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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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Steffen Limmer, Felix Lanfermann, Nils Einecke, "SPOC: Approaches by HRI", Space Optimisation Competition (SpOC) Workshop, 2022.

Abstract

Present approach for ESA GECCO optimization challenge at special SpOC workshop held by the ESA....



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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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Hemant Singh, Tapabrata Ray, Mohammad Rana, Steffen Limmer, Tobias Rodemann, Markus Olhofer, "Investigating the Use of Linear Programming and Evolutionary Algorithms for Multi-objective Electric Vehicle Charging Problem", IEEE Access, vol. 10, pp. 115322-115337, 2022.

Abstract

With the increasing uptake of electric vehicles (EVs), the need for efficient scheduling of EV charging is increasingly becoming important. A charging station operator needs to identify charging/discharging power of the client EVs over a time horizon while considering multiple objectives, such as operating costs and the peak power drawn from the grid. Evolutionary algorithms (EAs) are a popular choice when faced with problems involving multiple o...



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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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Thiago Rios, "Learning-based Representations of High-dimensional CAE Models for Automotive Design Optimization", Leiden University, 2022.

Abstract

In design optimization problems, engineers typically handcraft design representations based on personal expertise, which leaves a fingerprint of the user experience in the optimization data. Thus, learning this notion of experience as transferrable design features has potential to improve the performance of similar, yet more challenging, design optimization problems. However, engineering design data are unstructured, high-dimensional and often ha...



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Tim Puphal, Raphael Wenzel, Benedict Flade, Malte Probst , Julian Eggert, "Importance Filtering with Risk Models for Complex Driving Situations", IEEE ICRAE 2022, 2022.

Abstract

Self-driving cars face complex driving situations with a large amount of agents when moving in crowded cities. However, some of the agents are actually not influencing the behavior of the self-driving car. Filtering out unimportant agents would inherently simplify the behavior or motion planning task for the system. The planning system can then focus on fewer agents to find optimal behavior solutions for the ego agent. This is helpful especially ...



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Charlie Street, Bruno Lacerda, Michal Staniaszek, Manuel Mühlig, Nick Hawes, "Context-Aware Modelling for Multi-Robot Systems Under Uncertainty", International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2022.

Abstract

Formal models of multi-robot behaviour are fundamental to plan- ning, simulation, and model checking techniques. However, existing models are invalidated by strong assumptions that fail to capture execution-time multi-robot behaviour, such as simplistic duration models or synchronisation constraints. In this paper we propose a novel multi-robot Markov automaton formulation which mod- els asynchronous multi-robot execution in continuous time...



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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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