The results show that the proposed algorithm is able to successfully and reliably learn solutions using fewer system interactions than standard policy gradient methods.

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arXiv PDF [BibTex]

arXiv PDF [BibTex]


A Magnetically-Actuated Untethered Jellyfish-Inspired Soft Milliswimmer
A Magnetically-Actuated Untethered Jellyfish-Inspired Soft Milliswimmer

(Best Paper Award)

Ziyu Ren, T. W., Hu, W.

RSS 2019: Robotics: Science and Systems Conference, June 2019 (conference)

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[BibTex]

[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)

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link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [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.

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Project Page [BibTex]

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.

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link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [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)

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link (url) [BibTex]

link (url) [BibTex]


Feedback Control Goes Wireless: Guaranteed Stability over Low-power Multi-hop Networks
Feedback Control Goes Wireless: Guaranteed Stability over Low-power Multi-hop Networks

(Best Paper Award)

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

In Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems, pages: 97-108, 10th ACM/IEEE International Conference on Cyber-Physical Systems, April 2019 (inproceedings)

Abstract
Closing feedback loops fast and over long distances is key to emerging applications; for example, robot motion control and swarm coordination require update intervals below 100 ms. Low-power wireless is preferred for its flexibility, low cost, and small form factor, especially if the devices support multi-hop communication. Thus far, however, closed-loop control over multi-hop low-power wireless has only been demonstrated for update intervals on the order of multiple seconds. This paper presents a wireless embedded system that tames imperfections impairing control performance such as jitter or packet loss, and a control design that exploits the essential properties of this system to provably guarantee closed-loop stability for linear dynamic systems. Using experiments on a testbed with multiple cart-pole systems, we are the first to demonstrate the feasibility and to assess the performance of closed-loop control and coordination over multi-hop low-power wireless for update intervals from 20 ms to 50 ms.

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arXiv PDF DOI Project Page [BibTex]

arXiv PDF DOI Project Page [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]


Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization
Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization

de Roos, F., Hennig, P.

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89, pages: 1448-1457, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

Abstract
Pre-conditioning is a well-known concept that can significantly improve the convergence of optimization algorithms. For noise-free problems, where good pre-conditioners are not known a priori, iterative linear algebra methods offer one way to efficiently construct them. For the stochastic optimization problems that dominate contemporary machine learning, however, this approach is not readily available. We propose an iterative algorithm inspired by classic iterative linear solvers that uses a probabilistic model to actively infer a pre-conditioner in situations where Hessian-projections can only be constructed with strong Gaussian noise. The algorithm is empirically demonstrated to efficiently construct effective pre-conditioners for stochastic gradient descent and its variants. Experiments on problems of comparably low dimensionality show improved convergence. In very high-dimensional problems, such as those encountered in deep learning, the pre-conditioner effectively becomes an automatic learning-rate adaptation scheme, which we also empirically show to work well.

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PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Elastic modulus affects adhesive strength of gecko-inspired synthetics in variable temperature and humidity

Mitchell, CT, Drotlef, D, Dayan, CB, Sitti, M, Stark, AY

In INTEGRATIVE AND COMPARATIVE BIOLOGY, pages: E372-E372, OXFORD UNIV PRESS INC JOURNALS DEPT, 2001 EVANS RD, CARY, NC 27513 USA, March 2019 (inproceedings)

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[BibTex]

[BibTex]


Quantifying the Robustness of Natural Dynamics: a Viability Approach
Quantifying the Robustness of Natural Dynamics: a Viability Approach

Heim, S., Sproewitz, A.

Proceedings of Dynamic Walking , Dynamic Walking , 2019 (conference) Accepted

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Submission DW2019 [BibTex]

Submission DW2019 [BibTex]


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Gecko-inspired composite microfibers for reversible adhesion on smooth and rough surfaces

Drotlef, D., Dayan, C., Sitti, M.

In INTEGRATIVE AND COMPARATIVE BIOLOGY, pages: E58-E58, OXFORD UNIV PRESS INC JOURNALS DEPT, 2001 EVANS RD, CARY, NC 27513 USA, 2019 (inproceedings)

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[BibTex]

[BibTex]

2007


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A strategy for vision-based controlled pushing of microparticles

Lynch, N. A., Onal, C., Schuster, E., Sitti, M.

In Robotics and Automation, 2007 IEEE International Conference on, pages: 1413-1418, 2007 (inproceedings)

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[BibTex]

2007


[BibTex]


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Autonomous 2D microparticle manipulation based on visual feedback

Onal, C. D., Sitti, M.

In Advanced intelligent mechatronics, 2007 IEEE/ASME international conference on, pages: 1-6, 2007 (inproceedings)

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[BibTex]

[BibTex]


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STRIDE: A highly maneuverable and non-tethered water strider robot

Song, Y. S., Sitti, M.

In Robotics and Automation, 2007 IEEE International Conference on, pages: 980-984, 2007 (inproceedings)

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[BibTex]

[BibTex]


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Dry spinning polymeric nano/microfiber arrays using glass micropipettes with controlled porosities and fiber diameters

Nain, A. S., Gupta, A., Amon, C., Sitti, M.

In Nanotechnology, 2007. IEEE-NANO 2007. 7th IEEE Conference on, pages: 728-732, 2007 (inproceedings)

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[BibTex]

[BibTex]


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Microrobotically fabricated biological scaffolds for tissue engineering

Nain, A. S., Chung, F., Rule, M., Jadlowiec, J. A., Campbell, P. G., Amon, C., Sitti, M.

In Robotics and Automation, 2007 IEEE International Conference on, pages: 1918-1923, 2007 (inproceedings)

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[BibTex]

[BibTex]


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Bacterial flagella assisted propulsion of patterned latex particles: Effect of particle size

Behkam, B., Sitti, M.

In Nanotechnology, 2007. IEEE-NANO 2007. 7th IEEE Conference on, pages: 723-727, 2007 (inproceedings)

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Project Page [BibTex]

Project Page [BibTex]


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A scaled bilateral control system for experimental 1-D teleoperated nanomanipulation applications

Onal, C. D., Pawashe, C., Sitti, M.

In Intelligent Robots and Systems, 2007. IROS 2007. IEEE/RSJ International Conference on, pages: 483-488, 2007 (inproceedings)

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[BibTex]

[BibTex]

2004


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E. coli inspired propulsion for swimming microrobots

Behkam, B., Sitti, M.

In ASME 2004 International Mechanical Engineering Congress and Exposition, pages: 1037-1041, 2004 (inproceedings)

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Project Page [BibTex]

2004


Project Page [BibTex]


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Dynamic modes of nanoparticle motion during nanoprobe-based manipulation

Tafazzoli, A., Sitti, M.

In Nanotechnology, 2004. 4th IEEE Conference on, pages: 35-37, 2004 (inproceedings)

pi

[BibTex]

[BibTex]


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Modeling and design of biomimetic adhesives inspired by gecko foot-hairs

Shah, G. J., Sitti, M.

In Robotics and Biomimetics, 2004. ROBIO 2004. IEEE International Conference on, pages: 873-878, 2004 (inproceedings)

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Project Page [BibTex]

Project Page [BibTex]


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Augmented reality user interface for nanomanipulation using atomic force microscopes

Vogl, W., Sitti, M., Ehrenstrasser, M., Zäh, M.

In Proc. of Eurohaptics, pages: 413-416, 2004 (inproceedings)

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[BibTex]

[BibTex]


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WaalBots for Space applications

Menon, C., Murphy, M., Angrilli, F., Sitti, M.

In 55th IAC Conference, Vancouver, Canada, 2004 (inproceedings)

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[BibTex]

[BibTex]


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Dynamic behavior and simulation of nanoparticle sliding during nanoprobe-based positioning

Tafazzoli, A., Sitti, M.

In Proc. ASME International Mechanical Engineering Conference, 19, pages: 32, 2004 (inproceedings)

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[BibTex]

[BibTex]


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Three-dimensional nanoscale manipulation and manufacturing using proximal probes: controlled pulling of polymer micro/nanofibers

Nain, A. S., Amon, C., Sitti, M.

In Mechatronics, 2004. ICM’04. Proceedings of the IEEE International Conference on, pages: 224-230, 2004 (inproceedings)

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[BibTex]

[BibTex]


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Micro-and nano-scale robotics

Sitti, M.

In American Control Conference, 2004. Proceedings of the 2004, 1, pages: 1-8, 2004 (inproceedings)

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[BibTex]

[BibTex]


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Gecko inspired surface climbing robots

Menon, C., Murphy, M., Sitti, M.

In Robotics and Biomimetics, 2004. ROBIO 2004. IEEE International Conference on, pages: 431-436, 2004 (inproceedings)

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Project Page [BibTex]

Project Page [BibTex]