Publications

TRI Authors: Muratahan Aykol, Santosh Suram

All Authors: Logan Ward, Muratahan Aykol, Ben Blaiszik, Ian Foster, Bryce Meredig, James Saal, Santosh Suram Ongoing, rapid innovations in fields ranging from microelectronics, aerospace, and automotive to defense, energy, and health demand new advanced materials at even greater rates and lower costs. Traditional materials R&D methods offer few paths to achieve both outcomes simultaneously. Materials informatics, while a nascent field, offers such a promise through screening, growing databases of materials for new applications, learning new relationships from existing data resources, and building fast predictive models. We highlight key materials informatics successes from the atomic-scale modeling community, and discuss the ecosystem of open data, software, services, and infrastructure that have led to broad adoption of materials informatics approaches. We then examine emerging opportunities for informatics in materials science and describe an ideal data ecosystem capable of supporting similar widespread adoption of materials informatics, which we believe will enable the faster design of materials.  Read More

Citation: Ward, Logan, Muratahan Aykol, Ben Blaiszik, Ian Foster, Bryce Meredig, James Saal, and Santosh Suram. "Strategies for accelerating the adoption of materials informatics." MRS Bulletin 43, no. 9 (2018): 683-689.

TRI Author: Simon Stent All Authors: Recasens, Adria, Petr Kellnhofer, Simon Stent, Wojciech Matusik, and Antonio Torralba

We introduce a saliency-based distortion layer for convolutional neural networks that helps to improve the spatial sampling of input data for a given task. Our differentiable layer can be added as a preprocessing block to existing task networks and trained altogether in an end-to-end fashion. The effect of the layer is to efficiently estimate how to sample from the original data in order to boost task performance. For example, for an image classification task in which the original data might range in size up to several megapixels, but where the desired input images to the task network are much smaller, our layer learns how best to sample from the underlying high resolution data in a manner which preserves task-relevant information better than uniform downsampling. This has the effect of creating distorted, caricature-like intermediate images, in which idiosyncratic elements of the image that improve task performance are zoomed and exaggerated. Unlike alternative approaches such as spatial transformer networks, our proposed layer is inspired by image saliency, computed efficiently from uniformly downsampled data, and degrades gracefully to a uniform sampling strategy under uncertainty. We apply our layer to improve existing networks for the tasks of human gaze estimation and fine-grained object classification. Code for our method is available in: http://github.com/recasens/Saliency-Sampler. Read More

Citation: Recasens, Adria, Petr Kellnhofer, Simon Stent, Wojciech Matusik, and Antonio Torralba. "Learning to zoom: a saliency-based sampling layer for neural networks." In Proceedings of the European Conference on Computer Vision (ECCV), pp. 51-66. 2018.

TRI Author: Wadim Kehl

All Authors: Fabian Manhardt, Wadim Kehl, Nassir Navab, Federico Tombari

We present a novel approach for model-based 6D pose refinement in color data. Building on the established idea of contour-based pose tracking, we teach a deep neural network to predict a translational and rotational update. At the core, we propose a new visual loss that drives the pose update by aligning object contours, thus avoiding the definition of any explicit appearance model. In contrast to previous work our method is correspondence-free, segmentation-free, can handle occlusion and is agnostic to geometrical symmetry as well as visual ambiguities. Additionally, we observe a strong robustness towards rough initialization. The approach can run in real-time and produces pose accuracies that come close to 3D ICP without the need for depth data. Furthermore, our networks are trained from purely synthetic data and will be published together with the refinement code to ensure reproducibility. Read More

Citation: Manhardt, Fabian, Wadim Kehl, Nassir Navab, and Federico Tombari. "Deep model-based 6d pose refinement in rgb." In Proceedings of the European Conference on Computer Vision (ECCV), pp. 800-815. 2018.

TRI Author: Wadim Kehl All Authors:Tomas Hodan, Frank Michel, Eric Brachmann, Wadim Kehl, Anders Buch, Dirk Kraft, Bertram Drost, Joel Vidal, Stephan Ihrke, Xenophon Zabulis, Caner Sahin, Fabian Manhardt, Federico Tombari, Tae-Kyun Kim, Jiri Matas, Carsten Rother

We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input image. The training data consists of a texture-mapped 3D object model or images of the object in known 6D poses. The benchmark comprises of: i) eight datasets in a unified format that cover different practical scenarios, including two new datasets focusing on varying lighting conditions, ii) an evaluation methodology with a pose-error function that deals with pose ambiguities, iii) a comprehensive evaluation of 15 diverse recent methods that captures the status quo of the field, and iv) an online evaluation system that is open for continuous submission of new results. The evaluation shows that methods based on point-pair features currently perform best, outperforming template matching methods, learning-based methods and methods based on 3D local features. The project website is available at bop.felk.cvut.cz. Read MoreCitation: Hodan, Tomas, Frank Michel, Eric Brachmann, Wadim Kehl, Anders GlentBuch, Dirk Kraft, Bertram Drost et al. "Bop: Benchmark for 6d object pose estimation." In Proceedings of the European Conference on Computer Vision (ECCV), pp. 19-34. 2018.

TRI Author: Muratahan Aykol

All Authors: Vinay I Hegde, Muratahan Aykol, Scott Kirklin, Chris Wolverton

Understanding how the arrangement of atoms and their interactions determine material behavior has been the dominant paradigm in materials science. A complementary approach is studying the organizational structure of networks of materials, defined on the basis of interactions between materials themselves. In this work, we present the "phase diagram of all known inorganic materials", an extremely-dense complex network of nearly 2.1×104 stable inorganic materials (nodes) connected with 41×106 tie-lines (edges) defining their two-phase equilibria, as computed via high-throughput density functional theory. We show that the degree distribution of this network follows a lognormal form, with each material connected to on average 18% of the other materials in the network via tie-lines. Analyzing the structure and topology of this network has potential to uncover new materials knowledge inaccessible from the traditional bottom-up (atoms to materials) approaches. As an example, we derive a data-driven metric for the reactivity of a material as characterized by its connectedness in the network, and quantitatively identify the noblest materials in nature. Read more

Citation: Hegde, Vinay I., Muratahan Aykol, Scott Kirklin, and Chris Wolverton. "The phase diagram of all inorganic materials." In Science Advances, arXiv preprint arXiv:1808.10869 (2018).

TRI Author: Simon Stent

All Authors: Xishuai Peng, Ruirui Liu, Yi Lu Murphey, Simon Stent, Yuanxiang Li

Driving maneuver detection is one of the most challenging tasks in Advanced Driver Assistance Systems (ADAS). Research has shown that the early notification of improper driving maneuvers is helpful to avoid fatalities and serious accidents. In this paper, we introduce a driver maneuvering detection (DMD) system. The DMD system contains three major computational components, distance based representation of driving context, combined features of vehicle trajectory and VGG-19 network features extracted from the video images of vehicle front view, and a Long Short-Term Memory (LSTM)-based neural network model to learn sequence knowledge in driving maneuvering events. We show through experiments that the DMD system is capable of learning the latent features of five different classes of driving maneuvers and achieving significantly better performance than traditional classification methods on real-world driving trips. Read More

Citation: Peng, Xishuai, Ruirui Liu, Yi Lu Murphey, Simon Stent, and Yuanxiang Li. "Driving maneuver detection via sequence learning from vehicle signals and video images." In 2018 24th International Conference on Pattern Recognition (ICPR), pp. 1265-1270. IEEE, 2018.

TRI Author: Hongkai Dai

All Authors: Romeo Orsolino, Michele Focchi, Carlos Mastalli, Hongkai Dai, Darwin G. Caldwell and Claudio Semini

Motion planning in multicontact scenarios has recently gathered interest within the legged robotics community, however actuator force/torque limits are rarely considered. We believe that these limits gain paramount importance when the complexity of the terrains to be traversed increases. We build on previous research from the field of robotic grasping to propose two new six-dimensional bounded polytopes named the Actuation Wrench Polytope (AWP) and the Feasible Wrench Polytope (FWP). We define theAWP as the set of all the wrenches that a robot can generate while considering its actuation limits. This considers the admissible contact forces that the robot can generate given its current configuration and actuation capabilities. The Contact Wrench Cone (CWC) instead includes features of the environment such as the contact normal or the friction coefficient. The intersection of the AWP and of the CWC results in a convex polytope, the FWP, which turns out to be more descriptive of the real robot capabilities than existing simplified models, while maintaining the same compact representation. We explain how to efficiently compute the vertex-description of the FWP that is then used to evaluate a feasibility factor that we adapted from the field of robotic grasping. This allows us to optimize for robustness to external disturbance wrenches. Based on this, we present an implementation of a motion planner for our quadruped robot HyQ that provides online Center of Mass trajectories that are guaranteed to be statically stable and actuation-consistent. Read More

Citation: Orsolino, Romeo, Michele Focchi, Carlos Mastalli, Hongkai Dai, Darwin G. Caldwell, and Claudio Semini. "Application of wrench-based feasibility analysis to the online trajectory optimization of legged robots." IEEE Robotics and Automation Letters 3, no. 4 (2018): 3363-3370.

TRI Author: Soonho Kong All Authors: Soonho Kong, Armando Solar-Lezama, Sicun Gao We propose δ-complete decision procedures for solving satisfiability of nonlinear SMT problems over real numbers that contain universal quantification and a wide range of nonlinear functions. The methods combine interval constraint propagation, counterexample-guided synthesis, and numerical optimization. In particular, we show how to handle the interleaving of numerical and symbolic computation to ensure delta-completeness in quantified reasoning. We demonstrate that the proposed algorithms can handle various challenging global optimization and control synthesis problems that are beyond the reach of existing solvers. Read more 

Citation: Kong, Soonho, Armando Solar-Lezama, and Sicun Gao. "Delta-decision procedures for exists-forall problems over the reals." In International Conference on Computer Aided Verification, pp. 219-235. Springer, Cham, 2018.

TRI Author: Adrien Gaidon

All Authors: Adrien Gaidon, Antonio Lopez, Florent Perronnin

The recent successes in many visual recognition tasks, such as image classification, object detection, and semantic segmentation can be attributed in large part to three factors: (i) advances in end-to-end trainable deep learning models (LeCun 2015), (ii) the progress of computing hardware, and (iii) the introduction of increasingly larger labeled datasets such as PASCAL VOC (Everingham et al. 2010), KITTI (Geiger et al. 2012), ImageNet (Russakovsky et al. 2015), MS-COCO (Lin et al. 2014), and Cityscapes (Cordts et al. 2016), among others. In fact, recent results (Sun et al. 2017; Hestness et al. 2017) indicate that the reliability of current visual models might not be limited by the algorithms themselves but by the type and amount of supervised data available. Therefore, to tackle more challenging tasks, such as video scene understanding, progress is needed not only on the algorithmic and hardware fronts but also on the data front, both for learning and quantitative evaluation. However, acquiring and densely labeling a large visual dataset with ground truth information (e.g. semantic labels, depth, optical flow) for each new problem is not a scalable alternative.  Read More

Citation: Gaidon, Adrien, Antonio Lopez, and Florent Perronnin. "The reasonable effectiveness of synthetic visual data." International Journal of Computer Vision 126, no. 9 (2018): 899-901.

TRI Author: Hongkai Dai

All Authors: Bernardo Aceituno-Cabezas, Carlos Mastalli, Hongkai Dai, Michele Focchi, Andreea Radulescu, Darwin G. Caldwell, Jose Cappelletto, Juan C. Grieco, Gerardo Fernandez-Lopez, Claudio Semini

Traditional motion planning approaches for multilegged locomotion divide the problem into several stages, such as contact search and trajectory generation. However, reasoning about contacts and motions simultaneously is crucial for the generation of complex whole-body behaviors. Currently, coupling theses problems has required either the assumption of a fixed gait sequence and flat terrain condition, or nonconvex optimization with intractable computation time. In this letter, we propose a mixed-integer convex formulation to plan simultaneously contact locations, gait transitions, and motion, in a computationally efficient fashion. In contrast to previous works, our approach is not limited to flat terrain nor to a prespecified gait sequence. Instead, we incorporate the friction cone stability margin, approximate the robot's torque limits, and plan the gait using mixed-integer convex constraints. We experimentally validated our approach on the HyQ robot by traversing different challenging terrains, where nonconvexity and flat terrain assumptions might lead to suboptimal or unstable plans. Our method increases the motion robustness while keeping a low computation time. Read More

Citation: Aceituno-Cabezas, Bernardo, Carlos Mastalli, Hongkai Dai, Michele Focchi, Andreea Radulescu, Darwin G. Caldwell, José Cappelletto, Juan C. Grieco, Gerardo Fernández-López, and Claudio Semini. "Simultaneous contact, gait, and motion planning for robust multilegged locomotion via mixed-integer convex optimization." IEEE Robotics and Automation Letters 3, no. 3 (2017): 2531-2538.