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Anna Belardinelli, Dirk Ruiken, Daniel Tanneberg, "Intention estimation from gaze and motion features for human-robot shared-control object manipulation", IROS 2022, 2022.

Abstract

Shared control can help in teleoperated object manipulation by assisting with the execution of the user’s intention. To this end, robust and prompt intention estimation is needed, which relies on behavioral observations. Here, an intention estimation framework is presented, which uses natural gaze and motion features to predict the current action and the target object. The system is trained and tested in a simulated environment with pic...



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Viktor Losing and Martina Hasenjäger, "A Multi-Modal Gait Database of Natural Everyday-Walk in an Urban Environment", Scientific Data, vol. 9, no. 473, 2022.

Abstract

Human gait data have traditionally been recorded in controlled laboratory environments focusing on single aspects in isolation. In contrast, the database presented here provides recordings of everyday walk scenarios in a natural urban environment, including synchronized IMU−, FSR−, and gaze data. Twenty healthy participants (five females, fifteen males, between 18 and 69 years old, 178.5 ± 7.64 cm, 72.9 ± 8.7 kg) wore a full-body Lycra suit...



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Linda Spaa, van der, Jens Kober, Michael Gienger, "Disagreement-Aware Control for Online Learning Personal Solutions to Intention-Aware Physical Human-Robot Cooperation on Real Hardware", IEEE 2022 ICRA full day workshop: Shared Autonomy in Physical Human-Robot Interaction: Adaptability and Trust, 2022.

Abstract

In this paper we present a control methodology that allows the robot to be controlled and learn from its observations in a high-level abstract state-action space, without the human having to pay special attention to the robot’s kinematics/ dynamics or limits. A small number of data efficient steps allow quick setup and easy transfer to different setups in similarly structured contexts. The controller is tested on a 7-DoF Franka Emika Pan...



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Felix Lanfermann and Sebastian Schmitt, "Concept identification for complex engineering datasets", Advanced Engineering Informatics, 2022.

Abstract

Finding meaningful concepts in engineering application datasets which allow for a sensible grouping of designs is very helpful in many contexts. It allows for determining different groups of designs with similar properties and provides useful knowledge in the engineering decision making process. Also, it opens the route for further refinements of specific design candidates which exhibit certain characteristic features. In this work, an approach t...



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Ruben Albers, Judith Dörrenbächer, Martin Weigel, Dirk Ruiken, Thomas Weisswange, Christian Goerick, Marc Hassenzahl, "Meaningful Telerobots in Informal Care - A Conceptual Design Case", Nordic Conference on Human-Computer Interaction (NordiCHI 2022), 2022.

Abstract

While telerobots off er potentially unique ways to shape human-human relationships, current concepts often imitate existing practices, such as face-to-face conversations. Using the example of informal care, we explored whether the explicit use of the unique possibilities provided by telerobots can lead to meaningful extended or unique care practices. Initial in-depth conversations with fi ve caregivers and care recipients about their care prac...



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Anna Belardinelli, Dirk Ruiken, Daniel Tanneberg, "Intention estimation from gaze and motion features for human-robot shared-control object manipulation", arXiv, 2022.

Abstract

Shared control can help in teleoperated object manipulation by assisting with the execution of the user’s intention. To this end, robust and prompt intention estimation is needed, which relies on behavioral observations. Here, an intention estimation framework is presented, which uses natural gaze and motion features to predict the current action and the target object. The system is trained and tested in a simulated environment with pick and pl...



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Matti Krüger, "An Enactive Approach to Augmenting Situation Awareness", TeaP 2022 - Conference of Experimental Psychologists, 2022.

Abstract

Safe mobility in traffic environments heavily rely on an individual's ability to perceive, comprehend, and anticipate relevant information for collision avoidance. Collectively termed "situation awareness", the lack thereof continues to be a major contribution to accidents attributed to human errors. How can we design technology to mitigate this? This presentation begins by addressing the critical factors for awareness formation, particularly in ...



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Chao Wang, Derck Chu, Matti Krüger, Thomas Weisswange, "Hybrid Eyes: Design and Evaluation of the Prediction-level Cooperative Driving with a Real-World Automated Driving System", AutoUI 2022, pp. 8, 2022.

Abstract

While automated driving systems (ADS) have progressed fast in recent years, there are still various situations in which an ADS cannot perform as well as a human driver. Being able to anticipate situations, particularly when it comes to predicting the behaviour of surrounding traffic, is one of the key elements for ensuring safety and comfort. As humans are still surpassing state-of-the-art ADS in this task, this led to the development of a new co...



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Sadullah Canakci , Nikolay Matyunin, Kalman Graffi, Manuel Egele, "TargetFuzz: Using DARTs to Guide Directed Greybox Fuzzers", The 17th ACM ASIA Conference on Computer and Communications Security (ACM ASIACCS 2022), 2022.

Abstract

Software development is a continuous and incremental process. Developers continuously improve their software in small batches rather than in one large batch. The high frequency of small batches makes it essential to use effective testing methods that detect bugs under limited testing time. To this end, researchers propose directed greybox fuzzing (DGF) which aims to generate test cases towards stressing certain target sites. Different from the co...



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Andrea Castellani, Sebastian Schmitt, Barbara Hammer, "Stream-based Active Learning with Verification Latency in Non-stationary Environments", 31st International Conference on Artificial Neural Networks (ICANN), 2022.

Abstract

Data stream classification is an important problem in the field of machine learning. Due to the non-stationary nature of the data where the underlying distribution changes over time (concept drift), the model needs to continuously adapt to new data statistics. Stream-based Active Learning (AL) approaches address this problem by interactively querying a human expert to provide new data labels for the most recent samples, within a limited bud...



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