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Empirical Inference Article Data scarcity, robustness and extreme multi-label classification Babbar, R., Schölkopf, B. Machine Learning, 108(8):1329-1351, September 2019, Special Issue of the ECML PKDD 2019 Journal Track (Published) DOI BibTeX
Perceiving Systems Article Decoding the Viewpoint and Identity of Faces and Bodies Foster, C., Zhao, M., Bolkart, T., Black, M., Bartels, A., Bülthoff, I. Journal of Vision, 19(10): 54c:54-55, Arvo Journals, September 2019 (Published)
(2019). . , 19(10): 25.13, 54-55. doi: Zitierlink: http://hdl.handle.net/21.11116/0000-0003-7493-4
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Rationality Enhancement Conference Paper How do people learn how to plan? Jain, Y. R., Gupta, S., Rakesh, V., Dayan, P., Callaway, F., Lieder, F. 2019 Conference on Cognitive Computational Neuroscience, September 2019 (Published)
How does the brain learn how to plan? We reverse-engineer people's underlying learning mechanisms by combining rational process models of cognitive plasticity with recently developed empirical methods that allow us to trace the temporal evolution of people's planning strategies. We find that our Learned Value of Computation model (LVOC) accurately captures people's average learning curve. However, there were also substantial individual differences in metacognitive learning that are best understood in terms of multiple different learning mechanisms-including strategy selection learning. Furthermore, we observed that LVOC could not fully capture people's ability to adaptively decide when to stop planning. We successfully extended the LVOC model to address these discrepancies. Our models broadly capture people's ability to improve their decision mechanisms and represent a significant step towards reverse-engineering how the brain learns increasingly effective cognitive strategies through its interaction with the environment.
How do people learn to plan? How do people learn to plan? BibTeX
Perceiving Systems Conference Paper Learning to Train with Synthetic Humans Hoffmann, D. T., Tzionas, D., Black, M. J., Tang, S. In German Conference on Pattern Recognition (GCPR), 609-623, Springer International Publishing, September 2019
Neural networks need big annotated datasets for training. However, manual annotation can be too expensive or even unfeasible for certain tasks, like multi-person 2D pose estimation with severe occlusions. A remedy for this is synthetic data with perfect ground truth. Here we explore two variations of synthetic data for this challenging problem; a dataset with purely synthetic humans, as well as a real dataset augmented with synthetic humans. We then study which approach better generalizes to real data, as well as the influence of virtual humans in the training loss. We observe that not all synthetic samples are equally informative for training, while the informative samples are different for each training stage. To exploit this observation, we employ an adversarial student-teacher framework; the teacher improves the student by providing the hardest samples for its current state as a challenge. Experiments show that this student-teacher framework outperforms all our baselines.
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Intelligent Control Systems Conference Paper Predictive Triggering for Distributed Control of Resource Constrained Multi-agent Systems Mastrangelo, J. M., Baumann, D., Trimpe, S. In Proceedings of the 8th IFAC Workshop on Distributed Estimation and Control in Networked Systems, 79-84, 8th IFAC Workshop on Distributed Estimation and Control in Networked Systems (NecSys), September 2019 (Published) arXiv PDF DOI BibTeX
Rationality Enhancement Conference Paper Testing Computational Models of Goal Pursuit Mohnert, F., Tosic, M., Lieder, F. 2019 Conference on Cognitive Computational Neuroscience,, CCN2019, September 2019 (Published)
Goals are essential to human cognition and behavior. But how do we pursue them? To address this question, we model how capacity limits on planning and attention shape the computational mechanisms of human goal pursuit. We test the predictions of a simple model based on previous theories in a behavioral experiment. The results show that to fully capture how people pursue their goals it is critical to account for people’s limited attention in addition to their limited planning. Our findings elucidate the cognitive constraints that shape human goal pursuit and point to an improved model of human goal pursuit that can reliably predict which goals a person will achieve and which goals they will struggle to pursue effectively.
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Movement Generation and Control Article Unintended Consequences of Biased Robotic and Artificial Intelligence Systems [Ethical, Legal, and Societal Issues] Righetti, L., Madhavan, R., Chatila, R. IEEE Robotics & Automation Magazine, 26, September 2019 (Published) BibTeX
Micro, Nano, and Molecular Systems Article Genetically modified M13 bacteriophage nanonets for enzyme catalysis and recovery Kadiri, V. M., Alarcon-Correa, M., Guenther, J. P., Ruppert, J., Bill, J., Rothenstein, D., Fischer, P. Catalysts, 9:723, August 2019
Enzyme-based biocatalysis exhibits multiple advantages over inorganic catalysts, including the biocompatibility and the unchallenged specificity of enzymes towards their substrate. The recovery and repeated use of enzymes is essential for any realistic application in biotechnology, but is not easily achieved with current strategies. For this purpose, enzymes are often immobilized on inorganic scaffolds, which could entail a reduction of the enzymes’ activity. Here, we show that immobilization to a nano-scaled biological scaffold, a nanonetwork of end-to-end cross-linked M13 bacteriophages, ensures high enzymatic activity and at the same time allows for the simple recovery of the enzymes. The bacteriophages have been genetically engineered to express AviTags at their ends, which permit biotinylation and their specific end-to-end self-assembly while allowing space on the major coat protein for enzyme coupling. We demonstrate that the phages form nanonetwork structures and that these so-called nanonets remain highly active even after re-using the nanonets multiple times in a flow-through reactor.
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Perceiving Systems Conference Paper Motion Planning for Multi-Mobile-Manipulator Payload Transport Systems Tallamraju, R., Salunkhe, D., Rajappa, S., Ahmad, A., Karlapalem, K., Shah, S. V. In 15th IEEE International Conference on Automation Science and Engineering, 1469-1474, IEEE, 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), August 2019, ISSN: 2161-8089 (Published) DOI BibTeX
Micro, Nano, and Molecular Systems Article Light-controlled micromotors and soft microrobots Palagi, S., Singh, D. P., Fischer, P. Adv. Opt. Mat., 7:1900370, August 2019
Mobile microscale devices and microrobots can be powered by catalytic reactions (chemical micromotors) or by external fields. This report is focused on the role of light as a versatile means for wirelessly powering and controlling such microdevices. Recent advances in the development of autonomous micromotors are discussed, where light permits their actuation with unprecedented control and thereby enables advances in the field of active matter. In addition, structuring the light field is a new means to drive soft microrobots that are based on (photo‐) responsive polymers. The behavior of the two main classes of thermo‐ and photoresponsive polymers adopted in microrobotics (poly(N‐isopropylacrylamide) and liquid‐crystal elastomers) is analyzed, and recent applications are reported. The advantages and limitations of controlling micromotors and microrobots by light are reviewed, and some of the remaining challenges in the development of novel photo‐active materials for micromotors and microrobots are discussed.
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Empirical Inference Master Thesis Learning to Diagnose Diabetes from Magnetic Resonance Tomography Dietz, B. ETH Zurich, Switzerland, August 2019 (Published) BibTeX
Empirical Inference Bachelor Thesis Automatic Segmentation and Labelling for Robot Table Tennis Time Series Lutz, P. Technical University Darmstadt, Germany, August 2019 (Published) BibTeX
Dynamic Locomotion Article Beyond Basins of Attraction: Quantifying Robustness of Natural Dynamics Steve Heim, , Spröwitz, A. IEEE Transactions on Robotics (T-RO) , 35(4):939-952, August 2019 (Published)
Properly designing a system to exhibit favorable natural dynamics can greatly simplify designing or learning the control policy. However, it is still unclear what constitutes favorable natural dynamics and how to quantify its effect. Most studies of simple walking and running models have focused on the basins of attraction of passive limit cycles and the notion of self-stability. We instead emphasize the importance of stepping beyond basins of attraction. In this paper, we show an approach based on viability theory to quantify robust sets in state-action space. These sets are valid for the family of all robust control policies, which allows us to quantify the robustness inherent to the natural dynamics before designing the control policy or specifying a control objective. We illustrate our formulation using spring-mass models, simple low-dimensional models of running systems. We then show an example application by optimizing robustness of a simulated planar monoped, using a gradient-free optimization scheme. Both case studies result in a nonlinear effective stiffness providing more robustness.
arXiv preprint arXiv:1806.08081 T-RO DOI URL BibTeX
Rationality Enhancement Article Cognitive Prostheses for Goal Achievement Lieder, F., Chen, O. X., Krueger, P. M., Griffiths, T. L. Nature Human Behavior, 3, August 2019 (Published)
Procrastination and impulsivity take a significant toll on people’s lives and the economy at large. Both can result from the misalignment of an action's proximal rewards with its long-term value. Therefore, aligning immediate reward with long-term value could be a way to help people overcome motivational barriers and make better decisions. Previous research has shown that game elements, such as points, levels, and badges, can be used to motivate people and nudge their decisions on serious matters. Here, we develop a new approach to decision support that leveragesartificial intelligence and game elements to restructure challenging sequential decision problems in such a way that it becomes easier for people to take the right course of action. A series of four increasingly more realistic experiments suggests that this approach can enable people to make better decisions faster, procrastinate less, complete their work on time, and waste less time on unimportant tasks. These findings suggest that our method is a promising step towards developing cognitive prostheses that help people achieve their goals by enhancing their motivation and decision-making in everyday life.
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Haptic Intelligence Article Low-Hysteresis and Low-Interference Soft Tactile Sensor Using a Conductive Coated Porous Elastomer and a Structure for Interference Reduction Park, K., Kim, S., Lee, H., Park, I., Kim, J. Sensors and Actuators A: Physical, 295:541-550, August 2019 (Published)
The need for soft whole-body tactile sensors is emerging. Piezoresistive materials are advantageous in terms of making large tactile sensors, but the hysteresis of piezoresistive materials is a major drawback. The hysteresis of a piezoresistive material should be attenuated to make a practical piezoresistive soft tactile sensor. In this paper, we introduce a low-hysteresis and low-interference soft tactile sensor using a conductive coated porous elastomer and a structure to reduce interference (grooves). The developed sensor exhibits low hysteresis because the transduction mechanism of the sensor is dominated by the contact between the conductive coated surface. In a cyclic loading experiment with different loading frequencies, the mechanical and piezoresistive hysteresis values of the sensor are less than 21.7% and 6.8%, respectively. The initial resistance change is found to be within 4% after the first loading cycle. To reduce the interference among the sensing points, we also propose a structure where the grooves are inserted between the adjacent electrodes. This structure is implemented during the molding process, which is adopted to extend the porous tactile sensor to large-scale and facile fabrication. The effects of the structure are investigated with respect to the normalized design parameters ΘD, ΘW, and ΘT in a simulation, and the result is validated for samples with the same design parameters. An indentation experiment also shows that the structure designed for interference reduction effectively attenuates the interference of the sensor array, indicating that the spatial resolution of the sensor array is improved. As a result, the sensor can exhibit low hysteresis and low interference simultaneously. This research can be used for many applications, such as robotic skin, grippers, and wearable devices.
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Empirical Inference Poster Perception of temporal dependencies in autoregressive motion Meding, K., Schölkopf, B., Wichmann, F. A. Perception, 48(2-suppl):141, 42nd European Conference on Visual Perception (ECVP), August 2019 (Published) URL BibTeX
Empirical Inference Poster Phenomenal Causality and Sensory Realism Bruijns, S. A., Meding, K., Schölkopf, B., Wichmann, F. A. Perception, 48(2-suppl):141, 42nd European Conference on Visual Perception (ECVP), August 2019 (Published) URL BibTeX
Dynamic Locomotion Article Series Elastic Behavior of Biarticular Muscle-Tendon Structure in a Robotic Leg Ruppert, F., Badri-Spröwitz, A. Frontiers in Neurorobotics, 64:13, 13, August 2019 (Published) Frontiers YouTube DOI URL BibTeX
Dynamic Locomotion Conference Paper The positive side of damping Heim, S., Millard, M., Le Mouel, C., Sproewitz, A. Proceedings of AMAM, The 9th International Symposium on Adaptive Motion of Animals and Machines, August 2019 (Published) BibTeX
Rationality Enhancement Conference Paper Measuring How People Learn How to Plan Jain, Y. R., Callaway, F., Lieder, F. In Proceedings 41st Annual Meeting of the Cognitive Science Society, 1956-1962, CogSci2019, 41st Annual Meeting of the Cognitive Science Society, July 2019 (Published)
The human mind has an unparalleled ability to acquire complex cognitive skills, discover new strategies, and refine its ways of thinking and decision-making; these phenomena are collectively known as cognitive plasticity. One important manifestation of cognitive plasticity is learning to make better–more far-sighted–decisions via planning. A serious obstacle to studying how people learn how to plan is that cognitive plasticity is even more difficult to observe than cognitive strategies are. To address this problem, we develop a computational microscope for measuring cognitive plasticity and validate it on simulated and empirical data. Our approach employs a process tracing paradigm recording signatures of human planning and how they change over time. We then invert a generative model of the recorded changes to infer the underlying cognitive plasticity. Our computational microscope measures cognitive plasticity significantly more accurately than simpler approaches, and it correctly detected the effect of an external manipulation known to promote cognitive plasticity. We illustrate how computational microscopes can be used to gain new insights into the time course of metacognitive learning and to test theories of cognitive development and hypotheses about the nature of cognitive plasticity. Future work will leverage our computational microscope to reverse-engineer the learning mechanisms enabling people to acquire complex cognitive skills such as planning and problem solving.
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Micro, Nano, and Molecular Systems Conference Paper Soft Phantom for the Training of Renal Calculi Diagnostics and Lithotripsy Li., D., Suarez-Ibarrola, R., Choi, E., Jeong, M., Gratzke, C., Miernik, A., Fischer, P., Qiu, T. 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), July 2019
Organ models are important for medical training and surgical planning. With the fast development of additive fabrication technologies, including 3D printing, the fabrication of 3D organ phantoms with precise anatomical features becomes possible. Here, we develop the first high-resolution kidney phantom based on soft material assembly, by combining 3D printing and polymer molding techniques. The phantom exhibits both the detailed anatomy of a human kidney and the elasticity of soft tissues. The phantom assembly can be separated into two parts on the coronal plane, thus large renal calculi are readily placed at any desired location of the calyx. With our sealing method, the assembled phantom withstands a hydraulic pressure that is four times the normal intrarenal pressure, thus it allows the simulation of medical procedures under realistic pressure conditions. The medical diagnostics of the renal calculi is performed by multiple imaging modalities, including X-ray, ultrasound imaging and endoscopy. The endoscopic lithotripsy is also successfully performed on the phantom. The use of a multifunctional soft phantom assembly thus shows great promise for the simulation of minimally invasive medical procedures under realistic conditions.
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Probabilistic Numerics Conference Paper Active Multi-Information Source Bayesian Quadrature Gessner, A. G. J. M. M. Proceedings 35TH UNCERTAINTY IN ARTIFICIAL INTELLIGENCE CONFERENCE (UAI 2019), 712-721, (Editors: Adams, RP; Gogate, V), UAI, July 2019 (Published) URL BibTeX
Micro, Nano, and Molecular Systems Conference Paper Soft Continuous Surface for Micromanipulation driven by Light-controlled Hydrogels Choi, E., Jeong, H., Qiu, T., Fischer, P., Palagi, S. 4th IEEE International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS), July 2019
Remotely controlled, automated actuation and manipulation at the microscale is essential for a number of micro-manufacturing, biology, and lab-on-a-chip applications. To transport and manipulate micro-objects, arrays of remotely controlled micro-actuators are required, which, in turn, typically require complex and expensive solid-state chips. Here, we show that a continuous surface can function as a highly parallel, many-degree of freedom, wirelessly-controlled microactuator with seamless deformation. The soft continuous surface is based on a hydrogel that undergoes a volume change in response to applied light. The fabrication of the hydrogels and the characterization of their optical and thermomechanical behaviors are reported. The temperature-dependent localized deformation of the hydrogel is also investigated by numerical simulations. Static and dynamic deformations are obtained in the soft material by projecting light fields at high spatial resolution onto the surface. By controlling such deformations in open loop and especially closed loop, automated photoactuation is achieved. The surface deformations are then exploited to examine how inert microbeads can be manipulated autonomously on the surface. We believe that the proposed approach suggests ways to implement universal 2D micromanipulation schemes that can be useful for automation in microfabrication and lab-on-a-chip applications.
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Micro, Nano, and Molecular Systems Conference Paper A Magnetic Actuation System for the Active Microrheology in Soft Biomaterials Jeong, M., Choi, E., Li., D., Palagi, S., Fischer, P., Qiu, T. 4th IEEE International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS), July 2019
Microrheology is a key technique to characterize soft materials at small scales. The microprobe is wirelessly actuated and therefore typically only low forces or torques can be applied, which limits the range of the applied strain. Here, we report a new magnetic actuation system for microrheology consisting of an array of rotating permanent magnets, which achieves a rotating magnetic field with a spatially homogeneous high field strength of ~100 mT in a working volume of ~20×20×20 mm3. Compared to a traditional electromagnetic coil system, the permanent magnet assembly is portable and does not require cooling, and it exerts a large magnetic torque on the microprobe that is an order of magnitude higher than previous setups. Experimental results demonstrate that the measurement range of the soft gels’ elasticity covers at least five orders of magnitude. With the large actuation torque, it is also possible to study the fracture mechanics of soft biomaterials at small scales.
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Modern Magnetic Systems Micro, Nano, and Molecular Systems Materials Article Superior Magnetic Performance in FePt L10 Nanomaterials Son, K., Ryu, G. H., Jeong, H., Fink, L., Merz, M., Nagel, P., Schuppler, S., Richter, G., Goering, E., Schütz, G. Small, 15(34):1902353, Wiley, Weinheim, Germany, July 2019
The discovery of the high maximum energy product of 59 MGOe for NdFeB magnets is a breakthrough in the development of permanent magnets with a tremendous impact in many fields of technology. This value is still the world record, for 40 years. This work reports on a reliable and robust route to realize nearly perfectly ordered L1_0-phase FePt nanoparticles, leading to an unprecedented energy product of 80 MGOe at room temperature. Furthermore, with a 3 nm Au coverage, the magnetic polarization of these nanomagnets can be enhanced by 25% exceeding 1.8 T. This exceptional magnetization and anisotropy is confirmed by using multiple imaging and spectroscopic methods, which reveal highly consistent results. Due to the unprecedented huge energy product, this material can be envisaged as a new advanced basic magnetic component in modern micro and nanosized devices.
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Rationality Enhancement Conference Paper A Cognitive Tutor for Helping People Overcome Present Bias Lieder, F., Callaway, F., Jain, Y. R., Krueger, P. M., Das, P., Gul, S., Griffiths, T. L. RLDM, July 2019, Falk Lieder and Frederick Callaway contributed equally to this publication. (Published)
People's reliance on suboptimal heuristics gives rise to a plethora of cognitive biases in decision-making including the present bias, which denotes people's tendency to be overly swayed by an action's immediate costs/benefits rather than its more important long-term consequences. One approach to helping people overcome such biases is to teach them better decision strategies. But which strategies should we teach them? And how can we teach them effectively? Here, we leverage an automatic method for discovering rational heuristics and insights into how people acquire cognitive skills to develop an intelligent tutor that teaches people how to make better decisions. As a proof of concept, we derive the optimal planning strategy for a simple model of situations where people fall prey to the present bias. Our cognitive tutor teaches people this optimal planning strategy by giving them metacognitive feedback on how they plan in a 3-step sequential decision-making task. Our tutor's feedback is designed to maximally accelerate people's metacognitive reinforcement learning towards the optimal planning strategy. A series of four experiments confirmed that training with the cognitive tutor significantly reduced present bias and improved people's decision-making competency: Experiment 1 demonstrated that the cognitive tutor's feedback can help participants discover far-sighted planning strategies. Experiment 2 found that this training effect transfers to more complex environments. Experiment 3 found that these transfer effects are retained for at least 24 hours after the training. Finally, Experiment 4 found that practicing with the cognitive tutor can have additional benefits over being told the strategy in words. The results suggest that promoting metacognitive reinforcement learning with optimal feedback is a promising approach to improving the human mind.
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Empirical Inference Conference Paper Assessing Transferability in Reinforcement Learning from Randomized Simulations Muratore, F., Gienger, M., Peters, J. 4th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM), July 2019 (Published) URL BibTeX
Empirical Inference Conference Paper Belief space model predictive control for approximately optimal system identification Belousov, B., Abdulsamad, H., Schultheis, M., Peters, J. 4th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM), July 2019 (Published) URL BibTeX
Empirical Inference Conference Paper Beta Power May Mediate the Effect of Gamma-TACS on Motor Performance Mastakouri, A., Schölkopf, B., Grosse-Wentrup, M. 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 5902-5908, July 2019 (Published) arXiv PDF DOI URL BibTeX
Empirical Inference Conference Paper Coordinating Users of Shared Facilities via Data-driven Predictive Assistants and Game Theory Geiger, P., Besserve, M., Winkelmann, J., Proissl, C., Schölkopf, B. Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), 115:207-216, Proceedings of Machine Learning Research, (Editors: Adams, Ryan P. and Gogate, Vibhav), PMLR, July 2019 (Published) URL BibTeX
Empirical Inference Conference Paper Deep Optimal Control: Using the Euler-Lagrange Equation to learn an Optimal Feedback Control Law Lutter, M., Peters, J. 4th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM), July 2019 URL BibTeX
Haptic Intelligence Conference Paper Effect of Remote Masking on Detection of Electrovibration Jamalzadeh, M., Güçlü, B., Vardar, Y., Basdogan, C. In Proceedings of the IEEE World Haptics Conference (WHC), 229-234, Tokyo, Japan, July 2019 (Published)
Masking has been used to study human perception of tactile stimuli, including those created on haptic touch screens. Earlier studies have investigated the effect of in-site masking on tactile perception of electrovibration. In this study, we investigated whether it is possible to change detection threshold of electrovibration at fingertip of index finger via remote masking, i.e. by applying a (mechanical) vibrotactile stimulus on the proximal phalanx of the same finger. The masking stimuli were generated by a voice coil (Haptuator). For eight participants, we first measured the detection thresholds for electrovibration at the fingertip and for vibrotactile stimuli at the proximal phalanx. Then, the vibrations on the skin were measured at four different locations on the index finger of subjects to investigate how the mechanical masking stimulus propagated as the masking level was varied. Finally, electrovibration thresholds measured in the presence of vibrotactile masking stimuli. Our results show that vibrotactile masking stimuli generated sub-threshold vibrations around fingertip, and hence did not mechanically interfere with the electrovibration stimulus. However, there was a clear psychophysical masking effect due to central neural processes. Electrovibration absolute threshold increased approximately 0.19 dB for each dB increase in the masking level.
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Intelligent Control Systems Conference Paper Event-triggered Pulse Control with Model Learning (if Necessary) Baumann, D., Solowjow, F., Johansson, K. H., Trimpe, S. In Proceedings of the American Control Conference, 792-797, American Control Conference (ACC), July 2019 (Published) arXiv PDF BibTeX
Empirical Inference Conference Paper Exploration Driven by an Optimistic Bellman Equation Tosatto, S., D’Eramo, C., Pajarinen, J., Restelli, M., Peters, J. International Joint Conference on Neural Networks (IJCNN), 1-8, July 2019 (Published) DOI BibTeX
Rationality Enhancement Conference Paper Extending Rationality Pothos, E. M., Busemeyer, J. R., Pleskac, T., Yearsley, J. M., Tenenbaum, J. B., Goodman, N. D., Tessler, M. H., Griffiths, T. L., Lieder, F., Hertwig, R., Pachur, T., Leuker, C., Shiffrin, R. M. Proceedings of the 41st Annual Conference of the Cognitive Science Society, 39-40, CogSci, July 2019 (Published) Proceedings of the 41st Annual Conference of the Cognitive Science Society BibTeX
Haptic Intelligence Miscellaneous Fingertip Friction Enhances Perception of Normal Force Changes Gueorguiev, D., Lambert, J., Thonnard, J., Kuchenbecker, K. J. Work-in-progress paper (2 pages) presented at the IEEE World Haptics Conference (WHC), Tokyo, Japan, July 2019 (Published)
Using a force-controlled robotic platform, we tested the human perception of positive and negative modulations in normal force during passive dynamic touch, which also induced a strong related change in the finger-surface lateral force. In a two-alternative forced-choice task, eleven participants had to detect brief variations in the normal force compared to a constant controlled pre-stimulation force of 1 N and report whether it had increased or decreased. The average 75% just noticeable difference (JND) was found to be around 0.25 N for detecting the peak change and 0.30 N for correctly reporting the increase or the decrease. Interestingly, the friction coefficient of a subject’s fingertip positively correlated with his or her performance at detecting the change and reporting its direction, which suggests that humans may use the lateral force as a sensory cue to perceive variations in the normal force.
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Haptic Intelligence Conference Paper Fingertip Interaction Metrics Correlate with Visual and Haptic Perception of Real Surfaces Vardar, Y., Wallraven, C., Kuchenbecker, K. J. In Proceedings of the IEEE World Haptics Conference (WHC), 395-400, Tokyo, Japan, July 2019 (Published)
Both vision and touch contribute to the perception of real surfaces. Although there have been many studies on the individual contributions of each sense, it is still unclear how each modality’s information is processed and integrated. To fill this gap, we investigated the similarity of visual and haptic perceptual spaces, as well as how well they each correlate with fingertip interaction metrics. Twenty participants interacted with ten different surfaces from the Penn Haptic Texture Toolkit by either looking at or touching them and judged their similarity in pairs. By analyzing the resulting similarity ratings using multi-dimensional scaling (MDS), we found that surfaces are similarly organized within the three-dimensional perceptual spaces of both modalities. Also, between-participant correlations were significantly higher in the haptic condition. In a separate experiment, we obtained the contact forces and accelerations acting on one finger interacting with each surface in a controlled way. We analyzed the collected fingertip interaction data in both the time and frequency domains. Our results suggest that the three perceptual dimensions for each modality can be represented by roughness/smoothness, hardness/softness, and friction, and that these dimensions can be estimated by surface vibration power, tap spectral centroid, and kinetic friction coefficient, respectively.
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Haptic Intelligence Miscellaneous High-Fidelity Multiphysics Finite Element Modeling of Finger-Surface Interactions with Tactile Feedback Serhat, G., Kuchenbecker, K. J. Work-in-progress paper (2 pages) presented at the IEEE World Haptics Conference (WHC), Tokyo, Japan, July 2019 (Published)
In this study, we develop a high-fidelity finite element (FE) analysis framework that enables multiphysics simulation of the human finger in contact with a surface that is providing tactile feedback. We aim to elucidate a variety of physical interactions that can occur at finger-surface interfaces, including contact, friction, vibration, and electrovibration. We also develop novel FE-based methods that will allow prediction of nonconventional features such as real finger-surface contact area and finger stickiness. We envision using the developed computational tools for efficient design and optimization of haptic devices by replacing expensive and lengthy experimental procedures with high-fidelity simulation.
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Rationality Enhancement Conference Paper How should we incentivize learning? An optimal feedback mechanism for educational games and online courses Xu, L., Wirzberger, M., Lieder, F. 41st Annual Meeting of the Cognitive Science Society, July 2019 (Published)
Online courses offer much-needed opportunities for lifelong self-directed learning, but people rarely follow through on their noble intentions to complete them. To increase student retention educational software often uses game elements to motivate students to engage in and persist in learning activities. However, gamification only works when it is done properly, and there is currently no principled method that educational software could use to achieve this. We develop a principled feedback mechanism for encouraging good study choices and persistence in self-directed learning environments. Rather than giving performance feedback, our method rewards the learner's efforts with optimal brain points that convey the value of practice. To derive these optimal brain points, we applied the theory of optimal gamification to a mathematical model of skill acquisition. In contrast to hand-designed incentive structures, optimal brain points are constructed in such a way that the incentive system cannot be gamed. Evaluating our method in a behavioral experiment, we find that optimal brain points significantly increased the proportion of participants who instead of exploiting an inefficient skill they already knew-attempted to learn a difficult but more efficient skill, persisted through failure, and succeeded to master the new skill. Our method provides a principled approach to designing incentive structures and feedback mechanisms for educational games and online courses. We are optimistic that optimal brain points will prove useful for increasing student retention and helping people overcome the motivational obstacles that stand in the way of self-directed lifelong learning.
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Haptic Intelligence Miscellaneous Inflatable Haptic Sensor for the Torso of a Hugging Robot Block, A. E., Kuchenbecker, K. J. Work-in-progress paper (2 pages) presented at the IEEE World Haptics Conference (WHC), Tokyo, Japan, July 2019 (Published)
During hugs, humans naturally provide and intuit subtle non-verbal cues that signify the strength and duration of an exchanged hug. Personal preferences for this close interaction may vary greatly between people; robots do not currently have the abilities to perceive or understand these preferences. This work-in-progress paper discusses designing, building, and testing a novel inflatable torso that can simultaneously soften a robot and act as a tactile sensor to enable more natural and responsive hugging. Using PVC vinyl, a microphone, and a barometric pressure sensor, we created a small test chamber to demonstrate a proof of concept for the full torso. While contacting the chamber in several ways common in hugs (pat, squeeze, scratch, and rub), we recorded data from the two sensors. The preliminary results suggest that the complementary haptic sensing channels allow us to detect coarse and fine contacts typically experienced during hugs, regardless of user hand placement.
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Rationality Enhancement Conference Paper Measuring How People Learn How to Plan Jain, Y. R., Callaway, F., Lieder, F. 357-361, RLDM, July 2019 (Published)
The human mind has an unparalleled ability to acquire complex cognitive skills, discover new strategies, and refine its ways of thinking and decision-making; these phenomena are collectively known as cognitive plasticity. One important manifestation of cognitive plasticity is learning to make better – more far-sighted – decisions via planning. A serious obstacle to studying how people learn how to plan is that cognitive plasticity is even more difficult to observe than cognitive strategies are. To address this problem, we develop a computational microscope for measuring cognitive plasticity and validate it on simulated and empirical data. Our approach employs a process tracing paradigm recording signatures of human planning and how they change over time. We then invert a generative model of the recorded changes to infer the underlying cognitive plasticity. Our computational microscope measures cognitive plasticity significantly more accurately than simpler approaches, and it correctly detected the effect of an external manipulation known to promote cognitive plasticity. We illustrate how computational microscopes can be used to gain new insights into the time course of metacognitive learning and to test theories of cognitive development and hypotheses about the nature of cognitive plasticity. Future work will leverage our computational microscope to reverse-engineer the learning mechanisms enabling people to acquire complex cognitive skills such as planning and problem solving.
URL BibTeX
Empirical Inference Conference Paper Measuring Similarities between Markov Decision Processes Klink, P., Peters, J. 4th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM), July 2019 (Published) URL BibTeX
Empirical Inference Poster Neural mass modeling of the Ponto-Geniculo-Occipital wave and its neuromodulation Shao, K., Logothetis, N., Besserve, M. 28th Annual Computational Neuroscience Meeting (CNS*2019), July 2019 (Published) DOI BibTeX
Haptic Intelligence Conference Paper Objective and Subjective Assessment of Algorithms for Reducing Three-Axis Vibrations to One-Axis Vibrations Park, G., Kuchenbecker, K. J. In Proceedings of the IEEE World Haptics Conference (WHC), 467-472, Tokyo, Japan, July 2019 (Published)
A typical approach to creating realistic vibrotactile feedback is reducing 3D vibrations recorded by an accelerometer to 1D signals that can be played back on a haptic actuator, but some of the information is often lost in this dimensional reduction process. This paper describes seven representative algorithms and proposes four metrics based on the spectral match, the temporal match, and the average value and the variability of them across 3D rotations. These four performance metrics were applied to four texture recordings, and the method utilizing the discrete fourier transform (DFT) was found to be the best regardless of the sensing axis. We also recruited 16 participants to assess the perceptual similarity achieved by each algorithm in real time. We found the four metrics correlated well with the subjectively rated similarities for the six dimensional reduction algorithms, with the exception of taking the 3D vector magnitude, which was perceived to be good despite its low spectral and temporal match metrics.
DOI BibTeX
Empirical Inference Probabilistic Learning Group Conference Paper Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning Peharz, R., Vergari, A., Stelzner, K., Molina, A., Shao, X., Trapp, M., Kersting, K., Ghahramani, Z. Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), 115:334-344, Proceedings of Machine Learning Research, (Editors: Adams, Ryan P. and Gogate, Vibhav), PMLR, July 2019 (Published) URL BibTeX
Empirical Inference Master Thesis Reinforcement Learning for a Two-Robot Table Tennis Simulation Li, G. RWTH Aachen University, Germany, July 2019 (Published) BibTeX
Haptic Intelligence Article Tactile Roughness Perception of Virtual Gratings by Electrovibration Isleyen, A., Vardar, Y., Basdogan, C. IEEE Transactions on Haptics, 13(3):562-570, July 2019 (Published) DOI BibTeX
Empirical Inference Conference Paper The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA Gresele*, L., Rubenstein*, P. K., Mehrjou, A., Locatello, F., Schölkopf, B. Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), 115:217-227, Proceedings of Machine Learning Research, (Editors: Adams, Ryan P. and Gogate, Vibhav), PMLR, July 2019, *equal contribution (Published) URL BibTeX
Empirical Inference Conference Paper The Sensitivity of Counterfactual Fairness to Unmeasured Confounding Kilbertus, N., Ball, P. J., Kusner, M. J., Weller, A., Silva, R. Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), 115:616-626, Proceedings of Machine Learning Research, (Editors: Adams, Ryan P. and Gogate, Vibhav), PMLR, July 2019 (Published) URL BibTeX
Haptic Intelligence Miscellaneous The Haptician and the Alphamonsters Forte, M., L’Orsa, R., Mohan, M., Nam, S., Kuchenbecker, K. J. Student Innovation Challenge on Implementing Haptics in Virtual Reality Environment presented at the IEEE World Haptics Conference, Tokyo, Japan, July 2019, Maria-Paola Forte, Rachael L'Orsa, Mayumi Mohan, and Saekwang Nam contributed equally to this publication (Published)
Dysgraphia is a neurological disorder characterized by writing disabilities that affects between 7% and 15% of children. It presents itself in the form of unfinished letters, letter distortion, inconsistent letter size, letter collision, etc. Traditional therapeutic exercises require continuous assistance from teachers or occupational therapists. Autonomous partial or full haptic guidance can produce positive results, but children often become bored with the repetitive nature of such activities. Conversely, virtual rehabilitation with video games represents a new frontier for occupational therapy due to its highly motivational nature. Virtual reality (VR) adds an element of novelty and entertainment to therapy, thus motivating players to perform exercises more regularly. We propose leveraging the HTC VIVE Pro and the EXOS Wrist DK2 to create an immersive spellcasting “exergame” (exercise game) that helps motivate children with dysgraphia to improve writing fluency.
Student Innovation Challenge – Virtual Reality BibTeX