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Haptic Intelligence Master Thesis Wrist-Worn Pressure Pulses for Phantom Directional Cues in VR Kadmani, A. Technical University of Munich, Munich, Germany, September 2025, M.Sc. in Electrical Engineering and Information Technology (Published)
Haptic feedback in today's VR systems is often limited to vibration delivered through handheld controllers, leaving a gap for compact devices that can convey spatial cues without occupying the hands. This thesis presents the design and evaluation of SuperCUTE, a wrist-worn pressure feedback device that uses four soft electrohydraulic actuators to elicit phantom tactile sensations around the wrist. The device was evaluated with n = 20 participants in a user study comprising two tasks. In Task 1 (circular GUI), single-actuator cues produced tightly clustered responses (median resultant length R = 0.92); about 70% of trials fell within ± 22.5° of the stimulated cardinal. Adjacent-actuator pairs yielded in-between percepts (about 70% of reports), and intensity imbalance shifted perceived location toward the stronger actuator; reported intensity was higher for strong than weak drives (mean 0.76 vs. 0.32). Across cues, Rayleigh tests indicated strong clustering of response angles (median R ≈ 0.82). In Task 2 (VR), hand trajectories during 5 s cues aligned with cue geometry; end-directions showed strong clustering (median R ≈ 0.78), and latency estimated from a 1 cm displacement threshold had a median of 1.25 s (IQR 0.61 s). Questionnaire responses indicated clear, comfortable, and usable cues. Overall, pressure pulses are a feasible approach for directional wrist cues in VR. We provide device documentation, datasets, and analysis code to support pressure-based wearable haptics.
BibTeX
Haptic Intelligence Master Thesis Diffusion Models for Fast and Accurate Approximate Model Predictive Control Marquez Julbe, P. Eindhoven University of Technology, Eindhoven, the Netherlands, December 2024, Master of Science in Systems and Control (Published)
Model predictive control (MPC) is a powerful control and planning framework for a large class of problems, yet its practical application remains limited by computational demands. While previous efforts have focused on approximating MPC with explicit representations for high-frequency real-time deployment, handling complex MPC formulations with multiple local optima or set-valued global optima remains an open challenge in practice. This thesis explores the use of diffusion models for approximate MPC, enabling their application in such scenarios with low computational time. We introduce a novel diffusion-based approximator capable of accurately modeling multi-modal out- put distributions, while achieving computation times under 2.5 ms, allowing users to efficiently sample multiple feasible and locally optimal solutions with no additional computational overhead. Our method is quantitatively compared with traditional least-squares regression models, demonstrating significant improvements. Experimental validation is performed on a 7-DOF KUKA LBR4+ robotic arm operating at 250 Hz, confirming the benefits of our approach and providing insights into high-frequency neural control. Additionally, we examine diffusion model sampling strategies, leveraging their unique properties to ensure feasible and smooth closed-loop operation. As part of this work, we release a general software framework for data collection using optimal control policies in the photo-realistic simulator Isaac Lab. The framework includes multi-processing tools for CPU-based controllers and supports training and evaluating neural controllers, including diffusion models such as DDPM and traditional least-squares regression.
BibTeX
Empirical Inference Master Thesis Alien Recombination: Exploring Concept Blends Beyond Human Cognitive Availability in Visual Art Hernandez, A. Technical University of Munich, Germany, September 2024 (Published) BibTeX
Haptic Intelligence Master Thesis Estimating Contact Forces Across Soft Capacitive Tactile Sensors Using Machine Learning Tiwari, A. Saarland University, Saarbrücken, Germany, July 2024, M.Sc. in Embedded Systems (Published)
Robots have become an essential part of the modern world, playing a crucial role in applications from manufacturing to healthcare. Despite significant advancements, the operational range of robots remains relatively narrow, often limited to controlled environments and simple, predetermined tasks. Tactile sensors show promise in broadening this range by enhancing a robot's performance in fine manipulation tasks. These sensors enable robots to perceive contact, providing a more nuanced understanding of their environment in real time. The challenge, however, lies in deriving meaningful and interpretable insights from these sensors, such as contact location and force, which are crucial for dexterous manipulation tasks. To address this challenge, this thesis develops machine learning-based software that achieves precise real-time contact location and force sensing across the entire surface of a grid-based soft capacitive tactile sensor, enabling rapid and straightforward deployment and facilitating transferability to other sensor instances, all while retaining the advantageous attributes of capacitance technology. Machine learning models were trained using data captured by indenting the sensor surface and measuring the sensor responses and the applied normal forces. Convolutional neural networks (CNNs) were selected for their low prediction errors in contact force estimation with the collected dataset. Two distinct models were developed: one for estimating contact forces at a single point and another for estimating normal force distributions. The transferability of the trained models across different sensor instances was evaluated and improved. The single point contact force estimation model's practical utility was demonstrated through real-time closed-loop control of a Franka Emika Panda robot arm through two specific tasks: tactile servoing in 1D and active object centering in 2D. This research contributes to enhancing the accessibility of soft tactile sensors in robotic applications through machine learning and demonstrates that this approach can improve the capabilities of tactile sensors.
BibTeX
Empirical Inference Master Thesis Algorithmic Compositional Learning of Language Models Thomm, J. ETH Zurich, Switzerland, April 2024 (Published) BibTeX
Empirical Inference Master Thesis Interpreting How Large Language Models Handle Facts and Counterfactuals through Mechanistic Interpretability Ortu, F. University of Trieste, Italy, March 2024 (Published) BibTeX
Empirical Inference Master Thesis Denoising Representation Learning for Causal Discovery Sakenyte, U. Université de Genèva, Switzerland, December 2023, external supervision (Published) BibTeX
Empirical Inference Master Thesis Efficient Sampling from Differentiable Matrix Elements Kofler, A. Technical University of Munich, Germany, September 2023 (Published) BibTeX
Empirical Inference Master Thesis Intrinsic complexity and mechanisms of expressivity of cortical neurons Spieler, A. M. University of Tübingen, Germany, March 2023 (Published) BibTeX
Learning and Dynamical Systems Empirical Inference Master Thesis Causal Effect Estimation by Combining Observational and Interventional Data Kladny, K. ETH Zurich, Switzerland, February 2023 (Published) BibTeX
Empirical Inference Master Thesis Towards Generative Machine Teaching Qui, Z. Technical University of Munich, Germany, February 2023 (Published) BibTeX
Empirical Inference Master Thesis ArchiSound: Audio Generation with Diffusion Schneider, F. ETH Zurich, Switzerland, January 2023, external supervision (Published) BibTeX
Empirical Inference Master Thesis Generation and Quantification of Spin in Robot Table Tennis Dittrich, A. University of Stuttgart, Germany, January 2023 (Published) BibTeX
Empirical Inference Master Thesis Investigating Independent Mechanisms in Neural Networks Liang, W. Université Paris-Saclay, France, October 2022 (Published) BibTeX
Empirical Inference Master Thesis Modeling subgroup differences in fMRI data: disentangling subgroup-specific responses from shared ones Keidar, D. ETH Zurich, Switzerland, October 2022 (Published) BibTeX
Empirical Inference Master Thesis Independent Mechanism Analysis for High Dimensions Sliwa, J. University of Tübingen, Germany, September 2022, (Graduate Training Centre of Neuroscience) (Published) BibTeX
Empirical Inference Master Thesis Multi-Target Multi-Object Manipulation using Relational Deep Reinforcement Learning Feil, M. Technnical University Munich, Germany, September 2022 (Published) BibTeX
Empirical Inference Master Thesis On the Adversarial Robustness of Causal Algorithmic Recourse Dominguez-Olmedo, R. University of Tübingen, Germany, August 2022 (Published) BibTeX
Empirical Inference Master Thesis Independent Mechanism Analysis in High-Dimensional Observation Spaces Ghosh, S. ETH Zurich, Switzerland, June 2022 (Published) BibTeX
Empirical Inference Master Thesis Learning Neural Causal Models with Active Interventions Scherrer, N. ETH Zurich, Switzerland, November 2021 (Published) BibTeX
Empirical Inference Master Thesis HealthGen: Conditional Generation of Realistic Medical Time Series with Informative Missingness Bing, S. ETH Zurich, Switzerland, October 2021 (Published) BibTeX
Empirical Inference Master Thesis Study of the Interventional Consistency of Autoencoders Lanzillotta, G. ETH Zurich, Switzerland, October 2021 (Published) BibTeX
Empirical Inference Master Thesis Training with Few to Manipulate Many. On OOD generalization in relational reinforcement learning Mambelli, D. ETH Zurich, Switzerland, October 2021 (Published) BibTeX
Haptic Intelligence Master Thesis Robotic Surgery Training in AR: Multimodal Record and Replay Krauthausen, F. University of Stuttgart, Stuttgart, Germany, May 2021, Study Program in Software Engineering (Published) BibTeX
Modern Magnetic Systems Master Thesis Direct detection of spin Hall effect induced torques in platinum/ferromagnetic bilayer systems Alten, F. Universität Stuttgart, Stuttgart, January 2021 BibTeX
Physics for Inference and Optimization Master Thesis Community detection in heterogeneously attributed networks Zottino, N. Politecnico di Torino, 2021 (Published) URL BibTeX
Master Thesis Optimal allocation of attention in a signal detection task Chebolu, S. Indian Institute of Science Education and Research, Pune, 2021 BibTeX
Modern Magnetic Systems Master Thesis Hydromagnonics: Manipulation of magnonic systems with hydrogen Sauter, R. Universität Stuttgart, Stuttgart, December 2020 BibTeX
Empirical Inference Master Thesis A Comprehensive Benchmark Evaluation of Synthetic Data Generation for Biomedical Imaging DuMont Schütte, A. ETH Zurich, Switzerland, October 2020 (Published) BibTeX
Empirical Inference Master Thesis A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning Ahmed, O. ETH Zurich, Switzerland, October 2020 (Published) BibTeX
Empirical Inference Master Thesis Deep learning for the parameter estimation of tight-binding Hamiltonians Cacioppo, A. University of Roma, La Sapienza, Italy, May 2020 (Published) BibTeX
Empirical Inference Master Thesis Learning Algorithms, Invariances, and the Real World Zecevic, M. Technical University of Darmstadt, Germany, April 2020 (Published) BibTeX
Modern Magnetic Systems Master Thesis Interaction of hydrogen isotopes with flexible metal-organic frameworks Bondorf, L. Universität Stuttgart, Stuttgart, February 2020 BibTeX
Micro, Nano, and Molecular Systems Master Thesis Colloidal particles supporting urase activity Baldauf, A. Univ. of Stuttgart, 2020 BibTeX
Physics for Inference and Optimization Master Thesis Developing new methods for routing and optimal transport on networks Lonardi, A. Università degli studi di Padova, 2020 pdf BibTeX
Micro, Nano, and Molecular Systems Master Thesis Diffusion studies on biomolecules by NMR Bochert, I. Univ. of Stuttgart, 2020 BibTeX
Physics for Inference and Optimization Master Thesis Edge-Disjoint Path Problem on Stochastic Block Models through Message Passing Ferretti, L. Sapienza Università di Roma, 2020 BibTeX
Physics for Inference and Optimization Master Thesis Analysis and modelling of information ecosystems Pigani, E. Università degli studi di Padova, October 2019 URL BibTeX
Empirical Inference Master Thesis Inferring the Band Structure from Band Mapping Data through Machine Learning Stimper, V. Technical University of Munich, September 2019 (Published) BibTeX
Physics for Inference and Optimization Master Thesis A new approach for community detection in multilayer networks Contisciani, M. Università degli studi di Padova, September 2019 URL BibTeX
Empirical Inference Master Thesis Learning to Diagnose Diabetes from Magnetic Resonance Tomography Dietz, B. ETH Zurich, Switzerland, August 2019 (Published) BibTeX
Empirical Inference Master Thesis Reinforcement Learning for a Two-Robot Table Tennis Simulation Li, G. RWTH Aachen University, Germany, July 2019 (Published) BibTeX
Empirical Inference Master Thesis Characteristics of longitudinal physiological measurements of late-stage ALS patients Konieczny, L. Ludwig-Maximilians-Universität München, Germany, May 2019 (Published) BibTeX
Micro, Nano, and Molecular Systems Master Thesis Active matter and self propelled microparticles Kottapalli, S. N. M. Univ. of Stuttgart, 2019 BibTeX
Physics for Inference and Optimization Master Thesis Dynamic hierarchical ranking in directed networks: from static SpringRank to dynamic and bayesian models using variational techniques Vecchia, A. D. Università degli studi di Padova, 2019 pdf BibTeX
Dynamic Locomotion Master Thesis Electronics, Software and Analysis of a Bioinspired Sensorized Quadrupedal Robot Petereit, R. Technische Universität München, 2019 BibTeX
Dynamic Locomotion Master Thesis Entwicklung und Analyse neuartiger fluidischer Aktoren mit Rollmembran Hepp, J. Technische Universität München, 2019 BibTeX
Haptic Intelligence Master Thesis Haptic Reality: Novel Interfacing for Informed Assembly Systems Tashiro, N., Faulkner, R., Melnyk, S., Rosales, T. University of Stuttgart, Stuttgart, Germany, 2019 (Published) BibTeX
Empirical Inference Master Thesis Spatial Filtering based on Riemannian Manifold for Brain-Computer Interfacing Xu, J. Technical University of Munich, Germany, 2019 (Published) BibTeX
Physics for Inference and Optimization Master Thesis Variational Inference: Recent Advances and Applications to Dynamic Ranking on Graphs Ruggeri, N. Università degli studi di Padova, 2019 pdf BibTeX