05 Fakultät Informatik, Elektrotechnik und Informationstechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6
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Item Open Access Endowing a NAO robot with practical social-touch perception(2022) Burns, Rachael Bevill; Lee, Hyosang; Seifi, Hasti; Faulkner, Robert; Kuchenbecker, Katherine J.Social touch is essential to everyday interactions, but current socially assistive robots have limited touch-perception capabilities. Rather than build entirely new robotic systems, we propose to augment existing rigid-bodied robots with an external touch-perception system. This practical approach can enable researchers and caregivers to continue to use robotic technology they have already purchased and learned about, but with a myriad of new social-touch interactions possible. This paper presents a low-cost, easy-to-build, soft tactile-perception system that we created for the NAO robot, as well as participants’ feedback on touching this system. We installed four of our fabric-and-foam-based resistive sensors on the curved surfaces of a NAO’s left arm, including its hand, lower arm, upper arm, and shoulder. Fifteen adults then performed five types of affective touch-communication gestures (hitting, poking, squeezing, stroking, and tickling) at two force intensities (gentle and energetic) on the four sensor locations; we share this dataset of four time-varying resistances, our sensor patterns, and a characterization of the sensors’ physical performance. After training, a gesture-classification algorithm based on a random forest identified the correct combined touch gesture and force intensity on windows of held-out test data with an average accuracy of 74.1%, which is more than eight times better than chance. Participants rated the sensor-equipped arm as pleasant to touch and liked the robot’s presence significantly more after touch interactions. Our promising results show that this type of tactile-perception system can detect necessary social-touch communication cues from users, can be tailored to a variety of robot body parts, and can provide HRI researchers with the tools needed to implement social touch in their own systems.Item Open Access Analytical and numerical investigations of form-finding methods for tensegrity structures(2007) Gomez Estrada, Giovani; Bungartz, Hans-Joachim (Prof. Dr.)The analysis of statically indeterminate structures requires the calculation of an initial equilibrium geometry. Tensegrity structures are one of such statically indeterminate structures, with the additional constraint of holding their equilibrium configuration with the action of internal forces and without any anchorage point or external forces. The only source of balance is the state of self-stress held among tensile and compression forces. Tensegrity structures are thus statically indeterminate structures in a stable state of self-stressed self-equilibrium. The basic problem with the modelling of statically indeterminate structures is that there is no unique solution for the forces or geometry that equilibrate a structure. This is where form-finding comes into play. The process of determining their three-dimensional equilibrium shape is commonly called form-finding. This dissertation presents two investigations, one analytical and one numerical on the form-finding of tensegrity structures. Both are in fact complementary. The main results from these investigations appear in [77, 78, 79, 80]. The analytical form-finding for a class of highly symmetric structures with cylindrical shape is first presented, while the numerical procedure for general structures is given in the second part. A thorough analysis of tensegrity cylinders, e.g., the triplex and the quadruplex, is presented in analytical form. Moreover, the numerical procedure here presented is able to reproduce the results obtained with other form-finding methods with great accuracy. The versatility of the novel numerical form-finding procedure is nonetheless demonstrated by solving not only cylindrical and spherical but also new tensegrity structures.Item Open Access Learning-based control and localization of magnetic soft millirobots(2024) Demir, Sinan Özgün; Sitti, Metin (Prof. Dr.)Soft millirobots have promising biomedical applications due to mechanical compliance, absorbing excess forces without additional computational effort, and multifunctionality. Especially with wireless multimodal locomotion capabilities, magnetic soft millirobots (i.e., ≤1 cm) have emerged as potential minimally invasive medical robotic platforms as they can access confined and hard-to-reach spaces in the human body (e.g., distal vascular regions), and carry out medical applications, such as on-demand drug delivery, sensing, and embolization, in a target location. For such potential biomedical applications, the adaptivity of the robot control is essential to ensure the continuity of the operations, as task environment conditions show dynamic variations that can alter the robot's performance. However, fabrication-, material-, physical-interaction-dependent variations, and complex kinematics with virtually infinite degrees of freedom arising from the nature of their soft material structure limit the applicability of the conventional modeling and control methods. The main objective of this dissertation is to establish a data-efficient adaptive multimodal locomotion framework for the targeted application scenarios of magnetic soft millirobots. To this end, a probabilistic learning approach leveraging Bayesian optimization (BO) and Gaussian processes (GPs) is introduced to address the controller adaptation challenge. First, the efficacy of the BO to fabrication variabilities is shown on three different robots fabricated following the same steps. Next, through augmented tests on benchmark datasets, it is shown that transferring the posterior mean learned by one robot as the prior mean to the other robots and test cases improves the learning performance of BO by achieving quicker gait adaptation. Afterward, the controller adaptation method employing the proposed transfer learning approach is demonstrated in various task spaces with varying surface adhesion, surface roughness, and medium viscosity properties. To further improve the adaptation performance by including multimodal locomotion, the sim-to-real transfer learning method is developed in the third study. In this regard, a data-driven simulation environment is designed, and its accuracy is demonstrated by comparing the simulated results to the physical experiments. Leveraging the simulated experience and BO based transfer learning, it is demonstrated that sim-to-real transfer learning provides efficient locomotion learning. Furthermore, the adequacy of the automated locomotion adaptation through the Kullback-Leibler divergence-based domain recognition approach is shown to changing environmental conditions. As the secondary objective, a new localization method using electrical impedance tomography is introduced. The applicability of the proposed approach is demonstrated for stationary and moving cases in environments with and without any obstacles. With these contributions, this thesis proposes a domain-adaptive locomotion learning framework enabling the soft millirobot locomotion to quickly and continuously adapt to environmental changes while exploring actuation space.Item Open Access Avoiding shortcut-learning by mutual information minimization in deep learning-based image processing(2023) Fay, Louisa; Cobos, Erick; Yang, Bin; Gatidis, Sergios; Küstner, ThomasItem Open Access Automated imaging-based abdominal organ segmentation and quality control in 20,000 participants of the UK Biobank and German National Cohort Studies(2022) Kart, Turkay; Fischer, Marc; Winzeck, Stefan; Glocker, Ben; Bai, Wenjia; Bülow, Robin; Emmel, Carina; Friedrich, Lena; Kauczor, Hans-Ulrich; Keil, Thomas; Kröncke, Thomas; Mayer, Philipp; Niendorf, Thoralf; Peters, Annette; Pischon, Tobias; Schaarschmidt, Benedikt M.; Schmidt, Börge; Schulze, Matthias B.; Umutle, Lale; Völzke, Henry; Küstner, Thomas; Bamberg, Fabian; Schölkopf, Bernhard; Rückert, Daniel; Gatidis, SergiosLarge epidemiological studies such as the UK Biobank (UKBB) or German National Cohort (NAKO) provide unprecedented health-related data of the general population aiming to better understand determinants of health and disease. As part of these studies, Magnetic Resonance Imaging (MRI) is performed in a subset of participants allowing for phenotypical and functional characterization of different organ systems. Due to the large amount of imaging data, automated image analysis is required, which can be performed using deep learning methods, e. g. for automated organ segmentation. In this paper we describe a computational pipeline for automated segmentation of abdominal organs on MRI data from 20,000 participants of UKBB and NAKO and provide results of the quality control process. We found that approx. 90% of data sets showed no relevant segmentation errors while relevant errors occurred in a varying proportion of data sets depending on the organ of interest. Image-derived features based on automated organ segmentations showed relevant deviations of varying degree in the presence of segmentation errors. These results show that large-scale, deep learning-based abdominal organ segmentation on MRI data is feasible with overall high accuracy, but visual quality control remains an important step ensuring the validity of down-stream analyses in large epidemiological imaging studies.