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Haptic Intelligence Miscellaneous Minsound: Adding Internal Audio Sensing to Internal Vision Enables Human-Like In-Hand Fabric Recognition with Soft Robotic Fingertips Andrussow, I., Solano, J., Richardson, B. A., Martius, G., Kuchenbecker, K. J. Extended abstract (3 pages) to be presented at the German Robotics Conference (GRC), Cologne, Germany, March 2026 (Accepted) BibTeX
Haptic Intelligence Miscellaneous Rendering Forces with a Modular Cable System, Motors, and Brakes Bartels, J. U., Achberger, A., Kuchenbecker, K. J., Sedlmair, M. Extended abstract (3 pages) to be presented at the German Robotics Conference (GRC), Cologne, Germany, March 2026 (Accepted)
We describe the hardware design, force-rendering approach, and evaluation of a new reconfigurable haptic interface consisting of a network of hybrid motor-brake actuation modules that apply forces via cables. Each module contains both a motor and a brake, enabling it to smoothly render active forces up to 6 N using its motor and collision forces up to 186 N using its passive one-way brake. The modular design, meanwhile, allows the system to deliver rich haptic feedback in a flexible number of DoF and widely ranging configurations.
BibTeX
Social Foundations of Computation Miscellaneous Scaling Open-Ended Reasoning To Predict the Future Chandak, N., Shashwat, G., Prabhu, A., Hardt, M., Geiping, J. January 2026 (Submitted)
High-stakes decision making involves reasoning under uncertainty about the future. In this work, we train language models to make predictions on open-ended forecasting questions. To scale up training data, we synthesize novel forecasting questions from global events reported in daily news, using a fully automated, careful curation recipe. We train the Qwen3 thinking models on our dataset, OpenForesight. To prevent leakage of future information during training and evaluation, we use an offline news corpus, both for data generation and retrieval in our forecasting system. Guided by a small validation set, we show the benefits of retrieval, and an improved reward function for reinforcement learning (RL). Once we obtain our final forecasting system, we perform held-out testing between May to August 2025. Our specialized model, OpenForecaster 8B, matches much larger proprietary models, with our training improving the accuracy, calibration, and consistency of predictions. We find calibration improvements from forecasting training generalize across popular benchmarks. We open-source all our models, code, and data to make research on language model forecasting broadly accessible.
arXiv BibTeX
Social Foundations of Computation Miscellaneous Policy Design in Long-run Welfare Dynamics Wu, J., Abebe, R., Hardt, M., Stoica, A. Proceedings of the Fifth ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO), November 2025 (Published) URL BibTeX
Haptic Intelligence Miscellaneous The Benefits of Gait Retraining with Vibrotactile Feedback Outweigh Higher Perceived Mental Load Sundaram, V. H., Rokhmanova, N., Halilaj, E., Kuchenbecker, K. J. Extended abstract (1 page) presented at the American Society of Biomechanics Annual Meeting (ASB), Pittsburgh, USA, August 2025 (Published)
Knee osteoarthritis (KOA) affects millions worldwide, with excessive joint loading linked to disease progression. Modifying the foot progression angle (FPA) while walking is one strategy to reduce knee adduction moments, a measure associated with medial knee joint loading. This study investigated whether two types of vibrotactile biofeedback during a 20-minute treadmill gait-retraining session helped healthy adults better learn and retain a 10°toe-in gait. Participants who received feedback showed greater improvements in FPA accuracy than those without feedback and also reported significantly higher mental effort. The type of feedback that scaled the duration of the vibration with the magnitude of the error led to better short-term retention than no feedback, and it was also preferred by almost all subjects over constant-duration cues. These findings suggest that despite the added cognitive demand, users value biofeedback, emphasizing the need to design gait-retraining tools that consider both learning effectiveness and user experience.
BibTeX
Haptic Intelligence Miscellaneous A DNN-Based Metamodel for Simulating Fingertip Deformation Deshmukh, Y., Kuchenbecker, K. J., Serhat, G. Work-in-progress paper (2 pages) presented at the IEEE World Haptics Conference (WHC), Suwon, South Korea, July 2025 (Published) BibTeX
Social Foundations of Computation Miscellaneous Answer Matching Outperforms Multiple Choice for Language Model Evaluation Chandak, N., Goel, S., Prabhu, A., Hardt, M., Geiping, J. July 2025 (Submitted)
Multiple choice benchmarks have long been the workhorse of language model evaluation because grading multiple choice is objective and easy to automate. However, we show multiple choice questions from popular benchmarks can often be answered without even seeing the question. These shortcuts arise from a fundamental limitation of discriminative evaluation not shared by evaluations of the model's free-form, generative answers. Until recently, there appeared to be no viable, scalable alternative to multiple choice--but, we show that this has changed. We consider generative evaluation via what we call answer matching: Give the candidate model the question without the options, have it generate a free-form response, then use a modern language model with the reference answer to determine if the response matches the reference. To compare the validity of different evaluation strategies, we annotate MMLU-Pro and GPQA-Diamond to obtain human grading data, and measure the agreement of each evaluation approach. We find answer matching using recent models--even small ones--achieves near-perfect agreement, in the range of inter-annotator agreement. In contrast, both multiple choice evaluation and using LLM-as-a-judge without reference answers aligns poorly with human grading. Improving evaluations via answer matching is not merely a conceptual concern: the rankings of several models change significantly when evaluating their free-form responses with answer matching. In light of these findings, we discuss how to move the evaluation ecosystem from multiple choice to answer matching.
arXiv BibTeX
Haptic Intelligence Robotic Materials Miscellaneous Learning-Based Touch Detection and Force Estimation in Cutaneous Electrohydraulic Devices Sanchez-Tamayo, N., Singer, D., Keplinger, C., Kuchenbecker, K. J. Work-in-progress paper (2 pages) presented at the IEEE World Haptics Conference (WHC), Suwon, South Korea, July 2025 (Published) BibTeX
Haptic Intelligence Miscellaneous Perception of Diverse Asymmetric Vibration Signals Tashiro, N., Ballardini, G., Nunez, C. M., Vardar, Y., Kuchenbecker, K. J. Work-in-progress paper (2 pages) presented at the IEEE World Haptics Conference (WHC), Suwon, South Korea, July 2025 (Published) BibTeX
Haptic Intelligence Miscellaneous Quantifying Texture-Rendering Quality Across Haptic Devices Fazlollahi, F., Seifi, H., Ballardini, G., Taghizadeh, Z., Schulz, A. K., MacLean, K. E., Kuchenbecker, K. J. Work-in-progress paper (2 pages) presented at the IEEE World Haptics Conference (WHC), Suwon, South Korea, July 2025 (Published) BibTeX
Haptic Intelligence Robotics Miscellaneous Soft Magnetic Fingertip Devices for Clear Vibrotactile Feedback Gertler, I., Ballardini, G., Grüninger, F., Kuchenbecker, K. J. Hands-on demonstration presented at the IEEE World Haptics Conference (WHC), Suwon, South Korea, July 2025 (Published) BibTeX
Haptic Intelligence Miscellaneous Watch, Feel, Do: Investigating the Impact of Naturalistic Vibrotactile Feedback and Active Imitation on Manual Skill Learning Gourishetti, R., Hughes, A. G., Javot, B., Kuchenbecker, K. J. Work-in-progress paper (2 pages) presented at the IEEE World Haptics Conference (WHC), Suwon, South Korea, July 2025 (Published) BibTeX
Haptic Intelligence Miscellaneous Whole-Arm Humanoid Robot Teleoperation with Naturalistic Vibrotactile Feedback Gong, Y., Hudhud Mughrabi, M., L’Orsa, R., Mohan, M., Kuchenbecker, K. J. Work-in-progress paper (2 pages) presented at the IEEE World Haptics Conference (WHC), Suwon, South Korea, July 2025 (Published) BibTeX
Haptic Intelligence Robotics Robotic Materials Miscellaneous Wrist-Worn Circumferential Pressure Feedback for Conveying Directional Forces in Virtual Reality Kadmani, A., Bartels, J. U., Sanchez-Tamayo, N., Gasparetto, W., Kuchenbecker, K. J. Work-in-progress paper (2 pages) presented at the IEEE World Haptics Conference (WHC), Suwon, South Korea, July 2025 (Published) BibTeX
Haptic Intelligence Optics and Sensing Laboratory Miscellaneous Open-Source Multi-Viewpoint Surgical Telerobotics Caccianiga, G., Sharon, Y., Javot, B., Polikovsky, S., Ergün, G., Capobianco, I., Mihaljevic, A. L., Deguet, A., Kuchenbecker, K. J. Extended abstract (2 pages) presented at the ICRA Workshop on Robot-Assisted Medical Imaging (ICRA-RAMI), Atlanta, USA, May 2025 (Published) URL BibTeX
Autonomous Learning Miscellaneous Emergence of natural and robust bipedal walking by learning from biologically plausible objectives Schumacher, P., Geijtenbeek, T., Caggiano, V., Kumar, V., Schmitt, S., Martius, G., Haeufle, D. F. iScience, 28(4):112203, April 2025 (Published)
Humans show unparalleled ability when maneuvering diverse terrains. While reinforcement learning (RL) has shown great promise for musculoskeletal simulation in the development of robust controllers, complex behaviors are only achievable under extensive use of motion data. We demonstrate that the combination of a recent RL algorithm with a biologically plausible reward is capable of learning controllers for 4 different musculoskeletal models and achieves locomotion with up to 90 muscles without demonstrations. Our controllers generalize to diverse and unseen terrains, while only a single adaptive objective function is needed for training. We validate our findings on four models in two different simulators. The RL agents perform robustly with complex 3D models, where reflex-controllers are difficult to apply, and produce close-to-natural motion. This is a first step for the motor control, biomechanics, and rehabilitation communities to generate complex human movements with RL, without using motion data or simple unrepresentative models.
DOI URL BibTeX
Haptic Intelligence Miscellaneous A Method for Single-Input Sequencing of Hyperelastic Balloons Gertler, I., Kuchenbecker, K. J. Extended abstract (3 pages) presented at the IEEE-RAS International Conference on Soft Robotics (RoboSoft), Lausanne, Switzerland, April 2025 (Published)
This study demonstrates that encasing a hyperelastic balloon in an inextensible sleeve greatly increases its burst pressure while not influencing its minimum pressure. This simple mechanical behavior can be used to produce an asymmetric inflation-deflation sequence for coupled balloons with different thicknesses so they could serve as a soft robot's rear and front anchors when driven from a single fluid supply.
BibTeX
Haptic Intelligence Robotics Miscellaneous Bio-Inspired Gradient (BIG) Whiskers: Stiffness-Shifting Structures Provide Dynamic Functional Benefits for Contact Sensing Schulz, A. K., Andrussow, I., Farsijani, F., Faulkner, R., Kuchenbecker, K. J. Extended abstract (3 pages) presented at the IEEE-RAS International Conference on Soft Robotics (RoboSoft), Lausanne, Switzerland, April 2025 (Published)
Mammal whiskers have inspired many sensors that can help robots find obstacles, identify textures, or sense flow. Though they vary in geometry, past bio-inspired whisker sensors were primarily constructed from homogenous materials. Interestingly, animal whiskers tend to shift from a stiff root to a much softer point; this material stiffness gradient is hypothesized to provide functional benefits such as reduction of wear and amplification of contact sensations. We take inspiration from nature to fabricate bio-inspired gradient (BIG) whiskers via 3D printing, and we assess their performance compared to stiff, medium, and soft homogenous artificial whiskers with the same geometry. Tests with controlled quasi-static and dynamic perturbations allow us to measure the whisker point deflection and the reaction torque at the stationary whisker root, respectively. The dynamic results reveal that BIG whiskers uniquely encode contact location along their length through torque magnitude and frequency, features that are not seen in the homogenous whiskers. These exciting preliminary findings motivate further exploration of robotic whiskers and other sensing structures with bio-inspired stiffness gradients.
BibTeX
Haptic Intelligence Miscellaneous Error-State Extended Kalman Filter Sensor Fusion for Tracking Collaborating Humans Hudhud Mughrabi, M., Allemang–Trivalle, A., Kuchenbecker, K. J. Extended abstract (3 pages) presented at the German Robotics Conference (GRC), Nuremberg, Germany, March 2025 (Published)
How teams collaborate to perform complex tasks , from team sports to surgical procedures, has previously been investigated via multimodal sensing and analysis. Ultra-wideband (UWB) positioning systems are highly mobile and can be used to track collaborating team members even in cramped environments. However, the sampling rate of UWB systems is inversely proportional to the number of people tracked, and their accuracy is hindered by electromagnetic occlusion. To improve position and orientation estimation during team collaborative studies, we propose to fuse UWB positioning with a wearable inertial measurement unit (IMU) by applying an error-state extended Kalman filter (ES-EKF). This filter offers faster and more consistent estimation and remains functional even in the absence of UWB input. Single-human and multi-human sessions were recorded and filtered for evaluation against ground truth from optical motion capture. By integrating IMU readings, the ES-EKF increases the sampling rate from 0.5-20 Hz to 100 Hz. Even by correcting only planar position in the room, the ES-EKF yields improved results over UWB in four out of six DOF: lateral and longitudinal position and yaw and pitch orientation.
BibTeX
Haptic Intelligence Miscellaneous Haptify: A Measurement System for Benchmarking Grounded Force-Feedback Devices Fazlollahi, F., Kuchenbecker, K. J. Extended abstract (3 pages) presented at the German Robotics Conference (GRC), Nuremberg, Germany , March 2025 (Published)
Grounded force-feedback (GFF) devices are a well-established and diverse category of haptic technology based on robotic arms. However, the number of designs and their specifications make it challenging to compare devices effectively. We address this challenge by presenting Haptify, a benchmarking system capable of evaluating GFF haptic devices in a thorough, fair, and non-invasive way. The user holds the instrumented device end-effector and moves it through a series of passive and active experiments. Haptify captures the interaction between the hand, device, and ground using a seven-camera optical motion-capture system, a custom 60-cm-square force plate, and a customized sensing end-effector. We propose six key metrics for evaluating GFF device performance: workspace shape, global free-space forces, global free-space vibrations, local dynamic forces and torques, frictionless surface rendering, and stiffness rendering. We then benchmark two commercial haptic devices using Haptify. The more expensive Touch X has a smaller workspace than the 3D Systems Touch, but it outputs smaller free-space forces and vibrations, smaller and more predictable dynamic forces and torques, and higher-quality renderings of a frictionless surface and high stiffness.
BibTeX
Social Foundations of Computation Miscellaneous Training on the Test Task Confounds Evaluation and Emergence Dominguez-Olmedo, R., Dorner, F. E., Hardt, M. The Thirteenth International Conference on Learning Representations (ICLR 2025), January 2025 (Accepted)
We study a fundamental problem in the evaluation of large language models that we call training on the test task. Unlike wrongful practices like training on the test data, leakage, or data contamination, training on the test task is not malpractice. Rather, the term describes a growing set of techniques to include task-relevant data in the pretraining stage of a language model. We demonstrate that training on the test task confounds both relative model evaluations and claims about emergent capabilities. We argue that the seeming superiority of one model family over another may be explained by a different degree of training on the test task. To this end, we propose an effective method to adjust for training on the test task by fine-tuning each model under comparison on the same task-relevant data before evaluation. We then show that instances of emergent behavior largely vanish once we adjust for training on the test task. This also applies to reported instances of emergent behavior that cannot be explained by the choice of evaluation metric. Our work promotes a new perspective on the evaluation of large language models with broad implications for benchmarking and the study of emergent capabilities.
ArXiv BibTeX
Haptic Intelligence Autonomous Learning Empirical Inference Miscellaneous Demonstration: Minsight - A Soft Vision-Based Tactile Sensor for Robotic Fingertips Andrussow, I., Sun, H., Martius, G., Kuchenbecker, K. J. Hands-on demonstration presented at the Conference on Robot Learning (CoRL), Munich, Germany, November 2024 (Published)
Beyond vision and hearing, tactile sensing enhances a robot's ability to dexterously manipulate unfamiliar objects and safely interact with humans. Giving touch sensitivity to robots requires compact, robust, affordable, and efficient hardware designs, especially for high-resolution tactile sensing. We present a soft vision-based tactile sensor engineered to meet these requirements. Comparable in size to a human fingertip, Minsight uses machine learning to output high-resolution directional contact force distributions at 60 Hz. Minsight's tactile force maps enable precise sensing of fingertip contacts, which we use in this hands-on demonstration to allow a 3-DoF robot arm to physically track contact with a user's finger. While observing the colorful image captured by Minsight's internal camera, attendees can experience how its ability to detect delicate touches in all directions facilitates real-time robot interaction.
BibTeX
Haptic Intelligence Miscellaneous Demonstration: OCRA - A Kinematic Retargeting Algorithm for Expressive Whole-Arm Teleoperation Mohan, M., Kuchenbecker, K. J. Hands-on demonstration presented at the Conference on Robot Learning (CoRL), Munich, Germany, November 2024 (Published)
Traditional teleoperation systems focus on controlling the pose of the end-effector (task space), often neglecting the additional degrees of freedom present in human and many robotic arms. This demonstration presents the Optimization-based Customizable Retargeting Algorithm (OCRA), which was designed to map motions from one serial kinematic chain to another in real time. OCRA is versatile, accommodating any robot joint counts and segment lengths, and it can retarget motions from human arms to kinematically different serial robot arms with revolute joints both expressively and efficiently. One of OCRA's key features is its customizability, allowing the user to adjust the emphasis between hand orientation error and the configuration error of the arm's central line, which we call the arm skeleton. To evaluate the perceptual quality of the motions generated by OCRA, we conducted a video-watching study with 70 participants; the results indicated that the algorithm produces robot motions that closely resemble human movements, with a median rating of 78/100, particularly when the arm skeleton error weight and hand orientation error are balanced. In this demonstration, the presenter will wear an Xsens MVN Link and teleoperate the arms of a NAO child-size humanoid robot to highlight OCRA's ability to create intuitive and human-like whole-arm motions.
BibTeX
Haptic Intelligence Robotic Materials Miscellaneous Active Haptic Feedback for a Virtual Wrist-Anchored User Interface Bartels, J. U., Sanchez-Tamayo, N., Sedlmair, M., Kuchenbecker, K. J. Adjunct Proceedings of the Annual ACM Symposium on User Interface Software and Technology (UIST), (53)1-3, Hands-on demonstration presented at the Annual ACM Symposium on User Interface Software and Technology (UIST), Pittsburgh, USA, October 2024 (Published)
The presented system combines a virtual wrist-anchored user interface (UI) with a new low-profle, wrist-worn device that provides salient and expressive haptic feedback such as contact, pressure and broad-bandwidth vibration. This active feedback is used to add tactile cues to interactions with virtual mid-air UI elements that track the user's wrist; we demonstrate a simple menu-interaction task to showcase the utility of haptics for interactions with virtual buttons and sliders. Moving forward, we intend to use this platform to develop haptic guidelines for body-anchored interfaces and test multiple haptic devices across the body to create engaging interactions.
DOI BibTeX
Autonomous Learning Miscellaneous Directed Exploration in Reinforcement Learning from Linear Temporal Logic Bagatella, M., Krause, A., Martius, G. August 2024 (In revision)
Linear temporal logic (LTL) is a powerful language for task specification in reinforcement learning, as it allows describing objectives beyond the expressivity of conventional discounted return formulations. Nonetheless, recent works have shown that LTL formulas can be translated into a variable rewarding and discounting scheme, whose optimization produces a policy maximizing a lower bound on the probability of formula satisfaction. However, the synthesized reward signal remains fundamentally sparse, making exploration challenging. We aim to overcome this limitation, which can prevent current algorithms from scaling beyond low-dimensional, short-horizon problems. We show how better exploration can be achieved by further leveraging the LTL specification and casting its corresponding Limit Deterministic Büchi Automaton (LDBA) as a Markov reward process, thus enabling a form of high-level value estimation. By taking a Bayesian perspective over LDBA dynamics and proposing a suitable prior distribution, we show that the values estimated through this procedure can be treated as a shaping potential and mapped to informative intrinsic rewards. Empirically, we demonstrate applications of our method from tabular settings to high-dimensional continuous systems, which have so far represented a significant challenge for LTL-based reinforcement learning algorithms.
URL BibTeX
Haptic Intelligence Miscellaneous Adapting a High-Fidelity Simulation of Human Skin for Comparative Touch Sensing Schulz, A., Serhat, G., Kuchenbecker, K. J. Extended abstract (1 page) presented at the American Society of Biomechanics Annual Meeting (ASB), Madison, USA, August 2024 (Published) BibTeX
Haptic Intelligence Robotics Miscellaneous Modeling Shank Tissue Properties and Quantifying Body Composition with a Wearable Actuator-Accelerometer Set Rokhmanova, N., Martus, J., Faulkner, R., Fiene, J., Kuchenbecker, K. J. Extended abstract (1 page) presented at the American Society of Biomechanics Annual Meeting (ASB), Madison, USA, August 2024 (Published) BibTeX
Haptic Intelligence Robotics Miscellaneous GaitGuide: A Wearable Device for Vibrotactile Motion Guidance Rokhmanova, N., Martus, J., Faulkner, R., Fiene, J., Kuchenbecker, K. J. Workshop paper (3 pages) presented at the ICRA Workshop on Advancing Wearable Devices and Applications Through Novel Design, Sensing, Actuation, and AI, Yokohama, Japan, May 2024 (Published)
Wearable vibrotactile devices can provide salient sensations that attract the user's attention or guide them to change. The future integration of such feedback into medical or consumer devices would benefit from understanding how vibrotactile cues vary in amplitude and perceived strength across the heterogeneity of human skin. Here, we developed an adhesive vibrotactile device (the GaitGuide) that uses two individually mounted linear resonant actuators to deliver directional motion guidance. By measuring the mechanical vibrations of the actuators via small on-board accelerometers, we compared vibration amplitudes and perceived signal strength across 20 subjects at five signal voltages and four sites around the shank. Vibrations were consistently smallest in amplitude—but perceived to be strongest—at the site located over the tibia. We created a fourth-order linear dynamic model to capture differences in tissue properties across subjects and sites via optimized stiffness and damping parameters. The anterior site had significantly higher skin stiffness and damping; these values also correlate with subject-specific body-fat percentages. Surprisingly, our study shows that the perception of vibrotactile stimuli does not solely depend on the vibration magnitude delivered to the skin. These findings also help to explain the clinical practice of evaluating vibrotactile sensitivity over a bony prominence.
URL BibTeX
Haptic Intelligence Robotic Materials Miscellaneous Three-Dimensional Surface Reconstruction of a Soft System via Distributed Magnetic Sensing Sundaram, V. H., Smith, L., Turin, Z., Rentschler, M. E., Gonzalez Welker, C. Workshop paper (3 pages) presented at the ICRA Workshop on Advancing Wearable Devices and Applications Through Novel Design, Sensing, Actuation, and AI, Yokohama, Japan, May 2024 (Published)
This study presents a new method for reconstructing continuous 3D surface deformations for a soft pneumatic actuation system using embedded magnetic sensors. A finite element analysis (FEA) model was developed to quantify the surface deformation given the magnetometer readings, with a relative error between the experimental and the simulated sensor data of 7.8%. Using the FEA simulation solutions and a basic model-based mapping, our method achieves sub-millimeter accuracy in measuring deformation from sensor data with an absolute error between the experimental and simulated sensor data of 13.5%. These results show promise for real-time adjustments to deformation, crucial in environments like prosthetic and orthotic interfaces with human limbs.
URL BibTeX
Haptic Intelligence Robotic Materials Miscellaneous Cutaneous Electrohydraulic (CUTE) Wearable Devices for Multimodal Haptic Feedback Sanchez-Tamayo, N., Yoder, Z., Ballardini, G., Rothemund, P., Keplinger, C., Kuchenbecker, K. J. Extended abstract (1 page) presented at the IEEE RoboSoft Workshop on Multimodal Soft Robots for Multifunctional Manipulation, Locomotion, and Human-Machine Interaction, San Diego, USA, April 2024 (Published) BibTeX
Haptic Intelligence Robotic Materials Miscellaneous Cutaneous Electrohydraulic Wearable Devices for Expressive and Salient Haptic Feedback Sanchez-Tamayo, N., Yoder, Z., Ballardini, G., Rothemund, P., Keplinger, C., Kuchenbecker, K. J. Hands-on demonstration presented at the IEEE Haptics Symposium, Long Beach, USA, April 2024 (Published) BibTeX
Haptic Intelligence Robotic Materials Miscellaneous Demonstration: Cutaneous Electrohydraulic (CUTE) Wearable Devices for Expressive and Salient Haptic Feedback Sanchez-Tamayo, N., Yoder, Z., Ballardini, G., Rothemund, P., Keplinger, C., Kuchenbecker, K. J. Hands-on demonstration presented at the IEEE RoboSoft Conference, San Diego, USA, April 2024 (Published) BibTeX
Empirical Inference Miscellaneous Evidence for eccentricity in the population of binary black holes observed by LIGO-Virgo-KAGRA Gupte, N., Ramos-Buades, A., Buonanno, A., Gair, J., Miller, M. C., Dax, M., Green, S. R., Pürrer, M., Wildberger, J., Macke, J. H., Romero-Shaw, I. M., Schölkopf, B. April 2024 (Published) URL BibTeX
Haptic Intelligence Miscellaneous Quantifying Haptic Quality: External Measurements Match Expert Assessments of Stiffness Rendering Across Devices Fazlollahi, F., Seifi, H., Ballardini, G., Taghizadeh, Z., Schulz, A., MacLean, K. E., Kuchenbecker, K. J. Work-in-progress paper (2 pages) presented at the IEEE Haptics Symposium, Long Beach, USA, April 2024 (Published) URL BibTeX
Haptic Intelligence Miscellaneous CAPT Motor: A Strong Direct-Drive Rotary Haptic Interface Javot, B., Nguyen, V. H., Ballardini, G., Kuchenbecker, K. J. Hands-on demonstration presented at the IEEE Haptics Symposium, Long Beach, USA, April 2024 (Published)
We have designed and built a new motor named CAPT Motor that delivers continuous and precise torque. It is a brushless ironless motor using a Halbach-magnet ring and a planar axial Lorentz-coil array. This motor is unique as we use a two-phase design allowing for higher fill factor and geometrical accuracy of the coils, as they can all be made separately. This motor outperforms existing Halbach ring and cylinder motors with a torque constant per magnet volume of 9.94 (Nm/A)/dm3, a record in the field. The angular position of the rotor is measured by a high-resolution incremental optical encoder and tracked by a multimodal data acquisition device. The system's control firmware uses this angle measurement to calculate the two-phase motor currents needed to produce the torque commanded by the virtual environment at the rotor's position. The strength and precision of the CAPT Motor's torque and the lack of any mechanical transmission enable unusually high haptic rendering quality, indicating the promise of this new motor design.
URL BibTeX
Haptic Intelligence Miscellaneous Adapting a High-Fidelity Simulation of Human Skin for Comparative Touch Sensing in the Elephant Trunk Schulz, A., Serhat, G., Kuchenbecker, K. J. 64(Supplement_1):S458-S459, Abstract presented at the Society for Integrative and Comparative Biology Annual Meeting (SICB), Seattle, USA, January 2024 (Published)
Skin is a complex biological composite consisting of layers with distinct mechanical properties, morphologies, and mechanosensory capabilities. This work seeks to expand the comparative biomechanics field to comparative haptics, analyzing elephant trunk touch by redesigning a previously published human finger-pad model with morphological parameters measured from an elephant trunk. The dorsal surface of the elephant trunk has a thick, wrinkled epidermis covered with whiskers at the distal tip and deep folds at the proximal base. We hypothesize that this thick dorsal skin protects the trunk from mechanical damage but significantly dulls its tactile sensing ability. To facilitate safe and dexterous motion, the distributed dorsal whiskers might serve as pre-touch antennae, transmitting an amplified version of impending contact to the mechanoreceptors beneath the elephant's armor. We tested these hypotheses by simulating soft tissue deformation through high-fidelity finite element analyses involving representative skin layers and whiskers, modeled based on frozen African elephant trunk (Loxodonta africana) morphology. For a typical contact force, quintupling the stratum corneum thickness to match dorsal trunk skin reduces the von Mises stress communicated to the dermis by 18%. However, adding a whisker offsets this dulled sensing, as hypothesized, amplifying the stress by more than 15 at the same location. We hope this work will motivate further investigations of mammalian touch using approaches and models from the ample literature on human touch.
DOI BibTeX
Haptic Intelligence Materials Miscellaneous Whiskers That Don’t Whisk: Unique Structure From the Absence of Actuation in Elephant Whiskers Schulz, A., Kaufmann, L., Brecht, M., Richter, G., Kuchenbecker, K. J. 64(Supplement_1):S459, Abstract presented at the Society for Integrative and Comparative Biology Annual Meeting (SICB), Seattle, USA, January 2024 (Published)
Whiskers are so named because these hairs often actuate circularly, whisking, via collagen wrapping at the root of the hair follicle to increase their sensing volumes. Elephant trunks are a unique case study for whiskers, as the dorsal and lateral sections of the elephant proboscis have scattered sensory hairs that lack individual actuation. We hypothesize that the actuation limitations of these non-whisking whiskers led to anisotropic morphology and non-homogeneous composition to meet the animal's sensory needs. To test these hypotheses, we examined trunk whiskers from a 35-year-old female African savannah elephant (Loxodonta africana). Whisker morphology was evaluated through micro-CT and polarized light microscopy. The whiskers from the distal tip of the trunk were found to be axially asymmetric, with an ovular cross-section at the root, shifting to a near-square cross-section at the point. Nanoindentation and additional microscopy revealed that elephant whiskers have a composition unlike any other mammalian hair ever studied: we recorded an elastic modulus of 3 GPa at the root and 0.05 GPa at the point of a single 4-cm-long whisker. This work challenges the assumption that hairs have circular cross-sections and isotropic mechanical properties. With such striking differences compared to other mammals, including the mouse (Mus musculus), rat (Rattus norvegicus), and cat (Felis catus), we conclude that whisker morphology and composition play distinct and complementary roles in elephant trunk mechanosensing.
DOI BibTeX
Haptic Intelligence Miscellaneous MPI-10: Haptic-Auditory Measurements from Tool-Surface Interactions Khojasteh, B., Shao, Y., Kuchenbecker, K. J. Dataset published as a companion to the journal article "Robust Surface Recognition with the Maximum Mean Discrepancy: Degrading Haptic-Auditory Signals through Bandwidth and Noise" in IEEE Transactions on Haptics, January 2024 (Published) DOI BibTeX
Haptic Intelligence Miscellaneous Discrete Fourier Transform Three-to-One (DFT321): Code Landin, N., Romano, J. M., McMahan, W., Kuchenbecker, K. J. MATLAB code of discrete fourier transform three-to-one (DFT321), 2024 (Published) Code BibTeX
Empirical Inference Miscellaneous Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models Rajendran, G., Buchholz, S., Aragam, B., Schölkopf, B., Ravikumar, P. 2024 (Published) arXiv BibTeX
Empirical Inference Miscellaneous Multi-Armed Bandits and Quantum Channel Oracles Buchholz, S., Kübler, J. M., Schölkopf, B. 2024 (Published) arXiv BibTeX
Empirical Inference Miscellaneous Analyzing Human Questioning Behavior and Causal Curiosity through Natural Queries Ceraolo, R., Kharlapenko, D., Khan, A., Reymond, A., Mihalcea, R., Sachan, M., Schölkopf, B., Jin, Z. 2024 (Published) URL BibTeX
Empirical Inference Miscellaneous Language Model Alignment in Multilingual Trolley Problems Jin, Z., Levine, S., Kleiman-Weiner, M., Piatti, G., Liu, J., Gonzalez, F., Ortu, F., Strausz, A., Sachan, M., Mihalcea, R., Choi, Y., Schölkopf, B. 2024 (Published) URL BibTeX
Haptic Intelligence Miscellaneous Seeking Causal, Invariant, Structures with Kernel Mean Embeddings in Haptic-Auditory Data from Tool-Surface Interaction Khojasteh, B., Shao, Y., Kuchenbecker, K. J. Workshop paper (4 pages) presented at the IROS Workshop on Causality for Robotics: Answering the Question of Why, Detroit, USA, October 2023 (Published)
Causal inference could give future learning robots strong generalization and scalability capabilities, which are crucial for safety, fault diagnosis and error prevention. One application area of interest consists of the haptic recognition of surfaces. We seek to understand cause and effect during physical surface interaction by examining surface and tool identity, their interplay, and other contact-irrelevant factors. To work toward elucidating the mechanism of surface encoding, we attempt to recognize surfaces from haptic-auditory data captured by previously unseen hemispherical steel tools that differ from the recording tool in diameter and mass. In this context, we leverage ideas from kernel methods to quantify surface similarity through descriptive differences in signal distributions. We find that the effect of the tool is significantly present in higher-order statistical moments of contact data: aligning the means of the distributions being compared somewhat improves recognition but does not fully separate tool identity from surface identity. Our findings shed light on salient aspects of haptic-auditory data from tool-surface interaction and highlight the challenges involved in generalizing artificial surface discrimination capabilities.
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Haptic Intelligence Miscellaneous NearContact: Accurate Human Detection using Tomographic Proximity and Contact Sensing with Cross-Modal Attention Garrofé, G., Schoeffmann, C., Zangl, H., Kuchenbecker, K. J., Lee, H. Extended abstract (4 pages) presented at the International Workshop on Human-Friendly Robotics (HFR), Munich, Germany, September 2023 (Published) BibTeX
Haptic Intelligence Miscellaneous The Role of Kinematics Estimation Accuracy in Learning with Wearable Haptics Rokhmanova, N., Pearl, O., Kuchenbecker, K. J., Halilaj, E. Abstract (1 page) presented at the American Society of Biomechanics Annual Meeting (ASB), Knoxville, USA, August 2023 (Published) BibTeX
Haptic Intelligence Miscellaneous AiroTouch: Naturalistic Vibrotactile Feedback for Telerobotic Construction Gong, Y., Javot, B., Lauer, A. P. R., Sawodny, O., Kuchenbecker, K. J. Hands-on demonstration presented at the IEEE World Haptics Conference (WHC), Delft, The Netherlands, July 2023 (Published) BibTeX
Haptic Intelligence Miscellaneous Can Recording Expert Demonstrations with Tool Vibrations Facilitate Teaching of Manual Skills? Gourishetti, R., Javot, B., Kuchenbecker, K. J. Work-in-progress paper (1 page) presented at the IEEE World Haptics Conference (WHC), Delft, The Netherlands, July 2023 (Published) BibTeX
Haptic Intelligence Miscellaneous Capturing Rich Auditory-Haptic Contact Data for Surface Recognition Khojasteh, B., Shao, Y., Kuchenbecker, K. J. Work-in-progress paper (1 page) presented at the IEEE World Haptics Conference (WHC), Delft, The Netherlands, July 2023 (Published)
The sophistication of biological sensing and transduction processes during finger-surface and tool-surface interaction is remarkable, enabling humans to perform ubiquitous tasks such as discriminating and manipulating surfaces. Capturing and processing these rich contact-elicited signals during surface exploration with similar success is an important challenge for artificial systems. Prior research introduced sophisticated mobile surface-sensing systems, but it remains less clear what quality, resolution and acuity of sensor data are necessary to perform human tasks with the same efficiency and accuracy. In order to address this gap in our understanding about artificial surface perception, we have designed a novel auditory-haptic test bed. This study aims to inspire new designs for artificial sensing tools in human-machine and robotic applications.
BibTeX
Haptic Intelligence Miscellaneous Creating a Haptic Empathetic Robot Animal for Children with Autism Burns, R. B. Workshop paper (4 pages) presented at the RSS Pioneers Workshop, Daegu, South Korea, July 2023 (Published) URL BibTeX