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2018


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Risk-Sensitivity in Simulation Based Online Planning

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

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

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DOI [BibTex]

2018


DOI [BibTex]


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Part-Aligned Bilinear Representations for Person Re-identification

Suh, Y., Wang, J., Tang, S., Mei, T., Lee, K. M.

In European Conference on Computer Vision (ECCV), 11218, pages: 418-437, Springer, Cham, September 2018 (inproceedings)

Abstract
Comparing the appearance of corresponding body parts is essential for person re-identification. However, body parts are frequently misaligned be- tween detected boxes, due to the detection errors and the pose/viewpoint changes. In this paper, we propose a network that learns a part-aligned representation for person re-identification. Our model consists of a two-stream network, which gen- erates appearance and body part feature maps respectively, and a bilinear-pooling layer that fuses two feature maps to an image descriptor. We show that it results in a compact descriptor, where the inner product between two image descriptors is equivalent to an aggregation of the local appearance similarities of the cor- responding body parts, and thereby significantly reduces the part misalignment problem. Our approach is advantageous over other pose-guided representations by learning part descriptors optimal for person re-identification. Training the net- work does not require any part annotation on the person re-identification dataset. Instead, we simply initialize the part sub-stream using a pre-trained sub-network of an existing pose estimation network and train the whole network to minimize the re-identification loss. We validate the effectiveness of our approach by demon- strating its superiority over the state-of-the-art methods on the standard bench- mark datasets including Market-1501, CUHK03, CUHK01 and DukeMTMC, and standard video dataset MARS.

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pdf supplementary DOI [BibTex]

pdf supplementary DOI [BibTex]


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Playful: Reactive Programming for Orchestrating Robotic Behavior

Berenz, V., Schaal, S.

IEEE Robotics Automation Magazine, 25(3):49-60, September 2018 (article) In press

Abstract
For many service robots, reactivity to changes in their surroundings is a must. However, developing software suitable for dynamic environments is difficult. Existing robotic middleware allows engineers to design behavior graphs by organizing communication between components. But because these graphs are structurally inflexible, they hardly support the development of complex reactive behavior. To address this limitation, we propose Playful, a software platform that applies reactive programming to the specification of robotic behavior.

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playful website playful_IEEE_RAM link (url) DOI [BibTex]


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Learning Human Optical Flow

Ranjan, A., Romero, J., Black, M. J.

In 29th British Machine Vision Conference, September 2018 (inproceedings)

Abstract
The optical flow of humans is well known to be useful for the analysis of human action. Given this, we devise an optical flow algorithm specifically for human motion and show that it is superior to generic flow methods. Designing a method by hand is impractical, so we develop a new training database of image sequences with ground truth optical flow. For this we use a 3D model of the human body and motion capture data to synthesize realistic flow fields. We then train a convolutional neural network to estimate human flow fields from pairs of images. Since many applications in human motion analysis depend on speed, and we anticipate mobile applications, we base our method on SpyNet with several modifications. We demonstrate that our trained network is more accurate than a wide range of top methods on held-out test data and that it generalizes well to real image sequences. When combined with a person detector/tracker, the approach provides a full solution to the problem of 2D human flow estimation. Both the code and the dataset are available for research.

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video code pdf link (url) [BibTex]

video code pdf link (url) [BibTex]


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Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation

(Best Student Paper Award)

Omran, M., Lassner, C., Pons-Moll, G., Gehler, P. V., Schiele, B.

3DV, September 2018 (conference)

Abstract
Direct prediction of 3D body pose and shape remains a challenge even for highly parameterized deep learning models. Mapping from the 2D image space to the prediction space is difficult: perspective ambiguities make the loss function noisy and training data is scarce. In this paper, we propose a novel approach (Neural Body Fitting (NBF)). It integrates a statistical body model within a CNN, leveraging reliable bottom-up semantic body part segmentation and robust top-down body model constraints. NBF is fully differentiable and can be trained using 2D and 3D annotations. In detailed experiments, we analyze how the components of our model affect performance, especially the use of part segmentations as an explicit intermediate representation, and present a robust, efficiently trainable framework for 3D human pose estimation from 2D images with competitive results on standard benchmarks. Code is available at https://github.com/mohomran/neural_body_fitting

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arXiv code [BibTex]


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ClusterNet: Instance Segmentation in RGB-D Images

Shao, L., Tian, Y., Bohg, J.

arXiv, September 2018, Submitted to ICRA'19 (article) Submitted

Abstract
We propose a method for instance-level segmentation that uses RGB-D data as input and provides detailed information about the location, geometry and number of {\em individual\/} objects in the scene. This level of understanding is fundamental for autonomous robots. It enables safe and robust decision-making under the large uncertainty of the real-world. In our model, we propose to use the first and second order moments of the object occupancy function to represent an object instance. We train an hourglass Deep Neural Network (DNN) where each pixel in the output votes for the 3D position of the corresponding object center and for the object's size and pose. The final instance segmentation is achieved through clustering in the space of moments. The object-centric training loss is defined on the output of the clustering. Our method outperforms the state-of-the-art instance segmentation method on our synthesized dataset. We show that our method generalizes well on real-world data achieving visually better segmentation results.

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link (url) [BibTex]

link (url) [BibTex]


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Discovering and Teaching Optimal Planning Strategies

Lieder, F., Callaway, F., Krueger, P. M., Das, P., Griffiths, T. L., Gul, S.

In The 14th biannual conference of the German Society for Cognitive Science, GK, September 2018 (inproceedings)

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Project Page [BibTex]

Project Page [BibTex]


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Unsupervised Learning of Multi-Frame Optical Flow with Occlusions

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

In European Conference on Computer Vision (ECCV), Lecture Notes in Computer Science, vol 11220, pages: 713-731, Springer, Cham, September 2018 (inproceedings)

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pdf suppmat DOI Project Page [BibTex]

pdf suppmat DOI Project Page [BibTex]


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Leveraging Contact Forces for Learning to Grasp

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

arXiv, September 2018, Submitted to ICRA'19 (article) Submitted

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

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video arXiv [BibTex]

video arXiv [BibTex]


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Discovering Rational Heuristics for Risky Choice

Gul, S., Krueger, P. M., Callaway, F., Griffiths, T. L., Lieder, F.

The 14th biannual conference of the German Society for Cognitive Science, GK, The 14th biannual conference of the German Society for Cognitive Science, GK, September 2018 (conference)

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Project Page [BibTex]

Project Page [BibTex]


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Learning an Infant Body Model from RGB-D Data for Accurate Full Body Motion Analysis

Hesse, N., Pujades, S., Romero, J., Black, M. J., Bodensteiner, C., Arens, M., Hofmann, U. G., Tacke, U., Hadders-Algra, M., Weinberger, R., Muller-Felber, W., Schroeder, A. S.

In Int. Conf. on Medical Image Computing and Computer Assisted Intervention (MICCAI), September 2018 (inproceedings)

Abstract
Infant motion analysis enables early detection of neurodevelopmental disorders like cerebral palsy (CP). Diagnosis, however, is challenging, requiring expert human judgement. An automated solution would be beneficial but requires the accurate capture of 3D full-body movements. To that end, we develop a non-intrusive, low-cost, lightweight acquisition system that captures the shape and motion of infants. Going beyond work on modeling adult body shape, we learn a 3D Skinned Multi-Infant Linear body model (SMIL) from noisy, low-quality, and incomplete RGB-D data. We demonstrate the capture of shape and motion with 37 infants in a clinical environment. Quantitative experiments show that SMIL faithfully represents the data and properly factorizes the shape and pose of the infants. With a case study based on general movement assessment (GMA), we demonstrate that SMIL captures enough information to allow medical assessment. SMIL provides a new tool and a step towards a fully automatic system for GMA.

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pdf Project page video extended arXiv version [BibTex]

pdf Project page video extended arXiv version [BibTex]


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Deep Directional Statistics: Pose Estimation with Uncertainty Quantification

Prokudin, S., Gehler, P., Nowozin, S.

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

Abstract
Modern deep learning systems successfully solve many perception tasks such as object pose estimation when the input image is of high quality. However, in challenging imaging conditions such as on low resolution images or when the image is corrupted by imaging artifacts, current systems degrade considerably in accuracy. While a loss in performance is unavoidable we would like our models to quantify their uncertainty in order to achieve robustness against images of varying quality. Probabilistic deep learning models combine the expressive power of deep learning with uncertainty quantification. In this paper, we propose a novel probabilistic deep learning model for the task of angular regression. Our model uses von Mises distributions to predict a distribution over object pose angle. Whereas a single von Mises distribution is making strong assumptions about the shape of the distribution, we extend the basic model to predict a mixture of von Mises distributions. We show how to learn a mixture model using a finite and infinite number of mixture components. Our model allow for likelihood-based training and efficient inference at test time. We demonstrate on a number of challenging pose estimation datasets that our model produces calibrated probability predictions and competitive or superior point estimates compared to the current state-of-the-art.

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code pdf [BibTex]

code pdf [BibTex]


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SphereNet: Learning Spherical Representations for Detection and Classification in Omnidirectional Images

Coors, B., Condurache, A. P., Geiger, A.

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

Abstract
Omnidirectional cameras offer great benefits over classical cameras wherever a wide field of view is essential, such as in virtual reality applications or in autonomous robots. Unfortunately, standard convolutional neural networks are not well suited for this scenario as the natural projection surface is a sphere which cannot be unwrapped to a plane without introducing significant distortions, particularly in the polar regions. In this work, we present SphereNet, a novel deep learning framework which encodes invariance against such distortions explicitly into convolutional neural networks. Towards this goal, SphereNet adapts the sampling locations of the convolutional filters, effectively reversing distortions, and wraps the filters around the sphere. By building on regular convolutions, SphereNet enables the transfer of existing perspective convolutional neural network models to the omnidirectional case. We demonstrate the effectiveness of our method on the tasks of image classification and object detection, exploiting two newly created semi-synthetic and real-world omnidirectional datasets.

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pdf suppmat Project Page [BibTex]


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Recovering Accurate 3D Human Pose in The Wild Using IMUs and a Moving Camera

Marcard, T. V., Henschel, R., Black, M. J., Rosenhahn, B., Pons-Moll, G.

In European Conference on Computer Vision (ECCV), Lecture Notes in Computer Science, vol 11214, pages: 614-631, Springer, Cham, September 2018 (inproceedings)

Abstract
In this work, we propose a method that combines a single hand-held camera and a set of Inertial Measurement Units (IMUs) attached at the body limbs to estimate accurate 3D poses in the wild. This poses many new challenges: the moving camera, heading drift, cluttered background, occlusions and many people visible in the video. We associate 2D pose detections in each image to the corresponding IMU-equipped persons by solving a novel graph based optimization problem that forces 3D to 2D coherency within a frame and across long range frames. Given associations, we jointly optimize the pose of a statistical body model, the camera pose and heading drift using a continuous optimization framework. We validated our method on the TotalCapture dataset, which provides video and IMU synchronized with ground truth. We obtain an accuracy of 26mm, which makes it accurate enough to serve as a benchmark for image-based 3D pose estimation in the wild. Using our method, we recorded 3D Poses in the Wild (3DPW ), a new dataset consisting of more than 51; 000 frames with accurate 3D pose in challenging sequences, including walking in the city, going up-stairs, having co ffee or taking the bus. We make the reconstructed 3D poses, video, IMU and 3D models available for research purposes at http://virtualhumans.mpi-inf.mpg.de/3DPW.

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pdf SupMat data project DOI [BibTex]

pdf SupMat data project DOI [BibTex]


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Visual Perception and Evaluation of Photo-Realistic Self-Avatars From 3D Body Scans in Males and Females

Thaler, A., Piryankova, I., Stefanucci, J. K., Pujades, S., de la Rosa, S., Streuber, S., Romero, J., Black, M. J., Mohler, B. J.

Frontiers in ICT, 5, pages: 1-14, September 2018 (article)

Abstract
The creation or streaming of photo-realistic self-avatars is important for virtual reality applications that aim for perception and action to replicate real world experience. The appearance and recognition of a digital self-avatar may be especially important for applications related to telepresence, embodied virtual reality, or immersive games. We investigated gender differences in the use of visual cues (shape, texture) of a self-avatar for estimating body weight and evaluating avatar appearance. A full-body scanner was used to capture each participant's body geometry and color information and a set of 3D virtual avatars with realistic weight variations was created based on a statistical body model. Additionally, a second set of avatars was created with an average underlying body shape matched to each participant’s height and weight. In four sets of psychophysical experiments, the influence of visual cues on the accuracy of body weight estimation and the sensitivity to weight changes was assessed by manipulating body shape (own, average) and texture (own photo-realistic, checkerboard). The avatars were presented on a large-screen display, and participants responded to whether the avatar's weight corresponded to their own weight. Participants also adjusted the avatar's weight to their desired weight and evaluated the avatar's appearance with regard to similarity to their own body, uncanniness, and their willingness to accept it as a digital representation of the self. The results of the psychophysical experiments revealed no gender difference in the accuracy of estimating body weight in avatars. However, males accepted a larger weight range of the avatars as corresponding to their own. In terms of the ideal body weight, females but not males desired a thinner body. With regard to the evaluation of avatar appearance, the questionnaire responses suggest that own photo-realistic texture was more important to males for higher similarity ratings, while own body shape seemed to be more important to females. These results argue for gender-specific considerations when creating self-avatars.

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pdf DOI [BibTex]

pdf DOI [BibTex]


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Diffusion Measurements of Swimming Enzymes with Fluorescence Correlation Spectroscopy

Günther, J., Börsch, M., Fischer, P.

Accounts of Chemical Research, 51(9):1911-1920, August 2018 (article)

Abstract
Self-propelled chemical motors are chemically powered micro- or nanosized swimmers. The energy required for these motors’ active motion derives from catalytic chemical reactions and the transformation of a fuel dissolved in the solution. While self-propulsion is now well established for larger particles, it is still unclear if enzymes, nature’s nanometer-sized catalysts, are potentially also self-powered nanomotors. Because of its small size, any increase in an enzyme’s diffusion due to active self-propulsion must be observed on top of the enzyme’s passive Brownian motion, which dominates at this scale. Fluorescence correlation spectroscopy (FCS) is a sensitive method to quantify the diffusion properties of single fluorescently labeled molecules in solution. FCS experiments have shown a general increase in the diffusion constant of a number of enzymes when the enzyme is catalytically active. Diffusion enhancements after addition of the enzyme’s substrate (and sometimes its inhibitor) of up to 80\% have been reported, which is at least 1 order of magnitude higher than what theory would predict. However, many factors contribute to the FCS signal and in particular the shape of the autocorrelation function, which underlies diffusion measurements by fluorescence correlation spectroscopy. These effects need to be considered to establish if and by how much the catalytic activity changes an enzyme’s diffusion.We carefully review phenomena that can play a role in FCS experiments and the determination of enzyme diffusion, including the dissociation of enzyme oligomers upon interaction with the substrate, surface binding of the enzyme to glass during the experiment, conformational changes upon binding, and quenching of the fluorophore. We show that these effects can cause changes in the FCS signal that behave similar to an increase in diffusion. However, in the case of the enzymes F1-ATPase and alkaline phosphatase, we demonstrate that there is no measurable increase in enzyme diffusion. Rather, dissociation and conformational changes account for the changes in the FCS signal in the former and fluorophore quenching in the latter. Within the experimental accuracy of our FCS measurements, we do not observe any change in diffusion due to activity for the enzymes we have investigated.We suggest useful control experiments and additional tests for future FCS experiments that should help establish if the observed diffusion enhancement is real or if it is due to an experimental or data analysis artifact. We show that fluorescence lifetime and mean intensity measurements are essential in order to identify the nature of the observed changes in the autocorrelation function. While it is clear from theory that chemically active enzymes should also act as self-propelled nanomotors, our FCS measurements show that the associated increase in diffusion is much smaller than previously reported. Further experiments are needed to quantify the contribution of the enzymes’ catalytic activity to their self-propulsion. We hope that our findings help to establish a useful protocol for future FCS studies in this field and help establish by how much the diffusion of an enzyme is enhanced through catalytic activity.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Uphill production of dihydrogen by enzymatic oxidation of glucose without an external energy source

Suraniti, E., Merzeau, P., Roche, J., Gounel, S., Mark, A. G., Fischer, P., Mano, N., Kuhn, A.

Nature Communications, 9(1):3229, August 2018 (article)

Abstract
Chemical systems do not allow the coupling of energy from several simple reactions to drive a subsequent reaction, which takes place in the same medium and leads to a product with a higher energy than the one released during the first reaction. Gibbs energy considerations thus are not favorable to drive e.g., water splitting by the direct oxidation of glucose as a model reaction. Here, we show that it is nevertheless possible to carry out such an energetically uphill reaction, if the electrons released in the oxidation reaction are temporarily stored in an electromagnetic system, which is then used to raise the electrons' potential energy so that they can power the electrolysis of water in a second step. We thereby demonstrate the general concept that lower energy delivering chemical reactions can be used to enable the formation of higher energy consuming reaction products in a closed system.

pf

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Chemical micromotors self-assemble and self-propel by spontaneous symmetry breaking

Yu, T., Chuphal, P., Thakur, S., Reigh, S. Y., Singh, D. P., Fischer, P.

Chem. Comm., 54, pages: 11933-11936, August 2018 (article)

Abstract
Self-propelling chemical motors have thus far required the fabrication of Janus particles with an asymmetric catalyst distribution. Here, we demonstrate that simple, isotropic colloids can spontaneously assemble to yield dimer motors that self-propel. In a mixture of isotropic titanium dioxide colloids with photo-chemical catalytic activity and passive silica colloids, light illumination causes diffusiophoretic attractions between the active and passive particles and leads to the formation of dimers. The dimers constitute a symmetry-broken motor, whose dynamics can be fully controlled by the illumination conditions. Computer simulations reproduce the dynamics of the colloids and are in good agreement with experiments. The current work presents a simple route to obtain large numbers of self-propelling chemical motors from a dispersion of spherically symmetric colloids through spontaneous symmetry breaking.

pf

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Statistical Modelling of Fingertip Deformations and Contact Forces during Tactile Interaction

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

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

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

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[BibTex]

[BibTex]


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Learning-Based Robust Model Predictive Control with State-Dependent Uncertainty

Soloperto, R., Müller, M. A., Trimpe, S., Allgöwer, F.

In IFAC Conference on Nonlinear Model Predictive Control, Madison, Wisconsin, USA, 6th IFAC Conference on Nonlinear Model Predictive Control, August 2018 (inproceedings) Accepted

ics

[BibTex]

[BibTex]


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From Deterministic ODEs to Dynamic Structural Causal Models

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

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

ei

Arxiv link (url) [BibTex]

Arxiv link (url) [BibTex]


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

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

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

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arXiv [BibTex]

arXiv [BibTex]


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Decentralized MPC based Obstacle Avoidance for Multi-Robot Target Tracking Scenarios

Tallamraju, R., Rajappa, S., Black, M. J., Karlapalem, K., Ahmad, A.

2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pages: 1-8, IEEE, August 2018 (conference)

Abstract
In this work, we consider the problem of decentralized multi-robot target tracking and obstacle avoidance in dynamic environments. Each robot executes a local motion planning algorithm which is based on model predictive control (MPC). The planner is designed as a quadratic program, subject to constraints on robot dynamics and obstacle avoidance. Repulsive potential field functions are employed to avoid obstacles. The novelty of our approach lies in embedding these non-linear potential field functions as constraints within a convex optimization framework. Our method convexifies nonconvex constraints and dependencies, by replacing them as pre-computed external input forces in robot dynamics. The proposed algorithm additionally incorporates different methods to avoid field local minima problems associated with using potential field functions in planning. The motion planner does not enforce predefined trajectories or any formation geometry on the robots and is a comprehensive solution for cooperative obstacle avoidance in the context of multi-robot target tracking. We perform simulation studies for different scenarios to showcase the convergence and efficacy of the proposed algorithm.

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Published Version link (url) DOI Project Page [BibTex]

Published Version link (url) DOI Project Page [BibTex]


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Generalized Score Functions for Causal Discovery

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

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

ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Gait learning for soft microrobots controlled by light fields

Rohr, A. V., Trimpe, S., Marco, A., Fischer, P., Palagi, S.

In Proceeding of the International Conference on Intelligent Robots and Systems, International Conference on Intelligent Robots and Systems (IROS), July 2018 (inproceedings)

Abstract
Soft microrobots based on photoresponsive materials and controlled by light fields can generate a variety of different gaits. This inherent flexibility can be exploited to maximize their locomotion performance in a given environment and used to adapt them to changing environments. However, because of the lack of accurate locomotion models, and given the intrinsic variability among microrobots, analytical control design is not possible. Common data-driven approaches, on the other hand, require running prohibitive numbers of experiments and lead to very sample-specific results. Here we propose a probabilistic learning approach for light-controlled soft microrobots based on Bayesian Optimization (BO) and Gaussian Processes (GPs). The proposed approach results in a learning scheme that is highly data-efficient, enabling gait optimization with a limited experimental budget, and robust against differences among microrobot samples. These features are obtained by designing the learning scheme through the comparison of different GP priors and BO settings on a semisynthetic data set. The developed learning scheme is validated in microrobot experiments, resulting in a 115% improvement in a microrobot’s locomotion performance with an experimental budget of only 20 tests. These encouraging results lead the way toward self-adaptive microrobotic systems based on lightcontrolled soft microrobots and probabilistic learning control.

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arXiv [BibTex]

arXiv [BibTex]


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A machine from machines

Fischer, P.

Nature Physics, 14, pages: 1072–1073, July 2018 (misc)

Abstract
Building spinning microrotors that self-assemble and synchronize to form a gear sounds like an impossible feat. However, it has now been achieved using only a single type of building block -- a colloid that self-propels.

pf

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Chemotaxis of Active Janus Nanoparticles

Popescu, M. N., Uspal, W. E., Bechinger, C., Fischer, P.

Nano Letters, 18(9):5345–5349, July 2018 (article)

Abstract
While colloids and molecules in solution exhibit passive Brownian motion, particles that are partially covered with a catalyst, which promotes the transformation of a fuel dissolved in the solution, can actively move. These active Janus particles are known as “chemical nanomotors” or self-propelling “swimmers” and have been realized with a range of catalysts, sizes, and particle geometries. Because their active translation depends on the fuel concentration, one expects that active colloidal particles should also be able to swim toward a fuel source. Synthesizing and engineering nanoparticles with distinct chemotactic properties may enable important developments, such as particles that can autonomously swim along a pH gradient toward a tumor. Chemotaxis requires that the particles possess an active coupling of their orientation to a chemical gradient. In this Perspective we provide a simple, intuitive description of the underlying mechanisms for chemotaxis, as well as the means to analyze and classify active particles that can show positive or negative chemotaxis. The classification provides guidance for engineering a specific response and is a useful organizing framework for the quantitative analysis and modeling of chemotactic behaviors. Chemotaxis is emerging as an important focus area in the field of active colloids and promises a number of fascinating applications for nanoparticles and particle-based delivery.

pf

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Kernel Recursive ABC: Point Estimation with Intractable Likelihood

Kajihara, T., Kanagawa, M., Yamazaki, K., Fukumizu, K.

Proceedings of the 35th International Conference on Machine Learning, pages: 2405-2414, PMLR, July 2018 (conference)

Abstract
We propose a novel approach to parameter estimation for simulator-based statistical models with intractable likelihood. Our proposed method involves recursive application of kernel ABC and kernel herding to the same observed data. We provide a theoretical explanation regarding why the approach works, showing (for the population setting) that, under a certain assumption, point estimates obtained with this method converge to the true parameter, as recursion proceeds. We have conducted a variety of numerical experiments, including parameter estimation for a real-world pedestrian flow simulator, and show that in most cases our method outperforms existing approaches.

pn

Paper [BibTex]

Paper [BibTex]


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Programmable collective behavior in dynamically self-assembled mobile microrobotic swarms

B Yigit, , Y Alapan, , Sitti, M.

arXiv, July 2018 (article)

Abstract
Collective control of mobile microrobotic swarms is indispensable for their potential high-impact applications in targeted drug delivery, medical diagnostics, parallel micromanipulation, and environmental sensing and remediation. Lack of on-board computational and sensing capabilities in current microrobotic systems necessitates use of physical interactions among individual microrobots for local physical communication and cooperation. Here, we show that mobile microrobotic swarms with well-defined collective behavior can be designed by engineering magnetic interactions among individual units. Microrobots, consisting of a linear chain of self-assembled magnetic microparticles, locomote on surfaces in response to a precessing magnetic field. Control over the direction of precessing magnetic field allows engineering attractive and repulsive interactions among microrobots and, thus, collective order with well-defined spatial organization and parallel operation over macroscale distances (~ 1 cm). These microrobotic swarms can be guided through confined spaces, while preserving microrobot morphology and function. These swarms can further achieve directional transport of large cargoes on surfaces and small cargoes in bulk fluids. Described design approach, exploiting physical interactions among individual robots, enables facile and rapid formation of self-organized and reconfigurable microrobotic swarms with programmable collective order.

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link (url) [BibTex]


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3D-Printed Biodegradable Microswimmer for Drug Delivery and Targeted Cell Labeling

Hakan Ceylan, , I. Ceren Yasa, , Oncay Yasa, , Ahmet Fatih Tabak, , Joshua Giltinan, , Sitti, M.

bioRxiv, pages: 379024, July 2018 (article)

Abstract
Miniaturization of interventional medical devices can leverage minimally invasive technologies by enabling operational resolution at cellular length scales with high precision and repeatability. Untethered micron-scale mobile robots can realize this by navigating and performing in hard-to-reach, confined and delicate inner body sites. However, such a complex task requires an integrated design and engineering strategy, where powering, control, environmental sensing, medical functionality and biodegradability need to be considered altogether. The present study reports a hydrogel-based, biodegradable microrobotic swimmer, which is responsive to the changes in its microenvironment for theranostic cargo delivery and release tasks. We design a double-helical magnetic microswimmer of 20 micrometers length, which is 3D-printed with complex geometrical and compositional features. At normal physiological concentrations, matrix metalloproteinase-2 (MMP-2) enzyme can entirely degrade the microswimmer body in 118 h to solubilized non-toxic products. The microswimmer can respond to the pathological concentrations of MMP-2 by swelling and thereby accelerating the release kinetics of the drug payload. Anti-ErbB 2 antibody-tagged magnetic nanoparticles released from the degraded microswimmers serve for targeted labeling of SKBR3 breast cancer cells to realize the potential of medical imaging of local tissue sites following the therapeutic intervention. These results represent a leap forward toward clinical medical microrobots that are capable of sensing, responding to the local pathological information, and performing specific therapeutic and diagnostic tasks as orderly executed operations using their smart composite material architectures.

pi

DOI [BibTex]


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A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming

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

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

ei

link (url) [BibTex]

link (url) [BibTex]


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

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

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

ei

link (url) [BibTex]

link (url) [BibTex]


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Intrinsic disentanglement: an invariance view for deep generative models

Besserve, M., Sun, R., Schölkopf, B.

Workshop on Theoretical Foundations and Applications of Deep Generative Models at ICML, July 2018 (conference)

ei

PDF [BibTex]

PDF [BibTex]


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Robust Visual Augmented Reality in Robot-Assisted Surgery

Forte, M. P.

Politecnico di Milano, July 2018 (mastersthesis)

Abstract
The broader research objective of this line of research is to test the hypothesis that real-time stereo video analysis and augmented reality can increase safety and task efficiency in robot-assisted surgery. This master’s thesis aims to solve the first step needed to achieve this goal: the creation of a robust system that delivers the envisioned feedback to a surgeon while he or she controls a surgical robot that is identical to those used on human patients. Several approaches for applying augmented reality to da Vinci Surgical Systems have been proposed, but none of them entirely rely on a clinical robot; specifically, they require additional sensors, depend on access to the da Vinci API, are designed for a very specific task, or were tested on systems that are starkly different from those in clinical use. There has also been prior work that presents the real-world camera view and the computer graphics on separate screens, or not in real time. In other scenarios, the digital information is overlaid manually by the surgeons themselves or by computer scientists, rather than being generated automatically in response to the surgeon’s actions. We attempted to overcome the aforementioned constraints by acquiring input signals from the da Vinci stereo endoscope and providing augmented reality to the console in real time (less than 150 ms delay, including the 62 ms of inherent latency of the da Vinci). The potential benefits of the resulting system are broad because it was built to be general, rather than customized for any specific task. The entire platform is compatible with any generation of the da Vinci System and does not require a dVRK (da Vinci Research Kit) or access to the API. Thus, it can be applied to existing da Vinci Systems in operating rooms around the world.

hi

[BibTex]

[BibTex]


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Blind Justice: Fairness with Encrypted Sensitive Attributes

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

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

ei

link (url) [BibTex]

link (url) [BibTex]


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Detecting non-causal artifacts in multivariate linear regression models

Janzing, D., Schölkopf, B.

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

ei

link (url) [BibTex]

link (url) [BibTex]


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The Mirage of Action-Dependent Baselines in Reinforcement Learning

Tucker, G., Bhupatiraju, S., Gu, S., Turner, R., Ghahramani, Z., Levine, S.

Proceedings of the 35th International Conference on Machine Learning (ICML), 80, pages: 5022-5031, Proceedings of Machine Learning Research, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, July 2018 (conference)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Assessing Generative Models via Precision and Recall

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

Workshop on Theoretical Foundations and Applications of Deep Generative Models (TADGM) at the 35th International Conference on Machine Learning (ICML), July 2018 (conference)

ei

arXiv [BibTex]

arXiv [BibTex]


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Learning an Approximate Model Predictive Controller with Guarantees

Hertneck, M., Koehler, J., Trimpe, S., Allgöwer, F.

IEEE Control Systems Letters, 2(3):543-548, July 2018 (article)

Abstract
A supervised learning framework is proposed to approximate a model predictive controller (MPC) with reduced computational complexity and guarantees on stability and constraint satisfaction. The framework can be used for a wide class of nonlinear systems. Any standard supervised learning technique (e.g. neural networks) can be employed to approximate the MPC from samples. In order to obtain closed-loop guarantees for the learned MPC, a robust MPC design is combined with statistical learning bounds. The MPC design ensures robustness to inaccurate inputs within given bounds, and Hoeffding’s Inequality is used to validate that the learned MPC satisfies these bounds with high confidence. The result is a closed-loop statistical guarantee on stability and constraint satisfaction for the learned MPC. The proposed learning-based MPC framework is illustrated on a nonlinear benchmark problem, for which we learn a neural network controller with guarantees.

ics

arXiv PDF DOI [BibTex]

arXiv PDF DOI [BibTex]


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Robust Physics-based Motion Retargeting with Realistic Body Shapes

Borno, M. A., Righetti, L., Black, M. J., Delp, S. L., Fiume, E., Romero, J.

Computer Graphics Forum, 37, pages: 6:1-12, July 2018 (article)

Abstract
Motion capture is often retargeted to new, and sometimes drastically different, characters. When the characters take on realistic human shapes, however, we become more sensitive to the motion looking right. This means adapting it to be consistent with the physical constraints imposed by different body shapes. We show how to take realistic 3D human shapes, approximate them using a simplified representation, and animate them so that they move realistically using physically-based retargeting. We develop a novel spacetime optimization approach that learns and robustly adapts physical controllers to new bodies and constraints. The approach automatically adapts the motion of the mocap subject to the body shape of a target subject. This motion respects the physical properties of the new body and every body shape results in a different and appropriate movement. This makes it easy to create a varied set of motions from a single mocap sequence by simply varying the characters. In an interactive environment, successful retargeting requires adapting the motion to unexpected external forces. We achieve robustness to such forces using a novel LQR-tree formulation. We show that the simulated motions look appropriate to each character’s anatomy and their actions are robust to perturbations.

mg ps

pdf video [BibTex]

pdf video [BibTex]


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Comparison-Based Random Forests

Haghiri, S., Garreau, D., Luxburg, U. V.

International Conference on Machine learning (ICML), 2018 (conference)

slt

link (url) [BibTex]

link (url) [BibTex]


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Learning Independent Causal Mechanisms

Parascandolo, G., Kilbertus, N., Rojas-Carulla, M., Schölkopf, B.

Proceedings of the 35th International Conference on Machine Learning (ICML), 80, pages: 4033-4041, Proceedings of Machine Learning Research, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, July 2018 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Tempered Adversarial Networks

Sajjadi, M. S. M., Parascandolo, G., Mehrjou, A., Schölkopf, B.

Proceedings of the 35th International Conference on Machine Learning (ICML), 80, pages: 4448-4456, Proceedings of Machine Learning Research, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, July 2018 (conference)

ei

arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


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PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos

Parmas, P., Rasmussen, C., Peters, J., Doya, K.

Proceedings of the 35th International Conference on Machine Learning (ICML), 80, pages: 4065-4074, Proceedings of Machine Learning Research, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, July 2018 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


no image
Nonstationary GANs: Analysis as Nonautonomous Dynamical Systems

Mehrjou, A., Schölkopf, B.

Workshop on Theoretical Foundations and Applications of Deep Generative Models at ICML, July 2018 (conference)

ei

PDF [BibTex]

PDF [BibTex]


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Probabilistic Recurrent State-Space Models

Doerr, A., Daniel, C., Schiegg, M., Nguyen-Tuong, D., Schaal, S., Toussaint, M., Trimpe, S.

In Proceedings of the International Conference on Machine Learning, International Conference on Machine Learning (ICML), July 2018 (inproceedings) Accepted

Abstract
State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification. Deterministic versions of SSMs (e.g., LSTMs) proved extremely successful in modeling complex time-series data. Fully probabilistic SSMs, however, unfortunately often prove hard to train, even for smaller problems. To overcome this limitation, we propose a scalable initialization and training algorithm based on doubly stochastic variational inference and Gaussian processes. In the variational approximation we propose in contrast to related approaches to fully capture the latent state temporal correlations to allow for robust training.

am ics

arXiv pdf Project Page [BibTex]

arXiv pdf Project Page [BibTex]


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Convergence Rates of Gaussian ODE Filters

Kersting, H., Sullivan, T. J., Hennig, P.

arXiv preprint 2018, arXiv:1807.09737 [math.NA], July 2018 (article) In revision

Abstract
A recently-introduced class of probabilistic (uncertainty-aware) solvers for ordinary differential equations (ODEs) applies Gaussian (Kalman) filtering to initial value problems. These methods model the true solution $x$ and its first $q$ derivatives a priori as a Gauss--Markov process $\boldsymbol{X}$, which is then iteratively conditioned on information about $\dot{x}$. We prove worst-case local convergence rates of order $h^{q+1}$ for a wide range of versions of this Gaussian ODE filter, as well as global convergence rates of order $h^q$ in the case of $q=1$ and an integrated Brownian motion prior, and analyse how inaccurate information on $\dot{x}$ coming from approximate evaluations of $f$ affects these rates. Moreover, we present explicit formulas for the steady states and show that the posterior confidence intervals are well calibrated in all considered cases that exhibit global convergence---in the sense that they globally contract at the same rate as the truncation error.

pn

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Teaching a Robot Bimanual Hand-Clapping Games via Wrist-Worn IMUs

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

Frontiers in Robotics and Artificial Intelligence, 5(85), July 2018 (article)

Abstract
Colleagues often shake hands in greeting, friends connect through high fives, and children around the world rejoice in hand-clapping games. As robots become more common in everyday human life, they will have the opportunity to join in these social-physical interactions, but few current robots are intended to touch people in friendly ways. This article describes how we enabled a Baxter Research Robot to both teach and learn bimanual hand-clapping games with a human partner. Our system monitors the user's motions via a pair of inertial measurement units (IMUs) worn on the wrists. We recorded a labeled library of 10 common hand-clapping movements from 10 participants; this dataset was used to train an SVM classifier to automatically identify hand-clapping motions from previously unseen participants with a test-set classification accuracy of 97.0%. Baxter uses these sensors and this classifier to quickly identify the motions of its human gameplay partner, so that it can join in hand-clapping games. This system was evaluated by N = 24 naïve users in an experiment that involved learning sequences of eight motions from Baxter, teaching Baxter eight-motion game patterns, and completing a free interaction period. The motion classification accuracy in this less structured setting was 85.9%, primarily due to unexpected variations in motion timing. The quantitative task performance results and qualitative participant survey responses showed that learning games from Baxter was significantly easier than teaching games to Baxter, and that the teaching role caused users to consider more teamwork aspects of the gameplay. Over the course of the experiment, people felt more understood by Baxter and became more willing to follow the example of the robot. Users felt uniformly safe interacting with Baxter, and they expressed positive opinions of Baxter and reported fun interacting with the robot. Taken together, the results indicate that this robot achieved credible social-physical interaction with humans and that its ability to both lead and follow systematically changed the human partner's experience.

hi

DOI [BibTex]

DOI [BibTex]


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Real-time Perception meets Reactive Motion Generation

(Best Systems Paper Finalists - Amazon Robotics Best Paper Awards in Manipulation)

Kappler, D., Meier, F., Issac, J., Mainprice, J., Garcia Cifuentes, C., Wüthrich, M., Berenz, V., Schaal, S., Ratliff, N., Bohg, J.

IEEE Robotics and Automation Letters, 3(3):1864-1871, July 2018 (article)

Abstract
We address the challenging problem of robotic grasping and manipulation in the presence of uncertainty. This uncertainty is due to noisy sensing, inaccurate models and hard-to-predict environment dynamics. Our approach emphasizes the importance of continuous, real-time perception and its tight integration with reactive motion generation methods. We present a fully integrated system where real-time object and robot tracking as well as ambient world modeling provides the necessary input to feedback controllers and continuous motion optimizers. Specifically, they provide attractive and repulsive potentials based on which the controllers and motion optimizer can online compute movement policies at different time intervals. We extensively evaluate the proposed system on a real robotic platform in four scenarios that exhibit either challenging workspace geometry or a dynamic environment. We compare the proposed integrated system with a more traditional sense-plan-act approach that is still widely used. In 333 experiments, we show the robustness and accuracy of the proposed system.

am

arxiv video video link (url) DOI Project Page [BibTex]