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2019


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]

2019


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]


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Variational Autoencoders Recover PCA Directions (by Accident)

Rolinek, M., Zietlow, D., Martius, G.

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
The Variational Autoencoder (VAE) is a powerful architecture capable of representation learning and generative modeling. When it comes to learning interpretable (disentangled) representations, VAE and its variants show unparalleled performance. However, the reasons for this are unclear, since a very particular alignment of the latent embedding is needed but the design of the VAE does not encourage it in any explicit way. We address this matter and offer the following explanation: the diagonal approximation in the encoder together with the inherent stochasticity force local orthogonality of the decoder. The local behavior of promoting both reconstruction and orthogonality matches closely how the PCA embedding is chosen. Alongside providing an intuitive understanding, we justify the statement with full theoretical analysis as well as with experiments.

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arXiv link (url) Project Page [BibTex]

arXiv link (url) Project Page [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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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)

pi

[BibTex]

[BibTex]


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Control What You Can: Intrinsically Motivated Task-Planning Agent

Blaes, S., Vlastelica, M., Zhu, J., Martius, G.

In Advances in Neural Information Processing (NeurIPS’19), pages: 12520-12531, Curran Associates, Inc., NeurIPS'19, 2019 (inproceedings)

Abstract
We present a novel intrinsically motivated agent that learns how to control the environment in the fastest possible manner by optimizing learning progress. It learns what can be controlled, how to allocate time and attention, and the relations between objects using surprise based motivation. The effectiveness of our method is demonstrated in a synthetic as well as a robotic manipulation environment yielding considerably improved performance and smaller sample complexity. In a nutshell, our work combines several task-level planning agent structures (backtracking search on task graph, probabilistic road-maps, allocation of search efforts) with intrinsic motivation to achieve learning from scratch.

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link (url) Project Page [BibTex]

link (url) Project Page [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)

pi

[BibTex]

[BibTex]

2018


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

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

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

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arXiv PDF DOI Project Page Project Page [BibTex]

2018


arXiv PDF DOI Project Page Project Page [BibTex]


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Enhanced Non-Steady Gliding Performance of the MultiMo-Bat through Optimal Airfoil Configuration and Control Strategy

Kim, H., Woodward, M. A., Sitti, M.

In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages: 1382-1388, 2018 (inproceedings)

pi

[BibTex]

[BibTex]


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L4: Practical loss-based stepsize adaptation for deep learning

Rolinek, M., Martius, G.

In Advances in Neural Information Processing Systems 31 (NeurIPS 2018), pages: 6434-6444, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 2018 (inproceedings)

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Github link (url) Project Page [BibTex]

Github link (url) Project Page [BibTex]


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Collectives of Spinning Mobile Microrobots for Navigation and Object Manipulation at the Air-Water Interface

Wang, W., Kishore, V., Koens, L., Lauga, E., Sitti, M.

In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages: 1-9, 2018 (inproceedings)

pi

[BibTex]

[BibTex]


Systematic self-exploration of behaviors for robots in a dynamical systems framework
Systematic self-exploration of behaviors for robots in a dynamical systems framework

Pinneri, C., Martius, G.

In Proc. Artificial Life XI, pages: 319-326, MIT Press, Cambridge, MA, 2018 (inproceedings)

Abstract
One of the challenges of this century is to understand the neural mechanisms behind cognitive control and learning. Recent investigations propose biologically plausible synaptic mechanisms for self-organizing controllers, in the spirit of Hebbian learning. In particular, differential extrinsic plasticity (DEP) [Der and Martius, PNAS 2015], has proven to enable embodied agents to self-organize their individual sensorimotor development, and generate highly coordinated behaviors during their interaction with the environment. These behaviors are attractors of a dynamical system. In this paper, we use the DEP rule to generate attractors and we combine it with a “repelling potential” which allows the system to actively explore all its attractor behaviors in a systematic way. With a view to a self-determined exploration of goal-free behaviors, our framework enables switching between different motion patterns in an autonomous and sequential fashion. Our algorithm is able to recover all the attractor behaviors in a toy system and it is also effective in two simulated environments. A spherical robot discovers all its major rolling modes and a hexapod robot learns to locomote in 50 different ways in 30min.

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link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [BibTex]


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Endo-VMFuseNet: A Deep Visual-Magnetic Sensor Fusion Approach for Endoscopic Capsule Robots

Turan, M., Almalioglu, Y., Gilbert, H. B., Sari, A. E., Soylu, U., Sitti, M.

In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages: 1-7, 2018 (inproceedings)

pi

[BibTex]

[BibTex]


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Endosensorfusion: Particle filtering-based multi-sensory data fusion with switching state-space model for endoscopic capsule robots

Turan, M., Almalioglu, Y., Gilbert, H., Araujo, H., Cemgil, T., Sitti, M.

In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages: 1-8, 2018 (inproceedings)

pi

[BibTex]

[BibTex]


Learning equations for extrapolation and control
Learning equations for extrapolation and control

Sahoo, S. S., Lampert, C. H., Martius, G.

In Proc. 35th International Conference on Machine Learning, ICML 2018, Stockholm, Sweden, 2018, 80, pages: 4442-4450, http://proceedings.mlr.press/v80/sahoo18a/sahoo18a.pdf, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, 2018 (inproceedings)

Abstract
We present an approach to identify concise equations from data using a shallow neural network approach. In contrast to ordinary black-box regression, this approach allows understanding functional relations and generalizing them from observed data to unseen parts of the parameter space. We show how to extend the class of learnable equations for a recently proposed equation learning network to include divisions, and we improve the learning and model selection strategy to be useful for challenging real-world data. For systems governed by analytical expressions, our method can in many cases identify the true underlying equation and extrapolate to unseen domains. We demonstrate its effectiveness by experiments on a cart-pendulum system, where only 2 random rollouts are required to learn the forward dynamics and successfully achieve the swing-up task.

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Code Arxiv Poster Slides link (url) Project Page [BibTex]

Code Arxiv Poster Slides link (url) Project Page [BibTex]


Robust Affordable 3D Haptic Sensation via Learning Deformation Patterns
Robust Affordable 3D Haptic Sensation via Learning Deformation Patterns

Sun, H., Martius, G.

Proceedings International Conference on Humanoid Robots, pages: 846-853, IEEE, New York, NY, USA, 2018 IEEE-RAS International Conference on Humanoid Robots, 2018, Oral Presentation (conference)

Abstract
Haptic sensation is an important modality for interacting with the real world. This paper proposes a general framework of inferring haptic forces on the surface of a 3D structure from internal deformations using a small number of physical sensors instead of employing dense sensor arrays. Using machine learning techniques, we optimize the sensor number and their placement and are able to obtain high-precision force inference for a robotic limb using as few as 9 sensors. For the optimal and sparse placement of the measurement units (strain gauges), we employ data-driven methods based on data obtained by finite element simulation. We compare data-driven approaches with model-based methods relying on geometric distance and information criteria such as Entropy and Mutual Information. We validate our approach on a modified limb of the “Poppy” robot [1] and obtain 8 mm localization precision.

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DOI Project Page [BibTex]

DOI Project Page [BibTex]

2014


Geckogripper: A soft, inflatable robotic gripper using gecko-inspired elastomer micro-fiber adhesives
Geckogripper: A soft, inflatable robotic gripper using gecko-inspired elastomer micro-fiber adhesives

Song, S., Majidi, C., Sitti, M.

In Intelligent Robots and Systems (IROS 2014), 2014 IEEE/RSJ International Conference on, pages: 4624-4629, September 2014 (inproceedings)

Abstract
This paper proposes GeckoGripper, a novel soft, inflatable gripper based on the controllable adhesion mechanism of gecko-inspired micro-fiber adhesives, to pick-and-place complex and fragile non-planar or planar parts serially or in parallel. Unlike previous fibrillar structures that use peel angle to control the manipulation of parts, we developed an elastomer micro-fiber adhesive that is fabricated on a soft, flexible membrane, increasing the adaptability to non-planar three-dimensional (3D) geometries and controllability in adhesion. The adhesive switching ratio (the ratio between the maximum and minimum adhesive forces) of the developed gripper was measured to be around 204, which is superior to previous works based on peel angle-based release control methods. Adhesion control mechanism based on the stretch of the membrane and superior adaptability to non-planar 3D geometries enable the micro-fibers to pick-and-place various 3D parts as shown in demonstrations.

pi

DOI [BibTex]

2014


DOI [BibTex]


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Three-dimensional robotic manipulation and transport of micro-scale objects by a magnetically driven capillary micro-gripper

Giltinan, J., Diller, E., Mayda, C., Sitti, M.

In Robotics and Automation (ICRA), 2014 IEEE International Conference on, pages: 2077-2082, 2014 (inproceedings)

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Project Page [BibTex]

Project Page [BibTex]


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Self-Exploration of the Stumpy Robot with Predictive Information Maximization

Martius, G., Jahn, L., Hauser, H., V. Hafner, V.

In Proc. From Animals to Animats, SAB 2014, 8575, pages: 32-42, LNCS, Springer, 2014 (inproceedings)

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[BibTex]

[BibTex]


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Robotic assembly of hydrogels for tissue engineering and regenerative medicine

Tasoglu, S, Diller, E, Guven, S, Sitti, M, Demirci, U

In Journal of Tissue Engineering and Regenerative Medicine, 8, pages: 181-182, 2014 (inproceedings)

pi

Project Page [BibTex]

Project Page [BibTex]


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Versatile non-contact micro-manipulation method using rotational flows locally induced by magnetic microrobots

Ye, Z., Edington, C., Russell, A. J., Sitti, M.

In Advanced Intelligent Mechatronics (AIM), 2014 IEEE/ASME International Conference on, pages: 26-31, 2014 (inproceedings)

pi

Project Page [BibTex]

Project Page [BibTex]


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Structural optimization method towards synthesis of small scale flexure-based mobile grippers

Lum, G. Z., Diller, E., Sitti, M.

In Robotics and Automation (ICRA), 2014 IEEE International Conference on, pages: 2339-2344, 2014 (inproceedings)

pi

[BibTex]

[BibTex]


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Six-Degrees-of-Freedom Remote Actuation of Magnetic Microrobots.

Diller, E. D., Giltinan, J., Lum, G. Z., Ye, Z., Sitti, M.

In Robotics: Science and Systems, 2014 (inproceedings)

pi

[BibTex]

[BibTex]