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Sneha Saha, Leandro Minku, Xin Yao, Bernhard Sendhoff, Stefan Menzel, "Exploiting 3D Variational Autoencoders For Interactive Vehicle Design ", 17th International Design Conference (Design 2022), 2022.

Abstract

In automotive digital development, 3D prototype creation is a team effort of designers and engineers, each contributing with creative ideas and technical design evaluations through means of computer simulations. To support the team in the 3D design ideation and exploration task, we propose an interactive 3D cooperative design system for assisted design explorations and faster performance estimations. We utilize the advantage of geometric deep lea...



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Satchit Ramnath, Jami Shah, Patricia Wollstadt, Mariusz Bujny, Stefan Menzel, Duane Detwiler, "OSU-Honda automobile hood dataset (CarHoods10k)", Dryad.org, 2022.

Abstract

The CarHoods10k data set comprises a set of over 10,000 3D mesh geometries for variants of car hood frames, generated through an automated, industry-grade Computer Aided Design (CAD) workflow described in Ramnath (2019). The data set provides realistic designs that were validated by experts with respect to realism, manufacturability, variability, and performance. Variations in geometries were generated by a feature-based approach that varies para...



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Jihong Zhu, Michael Gienger, Jens Kober, "Learning Task-Parameterized Skills from Few Demonstrations", ICRA / RA-L, 2022.

Abstract

Moving away from repetitive tasks, robots nowadays demand versatile skills that adapt to different situations. Task- parameterized approaches improve the generalization of motion policy by encoding relevant contextual information in the task parameters, hence enabling flexible task executions. However, training such a policy often requires collecting multiple demon- strations in different situations. To create these situations, objects or ...



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Sander van Rijn, Sebastian Schmitt, Matthijs van Leeuwen, Thomas Bäck, "Finding Efficient Trade-offs in Multi-Fidelity Response Surface Modeling ", Engineering optimization, 2022.

Abstract

In the context of optimization approaches to engineering applications, time-consuming simulations are often utilized which can be configured to deliver solu- tions for various levels of accuracy, commonly referred to as different fidelity levels. It is common practice to train hierarchical surrogate models on the objective functions in order to speed-up the optimization process. These operate under the assumption that there is a correla- ...



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Jihong Zhu, Andrea Cherubini, Claire Dune, David Navarro-Alarcon, Farshid Alambeigi, Dmitry Berenson, Fanny Ficuciello, Michael Gienger, Kensuke Harada, Jens Kober, Xiang Li, Jia Pan, Wenzhen Yuan, "Challenges and Outlook in Robotic Manipulation of Deformable Objects", Robotics and Automation Magazine (RAM), 2022.

Abstract

Deformable object manipulation (DOM) is an emerging research problem in robotics. The ability to manipulate deformable objects endows robots with higher autonomy and promises new applications in the industrial, services, and healthcare sectors. However, compared to rigid object manipulation, the manipulation of deformable objects is considerably more complex, and is still an open research problem. Addressing DOM challenges demands breakthroughs i...



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Shen Li, Theodoros Stouraitis, Michael Gienger, Sethu Vijayakumar, Julie Shah, "Set-based State Estimation with Probabilistic Consistency Guarantee under Epistemic Uncertainty", Research and Automation Letters, 2022.

Abstract

Consistent state estimation is challenging, especially when both dynamic and observation models are nonlinear and learned from data. In this work, we develop a set-based estimation algorithm, that produces zonotopic state estimates that respect the epistemic uncertainties in the learned mod- els, in addition to the aleatoric uncertainties. Our algorithm guarantees probabilistic consistency, in the sense that the true state is always bound...



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Chao Wang and Anna Belardinelli, "Investigating explainable human-robot interaction with augmented reality", International Workshop on Virtual, Augmented, and Mixed-Reality for Human-Robot Interactions (VAM@HRI2022), 2022.

Abstract

In learning by demonstration with social robots, a fluid and coordinated interaction between human teacher and robotic learner is particularly critical and yet often difficult to assess. This is even more the case, if robots are to learn from non-expert users. In such cases, it is sometimes troublesome for the teacher to get a grasp of what the robot knows or to assess if a correct representation of the task has been formed even before the robot ...



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Muhammad Haris, Mathias Franzius, Ute Bauer-Wersing, " Learning Visual Landmarks for Localization with Minimal Supervision", International Conference on IMAGE ANALYSIS AND PROCESSING, 2022.

Abstract

Camera localization is one of the fundamental requirements for vision-based mobile robots, self-driving cars, and augmented reality applications. In this context, learning spatial representations relative to unique regions in a scene with Slow Feature Analysis (SFA) has demonstrated large-scale localization. However, it relies either on pre-existing object detectors or hand-labeled data to train a CNN for recognizing unique regions in a scene. We...



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Timo Friedrich, "Three-Dimensional Voxel-Based Neural Style Transfer and Quantification", Bielefeld University, 2022.

Abstract

Machine Learning and especially Deep Learning has started to conquer another human trait in recent years by being able to perform creative tasks. Neural Network based systems compose music, create dream-like creatures, generate faces of fictional persons, and even write complete books. Accordingly, of course, they also generate visual art. Here, Neural Style Transfer stylizes images and photographs with a style extracted from an arbitrary image, ...



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Fabio Muratore, Fabio Ramos, Wenhao Yu, Greg Turk, Michael Gienger, Jan Peters, "Robot Learning from Randomized Simulations: A Review", Frontiers Robotics and AI, 2022.

Abstract

The rise of deep learning has caused a paradigm shift in robotics research, favoring methods that require giant amounts of data. It is prohibitively expensive to generate such data sets on a physical platform. Therefore, state-of-the art approaches learn in simulation where data generation is fast as well as inexpensive, and subsequently transfer the knowledge to the real robot sim-to-real. Despite becoming more and more realistic, all simulators...



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