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2017


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The Numerics of GANs

Mescheder, L., Nowozin, S., Geiger, A.

In Proceedings from the conference "Neural Information Processing Systems 2017., (Editors: Guyon I. and Luxburg U.v. and Bengio S. and Wallach H. and Fergus R. and Vishwanathan S. and Garnett R.), Curran Associates, Inc., Advances in Neural Information Processing Systems 30 (NIPS), December 2017 (inproceedings)

Abstract
In this paper, we analyze the numerics of common algorithms for training Generative Adversarial Networks (GANs). Using the formalism of smooth two-player games we analyze the associated gradient vector field of GAN training objectives. Our findings suggest that the convergence of current algorithms suffers due to two factors: i) presence of eigenvalues of the Jacobian of the gradient vector field with zero real-part, and ii) eigenvalues with big imaginary part. Using these findings, we design a new algorithm that overcomes some of these limitations and has better convergence properties. Experimentally, we demonstrate its superiority on training common GAN architectures and show convergence on GAN architectures that are known to be notoriously hard to train.

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pdf Project Page [BibTex]

2017


pdf Project Page [BibTex]


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Evaluation of High-Fidelity Simulation as a Training Tool in Transoral Robotic Surgery

Bur, A. M., Gomez, E. D., Newman, J. G., Weinstein, G. S., Bert W. O’Malley, J., Rassekh, C. H., Kuchenbecker, K. J.

Laryngoscope, 127(12):2790-2795, December 2017 (article)

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DOI [BibTex]

DOI [BibTex]


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Synchronicity Trumps Mischief in Rhythmic Human-Robot Social-Physical Interaction

Fitter, N. T., Kuchenbecker, K. J.

In Proceedings of the International Symposium on Robotics Research (ISRR), Puerto Varas, Chile, December 2017 (inproceedings) In press

Abstract
Hand-clapping games and other forms of rhythmic social-physical interaction might help foster human-robot teamwork, but the design of such interactions has scarcely been explored. We leveraged our prior work to enable the Rethink Robotics Baxter Research Robot to competently play one-handed tempo-matching hand-clapping games with a human user. To understand how such a robot’s capabilities and behaviors affect user perception, we created four versions of this interaction: the hand clapping could be initiated by either the robot or the human, and the non-initiating partner could be either cooperative, yielding synchronous motion, or mischievously uncooperative. Twenty adults tested two clapping tempos in each of these four interaction modes in a random order, rating every trial on standardized scales. The study results showed that having the robot initiate the interaction gave it a more dominant perceived personality. Despite previous results on the intrigue of misbehaving robots, we found that moving synchronously with the robot almost always made the interaction more enjoyable, less mentally taxing, less physically demanding, and lower effort for users than asynchronous interactions caused by robot or human mischief. Taken together, our results indicate that cooperative rhythmic social-physical interaction has the potential to strengthen human-robot partnerships.

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[BibTex]

[BibTex]


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Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?

Behl, A., Jafari, O. H., Mustikovela, S. K., Alhaija, H. A., Rother, C., Geiger, A.

In Proceedings IEEE International Conference on Computer Vision (ICCV), IEEE, Piscataway, NJ, USA, IEEE International Conference on Computer Vision (ICCV), October 2017 (inproceedings)

Abstract
Existing methods for 3D scene flow estimation often fail in the presence of large displacement or local ambiguities, e.g., at texture-less or reflective surfaces. However, these challenges are omnipresent in dynamic road scenes, which is the focus of this work. Our main contribution is to overcome these 3D motion estimation problems by exploiting recognition. In particular, we investigate the importance of recognition granularity, from coarse 2D bounding box estimates over 2D instance segmentations to fine-grained 3D object part predictions. We compute these cues using CNNs trained on a newly annotated dataset of stereo images and integrate them into a CRF-based model for robust 3D scene flow estimation - an approach we term Instance Scene Flow. We analyze the importance of each recognition cue in an ablation study and observe that the instance segmentation cue is by far strongest, in our setting. We demonstrate the effectiveness of our method on the challenging KITTI 2015 scene flow benchmark where we achieve state-of-the-art performance at the time of submission.

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pdf suppmat Poster Project Page [BibTex]

pdf suppmat Poster Project Page [BibTex]


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Sparsity Invariant CNNs

Uhrig, J., Schneider, N., Schneider, L., Franke, U., Brox, T., Geiger, A.

International Conference on 3D Vision (3DV) 2017, International Conference on 3D Vision (3DV), October 2017 (conference)

Abstract
In this paper, we consider convolutional neural networks operating on sparse inputs with an application to depth upsampling from sparse laser scan data. First, we show that traditional convolutional networks perform poorly when applied to sparse data even when the location of missing data is provided to the network. To overcome this problem, we propose a simple yet effective sparse convolution layer which explicitly considers the location of missing data during the convolution operation. We demonstrate the benefits of the proposed network architecture in synthetic and real experiments \wrt various baseline approaches. Compared to dense baselines, the proposed sparse convolution network generalizes well to novel datasets and is invariant to the level of sparsity in the data. For our evaluation, we derive a novel dataset from the KITTI benchmark, comprising 93k depth annotated RGB images. Our dataset allows for training and evaluating depth upsampling and depth prediction techniques in challenging real-world settings.

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pdf suppmat Project Page Project Page [BibTex]

pdf suppmat Project Page Project Page [BibTex]


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OctNetFusion: Learning Depth Fusion from Data

Riegler, G., Ulusoy, A. O., Bischof, H., Geiger, A.

International Conference on 3D Vision (3DV) 2017, International Conference on 3D Vision (3DV), October 2017 (conference)

Abstract
In this paper, we present a learning based approach to depth fusion, i.e., dense 3D reconstruction from multiple depth images. The most common approach to depth fusion is based on averaging truncated signed distance functions, which was originally proposed by Curless and Levoy in 1996. While this method is simple and provides great results, it is not able to reconstruct (partially) occluded surfaces and requires a large number frames to filter out sensor noise and outliers. Motivated by the availability of large 3D model repositories and recent advances in deep learning, we present a novel 3D CNN architecture that learns to predict an implicit surface representation from the input depth maps. Our learning based method significantly outperforms the traditional volumetric fusion approach in terms of noise reduction and outlier suppression. By learning the structure of real world 3D objects and scenes, our approach is further able to reconstruct occluded regions and to fill in gaps in the reconstruction. We demonstrate that our learning based approach outperforms both vanilla TSDF fusion as well as TV-L1 fusion on the task of volumetric fusion. Further, we demonstrate state-of-the-art 3D shape completion results.

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pdf Video 1 Video 2 Project Page Project Page [BibTex]

pdf Video 1 Video 2 Project Page Project Page [BibTex]


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Direct Visual Odometry for a Fisheye-Stereo Camera

Liu, P., Heng, L., Sattler, T., Geiger, A., Pollefeys, M.

In Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Piscataway, NJ, USA, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 2017 (inproceedings)

Abstract
We present a direct visual odometry algorithm for a fisheye-stereo camera. Our algorithm performs simultaneous camera motion estimation and semi-dense reconstruction. The pipeline consists of two threads: a tracking thread and a mapping thread. In the tracking thread, we estimate the camera pose via semi-dense direct image alignment. To have a wider field of view (FoV) which is important for robotic perception, we use fisheye images directly without converting them to conventional pinhole images which come with a limited FoV. To address the epipolar curve problem, plane-sweeping stereo is used for stereo matching and depth initialization. Multiple depth hypotheses are tracked for selected pixels to better capture the uncertainty characteristics of stereo matching. Temporal motion stereo is then used to refine the depth and remove false positive depth hypotheses. Our implementation runs at an average of 20 Hz on a low-end PC. We run experiments in outdoor environments to validate our algorithm, and discuss the experimental results. We experimentally show that we are able to estimate 6D poses with low drift, and at the same time, do semi-dense 3D reconstruction with high accuracy.

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pdf Project Page [BibTex]

pdf Project Page [BibTex]


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Using Contact Forces and Robot Arm Accelerations to Automatically Rate Surgeon Skill at Peg Transfer

Brown, J. D., O’Brien, C. E., Leung, S. C., Dumon, K. R., Lee, D. I., Kuchenbecker, K. J.

IEEE Transactions on Biomedical Engineering, 64(9):2263-2275, September 2017 (article)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


Thumb xl hassan paper teasere
Augmented Reality Meets Deep Learning for Car Instance Segmentation in Urban Scenes

Alhaija, H. A., Mustikovela, S. K., Mescheder, L., Geiger, A., Rother, C.

In Proceedings of the British Machine Vision Conference 2017, Proceedings of the British Machine Vision Conference, September 2017 (inproceedings)

Abstract
The success of deep learning in computer vision is based on the availability of large annotated datasets. To lower the need for hand labeled images, virtually rendered 3D worlds have recently gained popularity. Unfortunately, creating realistic 3D content is challenging on its own and requires significant human effort. In this work, we propose an alternative paradigm which combines real and synthetic data for learning semantic instance segmentation models. Exploiting the fact that not all aspects of the scene are equally important for this task, we propose to augment real-world imagery with virtual objects of the target category. Capturing real-world images at large scale is easy and cheap, and directly provides real background appearances without the need for creating complex 3D models of the environment. We present an efficient procedure to augment these images with virtual objects. This allows us to create realistic composite images which exhibit both realistic background appearance as well as a large number of complex object arrangements. In contrast to modeling complete 3D environments, our data augmentation approach requires only a few user interactions in combination with 3D shapes of the target object category. We demonstrate the utility of the proposed approach for training a state-of-the-art high-capacity deep model for semantic instance segmentation. In particular, we consider the task of segmenting car instances on the KITTI dataset which we have annotated with pixel-accurate ground truth. Our experiments demonstrate that models trained on augmented imagery generalize better than those trained on synthetic data or models trained on limited amounts of annotated real data.

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pdf Project Page [BibTex]

pdf Project Page [BibTex]


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Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks

Mescheder, L., Nowozin, S., Geiger, A.

In Proceedings of the 34th International Conference on Machine Learning, 70, Proceedings of Machine Learning Research, (Editors: Doina Precup, Yee Whye Teh), PMLR, International Conference on Machine Learning (ICML), August 2017 (inproceedings)

Abstract
Variational Autoencoders (VAEs) are expressive latent variable models that can be used to learn complex probability distributions from training data. However, the quality of the resulting model crucially relies on the expressiveness of the inference model. We introduce Adversarial Variational Bayes (AVB), a technique for training Variational Autoencoders with arbitrarily expressive inference models. We achieve this by introducing an auxiliary discriminative network that allows to rephrase the maximum-likelihood-problem as a two-player game, hence establishing a principled connection between VAEs and Generative Adversarial Networks (GANs). We show that in the nonparametric limit our method yields an exact maximum-likelihood assignment for the parameters of the generative model, as well as the exact posterior distribution over the latent variables given an observation. Contrary to competing approaches which combine VAEs with GANs, our approach has a clear theoretical justification, retains most advantages of standard Variational Autoencoders and is easy to implement.

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pdf suppmat Project Page arxiv-version Project Page [BibTex]

pdf suppmat Project Page arxiv-version Project Page [BibTex]


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Stiffness Perception during Pinching and Dissection with Teleoperated Haptic Forceps

Ng, C., Zareinia, K., Sun, Q., Kuchenbecker, K. J.

In Proceedings of the International Symposium on Robot and Human Interactive Communication (RO-MAN), pages: 456-463, Lisbon, Portugal, August 2017 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Physical and Behavioral Factors Improve Robot Hug Quality

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

Workshop Paper (2 pages) presented at the RO-MAN Workshop on Social Interaction and Multimodal Expression for Socially Intelligent Robots, Lisbon, Portugal, August 2017 (misc)

Abstract
A hug is one of the most basic ways humans can express affection. As hugs are so common, a natural progression of robot development is to have robots one day hug humans as seamlessly as these intimate human-human interactions occur. 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 warm, soft, touch-sensitive PR2 humanoid robot can provide humans with satisfying hugs by matching both their hugging pressure and their hugging duration. Thirty participants experienced and evaluated twelve hugs with the robot, divided into three randomly ordered trials that focused on physical robot char- acteristics and nine randomly ordered trials with varied hug pressure and duration. We found that people 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.

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Project Page [BibTex]

Project Page [BibTex]


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Ungrounded Haptic Augmented Reality System for Displaying Texture and Friction

Culbertson, H., Kuchenbecker, K. J.

IEEE/ASME Transactions on Mechatronics, 22(4):1839-1849, August 2017 (article)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Slow Flow: Exploiting High-Speed Cameras for Accurate and Diverse Optical Flow Reference Data

Janai, J., Güney, F., Wulff, J., Black, M., Geiger, A.

In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, pages: 1406-1416, IEEE, Piscataway, NJ, USA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (inproceedings)

Abstract
Existing optical flow datasets are limited in size and variability due to the difficulty of capturing dense ground truth. In this paper, we tackle this problem by tracking pixels through densely sampled space-time volumes recorded with a high-speed video camera. Our model exploits the linearity of small motions and reasons about occlusions from multiple frames. Using our technique, we are able to establish accurate reference flow fields outside the laboratory in natural environments. Besides, we show how our predictions can be used to augment the input images with realistic motion blur. We demonstrate the quality of the produced flow fields on synthetic and real-world datasets. Finally, we collect a novel challenging optical flow dataset by applying our technique on data from a high-speed camera and analyze the performance of the state-of-the-art in optical flow under various levels of motion blur.

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pdf suppmat Project page Video DOI Project Page [BibTex]

pdf suppmat Project page Video DOI Project Page [BibTex]


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OctNet: Learning Deep 3D Representations at High Resolutions

Riegler, G., Ulusoy, O., Geiger, A.

In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (inproceedings)

Abstract
We present OctNet, a representation for deep learning with sparse 3D data. In contrast to existing models, our representation enables 3D convolutional networks which are both deep and high resolution. Towards this goal, we exploit the sparsity in the input data to hierarchically partition the space using a set of unbalanced octrees where each leaf node stores a pooled feature representation. This allows to focus memory allocation and computation to the relevant dense regions and enables deeper networks without compromising resolution. We demonstrate the utility of our OctNet representation by analyzing the impact of resolution on several 3D tasks including 3D object classification, orientation estimation and point cloud labeling.

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pdf suppmat Project Page Video Project Page [BibTex]

pdf suppmat Project Page Video Project Page [BibTex]


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A Multi-View Stereo Benchmark with High-Resolution Images and Multi-Camera Videos

Schöps, T., Schönberger, J. L., Galliani, S., Sattler, T., Schindler, K., Pollefeys, M., Geiger, A.

In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (inproceedings)

Abstract
Motivated by the limitations of existing multi-view stereo benchmarks, we present a novel dataset for this task. Towards this goal, we recorded a variety of indoor and outdoor scenes using a high-precision laser scanner and captured both high-resolution DSLR imagery as well as synchronized low-resolution stereo videos with varying fields-of-view. To align the images with the laser scans, we propose a robust technique which minimizes photometric errors conditioned on the geometry. In contrast to previous datasets, our benchmark provides novel challenges and covers a diverse set of viewpoints and scene types, ranging from natural scenes to man-made indoor and outdoor environments. Furthermore, we provide data at significantly higher temporal and spatial resolution. Our benchmark is the first to cover the important use case of hand-held mobile devices while also providing high-resolution DSLR camera images. We make our datasets and an online evaluation server available at http://www.eth3d.net.

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pdf suppmat Project Page Project Page [BibTex]

pdf suppmat Project Page Project Page [BibTex]


Thumb xl camposeco2017cvpr
Toroidal Constraints for Two Point Localization Under High Outlier Ratios

Camposeco, F., Sattler, T., Cohen, A., Geiger, A., Pollefeys, M.

In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (inproceedings)

Abstract
Localizing a query image against a 3D model at large scale is a hard problem, since 2D-3D matches become more and more ambiguous as the model size increases. This creates a need for pose estimation strategies that can handle very low inlier ratios. In this paper, we draw new insights on the geometric information available from the 2D-3D matching process. As modern descriptors are not invariant against large variations in viewpoint, we are able to find the rays in space used to triangulate a given point that are closest to a query descriptor. It is well known that two correspondences constrain the camera to lie on the surface of a torus. Adding the knowledge of direction of triangulation, we are able to approximate the position of the camera from \emphtwo matches alone. We derive a geometric solver that can compute this position in under 1 microsecond. Using this solver, we propose a simple yet powerful outlier filter which scales quadratically in the number of matches. We validate the accuracy of our solver and demonstrate the usefulness of our method in real world settings.

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pdf suppmat Project Page Project Page [BibTex]

pdf suppmat Project Page pdf Project Page [BibTex]


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Semantic Multi-view Stereo: Jointly Estimating Objects and Voxels

Ulusoy, A. O., Black, M. J., Geiger, A.

In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (inproceedings)

Abstract
Dense 3D reconstruction from RGB images is a highly ill-posed problem due to occlusions, textureless or reflective surfaces, as well as other challenges. We propose object-level shape priors to address these ambiguities. Towards this goal, we formulate a probabilistic model that integrates multi-view image evidence with 3D shape information from multiple objects. Inference in this model yields a dense 3D reconstruction of the scene as well as the existence and precise 3D pose of the objects in it. Our approach is able to recover fine details not captured in the input shapes while defaulting to the input models in occluded regions where image evidence is weak. Due to its probabilistic nature, the approach is able to cope with the approximate geometry of the 3D models as well as input shapes that are not present in the scene. We evaluate the approach quantitatively on several challenging indoor and outdoor datasets.

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YouTube pdf suppmat Project Page [BibTex]

YouTube pdf suppmat Project Page [BibTex]


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Design of a Parallel Continuum Manipulator for 6-DOF Fingertip Haptic Display

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

In Proceedings of the IEEE World Haptics Conference (WHC), pages: 599-604, Munich, Germany, June 2017, Finalist for best poster paper (inproceedings)

Abstract
Despite rapid advancements in the field of fingertip haptics, rendering tactile cues with six degrees of freedom (6 DOF) remains an elusive challenge. In this paper, we investigate the potential of displaying fingertip haptic sensations with a 6-DOF parallel continuum manipulator (PCM) that mounts to the user's index finger and moves a contact platform around the fingertip. Compared to traditional mechanisms composed of rigid links and discrete joints, PCMs have the potential to be strong, dexterous, and compact, but they are also more complicated to design. We define the design space of 6-DOF parallel continuum manipulators and outline a process for refining such a device for fingertip haptic applications. Following extensive simulation, we obtain 12 designs that meet our specifications, construct a manually actuated prototype of one such design, and evaluate the simulation's ability to accurately predict the prototype's motion. Finally, we demonstrate the range of deliverable fingertip tactile cues, including a normal force into the finger and shear forces tangent to the finger at three extreme points on the boundary of the fingertip.

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DOI Project Page [BibTex]

DOI Project Page [BibTex]


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High Magnitude Unidirectional Haptic Force Display Using a Motor/Brake Pair and a Cable

Hu, S., Kuchenbecker, K. J.

In Proceedings of the IEEE World Haptics Conference (WHC), pages: 394-399, Munich, Germany, June 2017 (inproceedings)

Abstract
Clever electromechanical design is required to make the force feedback delivered by a kinesthetic haptic interface both strong and safe. This paper explores a onedimensional haptic force display that combines a DC motor and a magnetic particle brake on the same shaft. Rather than a rigid linkage, a spooled cable connects the user to the actuators to enable a large workspace, reduce the moving mass, and eliminate the sticky residual force from the brake. This design combines the high torque/power ratio of the brake and the active output capabilities of the motor to provide a wider range of forces than can be achieved with either actuator alone. A prototype of this device was built, its performance was characterized, and it was used to simulate constant force sources and virtual springs and dampers. Compared to the conventional design of using only a motor, the hybrid device can output higher unidirectional forces at the expense of free space feeling less free.

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DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Physically Interactive Exercise Games with a Baxter Robot

Fitter, N. T., Kuchenbecker, K. J.

Hands-on demonstration presented at the IEEE World Haptics Conference (WHC), Munich, Germany, June 2017 (misc)

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Project Page [BibTex]

Project Page [BibTex]


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Perception of Force and Stiffness in the Presence of Low-Frequency Haptic Noise

Gurari, N., Okamura, A. M., Kuchenbecker, K. J.

PLoS ONE, 12(6):e0178605, June 2017 (article)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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A Wrist-Squeezing Force-Feedback System for Robotic Surgery Training

Brown, J. D., Fernandez, J. N., Cohen, S. P., Kuchenbecker, K. J.

In Proceedings of the IEEE World Haptics Conference (WHC), pages: 107-112, Munich, Germany, June 2017 (inproceedings)

Abstract
Over time, surgical trainees learn to compensate for the lack of haptic feedback in commercial robotic minimally invasive surgical systems. Incorporating touch cues into robotic surgery training could potentially shorten this learning process if the benefits of haptic feedback were sustained after it is removed. In this paper, we develop a wrist-squeezing haptic feedback system and evaluate whether it holds the potential to train novice da Vinci users to reduce the force they exert on a bimanual inanimate training task. Subjects were randomly divided into two groups according to a multiple baseline experimental design. Each of the ten participants moved a ring along a curved wire nine times while the haptic feedback was conditionally withheld, provided, and withheld again. The realtime tactile feedback of applied force magnitude significantly reduced the integral of the force produced by the da Vinci tools on the task materials, and this result remained even when the haptic feedback was removed. Overall, our findings suggest that wrist-squeezing force feedback can play an essential role in helping novice trainees learn to minimize the force they exert with a surgical robot.

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DOI [BibTex]

DOI [BibTex]


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Handling Scan-Time Parameters in Haptic Surface Classification

Burka, A., Kuchenbecker, K. J.

In Proceedings of the IEEE World Haptics Conference (WHC), pages: 424-429, Munich, Germany, June 2017 (inproceedings)

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DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Proton Pack: Visuo-Haptic Surface Data Recording

Burka, A., Kuchenbecker, K. J.

Hands-on demonstration presented at the IEEE World Haptics Conference (WHC), Munich, Germany, June 2017 (misc)

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Project Page [BibTex]

Project Page [BibTex]


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Teaching a Robot to Collaborate with a Human Via Haptic Teleoperation

Hu, S., Kuchenbecker, K. J.

Work-in-progress paper (2 pages) presented at the IEEE World Haptics Conference (WHC), Munich, Germany, June 2017 (misc)

hi

Project Page [BibTex]

Project Page [BibTex]


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How Should Robots Hug?

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

Work-in-progress paper (2 pages) presented at the IEEE World Haptics Conference (WHC), Munich, Germany, June 2017 (misc)

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Project Page [BibTex]

Project Page [BibTex]


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Evaluation of a Vibrotactile Simulator for Dental Caries Detection

Kuchenbecker, K. J., Parajon, R., Maggio, M. P.

Simulation in Healthcare, 12(3):148-156, June 2017 (article)

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DOI [BibTex]

DOI [BibTex]


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Proton 2: Increasing the Sensitivity and Portability of a Visuo-haptic Surface Interaction Recorder

Burka, A., Rajvanshi, A., Allen, S., Kuchenbecker, K. J.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages: 439-445, Singapore, May 2017 (inproceedings)

Abstract
The Portable Robotic Optical/Tactile ObservatioN PACKage (PROTONPACK, or Proton for short) is a new handheld visuo-haptic sensing system that records surface interactions. We previously demonstrated system calibration and a classification task using external motion tracking. This paper details improvements in surface classification performance and removal of the dependence on external motion tracking, necessary before embarking on our goal of gathering a vast surface interaction dataset. Two experiments were performed to refine data collection parameters. After adjusting the placement and filtering of the Proton's high-bandwidth accelerometers, we recorded interactions between two differently-sized steel tooling ball end-effectors (diameter 6.35 and 9.525 mm) and five surfaces. Using features based on normal force, tangential force, end-effector speed, and contact vibration, we trained multi-class SVMs to classify the surfaces using 50 ms chunks of data from each end-effector. Classification accuracies of 84.5% and 91.5% respectively were achieved on unseen test data, an improvement over prior results. In parallel, we pursued on-board motion tracking, using the Proton's camera and fiducial markers. Motion tracks from the external and onboard trackers agree within 2 mm and 0.01 rad RMS, and the accuracy decreases only slightly to 87.7% when using onboard tracking for the 9.525 mm end-effector. These experiments indicate that the Proton 2 is ready for portable data collection.

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DOI Project Page [BibTex]

DOI Project Page [BibTex]


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An Interactive Augmented-Reality Video Training Platform for the da Vinci Surgical System

Carlson, J., Kuchenbecker, K. J.

Workshop paper (3 pages) presented at the ICRA Workshop on C4 Surgical Robots, Singapore, May 2017 (misc)

Abstract
Teleoperated surgical robots such as the Intuitive da Vinci Surgical System facilitate minimally invasive surgeries, which decrease risk to patients. However, these systems can be difficult to learn, and existing training curricula on surgical simulators do not offer students the realistic experience of a full operation. This paper presents an augmented-reality video training platform for the da Vinci that will allow trainees to rehearse any surgery recorded by an expert. While the trainee operates a da Vinci in free space, they see their own instruments overlaid on the expert video. Tools are identified in the source videos via color segmentation and kernelized correlation filter tracking, and their depth is calculated from the da Vinci’s stereoscopic video feed. The user tries to follow the expert’s movements, and if any of their tools venture too far away, the system provides instantaneous visual feedback and pauses to allow the user to correct their motion. The trainee can also rewind the expert video by bringing either da Vinci tool very close to the camera. This combined and augmented video provides the user with an immersive and interactive training experience.

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[BibTex]

[BibTex]


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Hand-Clapping Games with a Baxter Robot

Fitter, N. T., Kuchenbecker, K. J.

Hands-on demonstration presented at ACM/IEEE International Conference on Human-Robot Interaction (HRI), Vienna, Austria, March 2017 (misc)

Abstract
Robots that work alongside humans might be more effective if they could forge a strong social bond with their human partners. Hand-clapping games and other forms of rhythmic social-physical interaction may foster human-robot teamwork, but the design of such interactions has scarcely been explored. At the HRI 2017 conference, we will showcase several such interactions taken from our recent work with the Rethink Robotics Baxter Research Robot, including tempo-matching, Simon says, and Pat-a-cake-like games. We believe conference attendees will be both entertained and intrigued by this novel demonstration of social-physical HRI.

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Project Page [BibTex]

Project Page [BibTex]


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Automatic OSATS Rating of Trainee Skill at a Pediatric Laparoscopic Suturing Task

Oquendo, Y. A., Riddle, E. W., Hiller, D., Blinman, T. A., Kuchenbecker, K. J.

Surgical Endoscopy, 31(Supplement 1):S28, Extended abstract presented as a podium presentation at the Annual Meeting of the Society of American Gastrointestinal and Endoscopic Surgeons (SAGES), Springer, Houston, USA, March 2017 (misc)

Abstract
Introduction: Minimally invasive surgery has revolutionized surgical practice, but challenges remain. Trainees must acquire complex technical skills while minimizing patient risk, and surgeons must maintain their skills for rare procedures. These challenges are magnified in pediatric surgery due to the smaller spaces, finer tissue, and relative dearth of both inanimate and virtual simulators. To build technical expertise, trainees need opportunities for deliberate practice with specific performance feedback, which is typically provided via tedious human grading. This study aimed to validate a novel motion-tracking system and machine learning algorithm for automatically evaluating trainee performance on a pediatric laparoscopic suturing task using a 1–5 OSATS Overall Skill rating. Methods: Subjects (n=14) ranging from medical students to fellows per- formed one or two trials of an intracorporeal suturing task in a custom pediatric laparoscopy training box (Fig. 1) after watching a video of ideal performance by an expert. The position and orientation of the tools and endoscope were recorded over time using Ascension trakSTAR magnetic motion-tracking sensors, and both instrument grasp angles were recorded over time using flex sensors on the handles. The 27 trials were video-recorded and scored on the OSATS scale by a senior fellow; ratings ranged from 1 to 4. The raw motion data from each trial was processed to calculate over 200 preliminary motion parameters. Regularized least-squares regression (LASSO) was used to identify the most predictive parameters for inclusion in a regression tree. Model performance was evaluated by leave-one-subject-out cross validation, wherein the automatic scores given to each subject’s trials (by a model trained on all other data) are compared to the corresponding human rater scores. Results: The best-performing LASSO algorithm identified 14 predictive parameters for inclusion in the regression tree, including completion time, linear path length, angular path length, angular acceleration, grasp velocity, and grasp acceleration. The final model’s raw output showed a strong positive correlation of 0.87 with the reviewer-generated scores, and rounding the output to the nearest integer yielded a leave-one-subject-out cross-validation accuracy of 77.8%. Results are summarized in the confusion matrix (Table 1). Conclusions: Our novel motion-tracking system and regression model automatically gave previously unseen trials overall skill scores that closely match scores from an expert human rater. With additional data and further development, this system may enable creation of a motion-based training platform for pediatric laparoscopic surgery and could yield insights into the fundamental components of surgical skill.

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[BibTex]

[BibTex]


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How Much Haptic Surface Data is Enough?

Burka, A., Kuchenbecker, K. J.

Workshop paper (5 pages) presented at the AAAI Spring Symposium on Interactive Multi-Sensory Object Perception for Embodied Agents, Stanford, USA, March 2017 (misc)

Abstract
The Proton Pack is a portable visuo-haptic surface interaction recording device that will be used to collect a vast multimodal dataset, intended for robots to use as part of an approach to understanding the world around them. In order to collect a useful dataset, we want to pick a suitable interaction duration for each surface, noting the tradeoff between data collection resources and completeness of data. One interesting approach frames the data collection process as an online learning problem, building an incremental surface model and using that model to decide when there is enough data. Here we examine how to do such online surface modeling and when to stop collecting data, using kinetic friction as a first domain in which to apply online modeling.

hi

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Importance of Matching Physical Friction, Hardness, and Texture in Creating Realistic Haptic Virtual Surfaces

Culbertson, H., Kuchenbecker, K. J.

IEEE Transactions on Haptics, 10(1):63-74, January 2017 (article)

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[BibTex]


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Effects of Grip-Force, Contact, and Acceleration Feedback on a Teleoperated Pick-and-Place Task

Khurshid, R. P., Fitter, N. T., Fedalei, E. A., Kuchenbecker, K. J.

IEEE Transactions on Haptics, 10(1):40-53, January 2017 (article)

hi

[BibTex]

[BibTex]


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Functionalised metal-organic frameworks: a novel approach to stabilising single metal atoms

Szilágyi, P. Á., Rogers, D. M., Zaiser, I., Callini, E., Turner, S., Borgschulte, A., Züttel, A., Geerlings, H., Hirscher, M., Dam, B.

{Journal of Materials Chemistry A}, 5(30):15559-15566, Royal Society of Chemistry, Cambridge, UK, 2017 (article)

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DOI [BibTex]

DOI [BibTex]


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Exploiting diffusion barrier and chemical affinity of metal-organic frameworks for efficient hydrogen isotope separation

Kim, J. Y., Balderas-Xicohténcatl, R., Zhang, L., Kang, S. G., Hirscher, M., Oh, H., Moon, H. R.

{Journal of the American Chemical Society}, 139(42):15135-15141, American Chemical Society, Washington, DC, 2017 (article)

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DOI [BibTex]

DOI [BibTex]


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Facile fabrication of mesoporous silica micro-jets with multi-functionalities

Vilela, D., Hortelao, A. C., Balderas-Xicohténcatl, R., Hirscher, M., Hahn, K., Ma, X., Sánchez, S.

{Nanoscale}, 9(37):13990-13997, Royal Society of Chemistry, Cambridge, UK, 2017 (article)

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DOI [BibTex]

DOI [BibTex]


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Selective hydrogen isotope separation via breathing transition in MIL-53(Al)

Kim, J. Y., Zhang, L., Balderas-Xicohténcatl, R., Park, J., Hirscher, M., Moon, H. R., Oh, H.

{Journal of the American Chemical Society}, 139(49):17743-17746, American Chemical Society, Washington, DC, 2017 (article)

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DOI [BibTex]

DOI [BibTex]


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Efficient synthesis for large-scale production and characterization for hydrogen storage of ligand exchanged MOF-74/174/184-M (M\textequalsMg2+, Ni2+)

Oh, H., Maurer, S., Balderas-Xicohténcatl, R., Arnold, L., Magdysyuk, O. V., Schütz, G., Müller, U., Hirscher, M.

{International Journal of Hydrogen Energy}, 42(2):1027-1035, Elsevier, Amsterdam, 2017 (article)

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DOI [BibTex]

DOI [BibTex]


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Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art

Janai, J., Güney, F., Behl, A., Geiger, A.

Arxiv, 2017 (article)

Abstract
Recent years have witnessed amazing progress in AI related fields such as computer vision, machine learning and autonomous vehicles. As with any rapidly growing field, however, it becomes increasingly difficult to stay up-to-date or enter the field as a beginner. While several topic specific survey papers have been written, to date no general survey on problems, datasets and methods in computer vision for autonomous vehicles exists. This paper attempts to narrow this gap by providing a state-of-the-art survey on this topic. Our survey includes both the historically most relevant literature as well as the current state-of-the-art on several specific topics, including recognition, reconstruction, motion estimation, tracking, scene understanding and end-to-end learning. Towards this goal, we first provide a taxonomy to classify each approach and then analyze the performance of the state-of-the-art on several challenging benchmarking datasets including KITTI, ISPRS, MOT and Cityscapes. Besides, we discuss open problems and current research challenges. To ease accessibility and accommodate missing references, we will also provide an interactive platform which allows to navigate topics and methods, and provides additional information and project links for each paper.

avg

pdf Project Page Project Page [BibTex]


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Understanding FORC using synthetic micro-structured systems with variable coupling- and coercivefield distributions

Groß, Felix

Universität Stuttgart, Stuttgart, 2017 (mastersthesis)

mms

[BibTex]


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Corrosion-protected hybrid nanoparticles

Jeong, H., Alarcón-Correa, M., Mark, A. G., Son, K., Lee, T., Fischer, P.

{Advanced Science}, 4(12), Wiley-VCH, Weinheim, 2017 (article)

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DOI [BibTex]

DOI [BibTex]


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Investigation of the Dzyaloshinskii-Moriya interaction and room temperature skyrmions in W/CoFeB/MgO thin films and microwires

Jaiswal, S., Litzius, K., Lemesh, I., Büttner, F., Finizio, S., Raabe, J., Weigand, M., Lee, K., Langer, J., Ocker, B., Jakob, G., Beach, G. S. D., Kläui, M.

{Applied Physics Letters}, 111(2), American Institute of Physics, Melville, NY, 2017 (article)

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DOI [BibTex]

DOI [BibTex]


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Ultrafast demagnetization after femtosecond laser pulses: Transfer of angular momentum from the electronic system to magnetoelastic spin-phonon modes

Fähnle, M., Tsatsoulis, T., Illg, C., Haag, M., Müller, B. Y., Zhang, L.

{Journal of Superconductivity and Novel Magnetism}, 30(5):1381-1387, Springer Science + Business Media B.V., New York, 2017 (article)

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DOI [BibTex]

DOI [BibTex]


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Magnetic behavior of single chain magnets in metal organic frameworks CPO-27-Co

Son, K., Goering, E., Hirscher, M., Oh, H.

{Journal of Nanoscience and Nanotechnology}, 17(10):7541-7546, American Scientific Publishers, Stevenson Ranch, Calif., 2017 (article)

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DOI [BibTex]

DOI [BibTex]


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Switching by domain-wall automotion in asymmetric ferromagnetic rings

Mawass, M., Richter, K., Bisig, A., Reeve, R. M., Krüger, B., Weigand, M., Stoll, H., Krone, A., Kronast, F., Schütz, G., Kläui, M.

{Physical Review Applied}, 7(4), American Physical Society, College Park, Md. [u.a.], 2017 (article)

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DOI [BibTex]

DOI [BibTex]


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A neutral atom moving in an external magnetic field does not feel a Lorentz force

Fähnle, M.

{American Journal of Modern Physics}, 6(6):153-155, Science Publishing Group, New York, NY, 2017 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Adsorption von Wasserstoffmolekülen in nanoporösen Gerüststrukturen

Kotzur, Nadine

Universität Stuttgart, Stuttgart, 2017 (mastersthesis)

mms

[BibTex]

[BibTex]