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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
Social Foundations of Computation Algorithms and Society Conference Paper Algorithmic Collective Action in Machine Learning Hardt, M., Mazumdar, E., Mendler-Dünner, C., Zrnic, T. In Proceedings of the 40th International Conference on Machine Learning (ICML 2023), PMLR, The Forty International Conference on Machine Learning (ICML), July 2023 (Published)
We initiate a principled study of algorithmic collective action on digital platforms that deploy machine learning algorithms. We propose a simple theoretical model of a collective interacting with a firm’s learning algorithm. The collective pools the data of participating individuals and executes an algorithmic strategy by instructing participants how to modify their own data to achieve a collective goal. We investigate the consequences of this model in three fundamental learning-theoretic settings: nonparametric optimal learning, parametric risk minimization, and gradient-based optimization. In each setting, we come up with coordinated algorithmic strategies and characterize natural success criteria as a function of the collective’s size. Complementing our theory, we conduct systematic experiments on a skill classification task involving tens of thousands of resumes from a gig platform for freelancers. Through more than two thousand model training runs of a BERT-like language model, we see a striking correspondence emerge between our empirical observations and the predictions made by our theory. Taken together, our theory and experiments broadly support the conclusion that algorithmic collectives of exceedingly small fractional size can exert significant control over a platform’s learning algorithm.
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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.
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Empirical Inference Article Catastrophic overfitting can be induced with discriminative non-robust features Ortiz-Jimenez*, G., de Jorge*, P., Sanyal, A., Bibi, A., Dokania, P. K., Frossard, P., Rogez, G., Torr, P. Transactions on Machine Learning Research , July 2023, *equal contribution (Published) PDF Code URL BibTeX
Empirical Inference Conference Paper Certifying Ensembles: A General Certification Theory with S-Lipschitzness Petrov, A., Eiras, F., Sanyal, A., Torr, P., Bibi, A. Proceedings of the 40th International Conference on Machine Learning (ICML), 202:27709-27736, Proceedings of Machine Learning Research, (Editors: A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato and J. Scarlett), PMLR, July 2023 (Published) arXiv URL BibTeX
Empirical Inference Article Comparing Apples with Apples: Robust Detection Limits for Exoplanet High-contrast Imaging in the Presence of Non-Gaussian Noise Bonse, M. J., Garvin, E. O., Gebhard, T. D., Dannert, F. A., Cantalloube, F., Cugno, G., Absil, O., Hayoz, J., Milli, J., Kasper, M., Quanz, S. P. The American Astronomical Society, 166(2), July 2023 (Published) DOI BibTeX
Robust Machine Learning Conference Paper Compositional Generalization from First Principles Wiedemer, T., Mayilvahanan, P., Bethge, M., Brendel, W. In July 2023 (Published) NeurIPS Proceedings 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
Empirical Inference Robust Machine Learning Conference Paper Desiderata for Representation Learning from Identifiability, Disentanglement, and Group-Structuredness Keurti, H., Reizinger, P., Schölkopf, B., Brendel, W. 2nd Annual Topology, Algebra, and Geometry in Machine Learning (TAG) at ICML 2023, July 2023 (Published) URL BibTeX
Empirical Inference Article Dexterous robotic manipulation using deep reinforcement learning and knowledge transfer for complex sparse reward-based tasks Wang, Q., Sanchez, F. R., McCarthy, R., Bulens, D. C., McGuinness, K., O’Connor, N., Wüthrich, M., Widmaier, F., Bauer, S., Redmond, S. J. Expert Systems, 40(6), July 2023 (Published) DOI URL BibTeX
Empirical Inference Conference Paper Diffusion Based Representation Learning Mittal*, S., Abstreiter*, K., Bauer, S., Schölkopf, B., Mehrjou, A. Proceedings of the 40th International Conference on Machine Learning (ICML), 202:24963-24982, Proceedings of Machine Learning Research, (Editors: A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato and J. Scarlett), PMLR, July 2023, *equal contribution (Published) URL BibTeX
Empirical Inference Conference Paper Discrete Key-Value Bottleneck Träuble, F., Goyal, A., Rahaman, N., Mozer, M. C., Kawaguchi, K., Bengio, Y., Schölkopf, B. Proceedings of the 40th International Conference on Machine Learning (ICML) , 202:34431-34455, Proceedings of Machine Learning Research, (Editors: A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato and J. Scarlett), PMLR, July 2023 (Published) URL BibTeX
Empirical Inference Conference Paper Efficient Semiring-Weighted Earley Parsing Opedal, A., Zmigrod, R., Vieira, T., Cotterell, R., Eisner, J. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL), 1:3687-3713, (Editors: Anna Rogers, Jordan L. Boyd-Graber and Naoaki Okazaki), Association for Computational Linguistics, July 2023 (Published) URL BibTeX
Empirical Inference Conference Paper Estimation Beyond Data Reweighting: Kernel Method of Moments Kremer, H., Nemmour, Y., Schölkopf, B., Zhu, J. Proceedings of the 40th International Conference on Machine Learning (ICML), 202:17745-17783, Proceedings of Machine Learning Research, (Editors: A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato and J. Scarlett), PMLR, July 2023 (Published) URL BibTeX
Empirical Inference Conference Paper Flow Matching for Scalable Simulation-Based Inference Wildberger*, J. B., Dax*, M., Buchholz*, S., Green, S. R., Macke, J. H., Schölkopf, B. ICML 2023 Workshop on Structured Probabilistic Inference & Generative Modeling, July 2023, *equal contribution (Published) URL BibTeX
Empirical Inference Optics and Sensing Laboratory Software Workshop Conference Paper Glare Removal for Astronomical Images with High Local Dynamic Range Bastelaer, M., Kremer, H., Volchkov, V., Passy, J., Schölkopf, B. IEEE International Conference on Computational Photography (ICCP), 1-11, IEEE, July 2023 (Published) DOI BibTeX
Empirical Inference Conference Paper Hindsight States: Blending Sim and Real Task Elements for Efficient Reinforcement Learning Guist, S., Schneider, J., Dittrich, A., Berenz, V., Schölkopf, B., Büchler, D. Robotics: Science and Systems XIX, July 2023 (Published) Project Page DOI URL BibTeX
Empirical Inference Conference Paper Homomorphism AutoEncoder — Learning Group Structured Representations from Observed Transitions Keurti, H., Pan, H., Besserve, M., Grewe, B. F., Schölkopf, B. Proceedings of the 40th International Conference on Machine Learning (ICML), 202:16190-16215, Proceedings of Machine Learning Research, (Editors: A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato and J. Scarlett), PMLR, July 2023 (Published) arXiv URL BibTeX
Haptic Intelligence Miscellaneous Improving Haptic Rendering Quality by Measuring and Compensating for Undesired Forces Fazlollahi, F., Taghizadeh, Z., Kuchenbecker, K. J. Work-in-progress paper (1 page) presented at the IEEE World Haptics Conference (WHC), Delft, The Netherlands, July 2023 (Published) BibTeX
Physics for Inference and Optimization Article Latent Network Models to Account for Noisy, Multiply-Reported Social Network Data. De Bacco, C., Contisciani, M., Cardoso-Silva, J., Safdari, H., Theuerkauf, D. B., Sweet, T., Young, J., Koster, J., Ross, C. T., McElreath, R., Redhead, D., Power, E. A. Journal of the Royal Statistical Society: Series A, 186(3):355-375, July 2023 (Published) Code Preprint DOI URL BibTeX
Perceiving Systems Ph.D. Thesis Learning Clothed 3D Human Models with Articulated Neural Implicit Representations Chen, X. July 2023 (Published)
3D digital humans are important for a range of applications including movie and game production, virtual and augmented reality, and human-computer interaction. However, existing industrial solutions for creating 3D digital humans rely on expensive scanning devices and intensive manual labor, preventing their broader application. To address these challenges, the research community focuses on learning 3D parametric human models from data, aiming to automatically generate realistic digital humans based on input parameters that specify pose and shape attributes. Although recent advancements have enabled the generation of faithful 3D human bodies, modeling realistic humans that include additional features such as clothing, hair, and accessories remains an open research challenge. The goal of this thesis is to develop 3D parametric human models that can generate realistic digital humans including not only human bodies but also additional features, in particular clothing. The central challenge lies in the fundamental problem of how to represent non-rigid, articulated, and topology-varying shapes. Explicit geometric representations like polygon meshes lack the flexibility needed to model varying topology between clothing and human bodies, and across different clothing styles. On the other hand, implicit representations, such as signed distance functions, are topologically flexible but do not have a robust articulation algorithm yet. To tackle this problem, we first introduce a principled algorithm that models articulation for implicit representations, in particular the recently emerging neural implicit representations which have shown impressive modeling fidelity. Our algorithm, SNARF, generalizes linear blend skinning for polygon meshes to implicit representations and can faithfully articulate implicit shapes to any pose. SNARF is fully differentiable, which enables learning skinning weights and shapes jointly from posed observations. By leveraging this algorithm, we can learn single-subject clothed human models with realistic shapes and natural deformations from 3D scans. We further improve SNARF’s efficiency with several implementation and algorithmic optimizations, including using a more compact representation of the skinning weights, factoring out redundant computations, and custom CUDA kernel implementations. Collectively, these adaptations result in a speedup of 150 times while preserving accuracy, thereby enabling the efficient learning of 3D animatable humans. Next, we go beyond single-subject modeling and tackle the more challenging task of generative modeling clothed 3D humans. By integrating our articulation module with deep generative models, we have developed a generative model capable of creating novel 3D humans with various clothing styles and identities, as well as geometric details such as wrinkles. Lastly, to eliminate the reliance on expensive 3D scans and to facilitate texture learning, we introduce a system that integrates our differentiable articulation module with differentiable volume rendering in an end-to-end manner, enabling the reconstruction of animatable 3D humans directly from 2D monocular videos. The contributions of this thesis significantly advance the realistic generation and reconstruction of clothed 3D humans and provide new tools for modeling non-rigid, articulated, and topology-varying shapes. We hope that this work will contribute to the development of 3D human modeling and pave the way for new applications in the future.
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Empirical Inference Ph.D. Thesis Learning and Testing Powerful Hypotheses Kübler, J. M. University of Tübingen, Germany, July 2023 (Published) BibTeX
Rationality Enhancement Conference Paper Learning planning strategies without feedback Srinivas, S., He, R., Lieder, F. July 2023 (Published) DOI URL BibTeX
Empirical Inference Conference Paper Membership Inference Attacks against Language Models via Neighbourhood Comparison Mattern, J., Mireshghallah, F., Jin, Z., Schölkopf, B., Sachan, M., Berg-Kirkpatrick, T. Findings of the Association for Computational Linguistics (ACL), 11330-11343, (Editors: Rogers, A. and Boyd-Graber, J. L. and Okazaki, N.), Association for Computational Linguistics, July 2023 (Published) DOI BibTeX
Haptic Intelligence Intelligent Control Systems Miscellaneous Multimodal Multi-User Surface Recognition with the Kernel Two-Sample Test: Code Khojasteh, B., Solowjow, F., Trimpe, S., Kuchenbecker, K. J. Code published as a companion to the journal article "Multimodal Multi-User Surface Recognition with the Kernel Two-Sample Test" in IEEE Transactions on Automation Science and Engineering, July 2023 (Published) DOI BibTeX
Haptic Intelligence Conference Paper Naturalistic Vibrotactile Feedback Could Facilitate Telerobotic Assembly on Construction Sites Gong, Y., Javot, B., Lauer, A. P. R., Sawodny, O., Kuchenbecker, K. J. In Proceedings of the IEEE World Haptics Conference (WHC), 169-175, Delft, The Netherlands, July 2023 (Published)
Telerobotics is regularly used on construction sites to build large structures efficiently. A human operator remotely controls the construction robot under direct visual feedback, but visibility is often poor. Future construction robots that move autonomously will also require operator monitoring. Thus, we designed a wireless haptic feedback system to provide the operator with task-relevant mechanical information from a construction robot in real time. Our AiroTouch system uses an accelerometer to measure the robot end-effector's vibrations and uses off-the-shelf audio equipment and a voice-coil actuator to display them to the user with high fidelity. A study was conducted to evaluate how this type of naturalistic vibration feedback affects the observer's understanding of telerobotic assembly on a real construction site. Seven adults without construction experience observed a mix of manual and autonomous assembly processes both with and without naturalistic vibrotactile feedback. Qualitative analysis of their survey responses and interviews indicated that all participants had positive responses to this technology and believed it would be beneficial for construction activities.
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Empirical Inference Conference Paper On Data Manifolds Entailed by Structural Causal Models Dominguez-Olmedo, R., Karimi, A., Arvanitidis, G., Schölkopf, B. Proceedings of the 40th International Conference on Machine Learning (ICML), 202:8188-8201, Proceedings of Machine Learning Research, (Editors: A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato and J. Scarlett), PMLR, July 2023 (Published) URL BibTeX
Empirical Inference Conference Paper On the Identifiability and Estimation of Causal Location-Scale Noise Models Immer, A., Schultheiss, C., Vogt, J. E., Schölkopf, B., Bühlmann, P., Marx, A. Proceedings of the 40th International Conference on Machine Learning (ICML), 202:14316-14332, Proceedings of Machine Learning Research, (Editors: A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato and J. Scarlett), PMLR, July 2023 (Published) URL BibTeX
Empirical Inference Conference Paper On the Relationship Between Explanation and Prediction: A Causal View Karimi, A., Muandet, K., Kornblith, S., Schölkopf, B., Kim, B. Proceedings of the 40th International Conference on Machine Learning (ICML), 202:15861-15883, Proceedings of Machine Learning Research, (Editors: A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato and J. Scarlett), PMLR, July 2023 (Published) URL BibTeX
Empirical Inference Robust Machine Learning Conference Paper Provably Learning Object-Centric Representations Brady*, J., Zimmermann*, R. S., Sharma, Y., Schölkopf, B., von Kügelen, J., Brendel, W. Proceedings of the 40th International Conference on Machine Learning (ICML), 202:3038-3062, Proceedings of Machine Learning Research, (Editors: A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato and J. Scarlett), JMLR, Cambridge, MA, July 2023, *equal contribution (Published) URL BibTeX
Empirical Inference Conference Paper Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels Immer, A., van der Ouderaa, T. F. A., van der Wilk, M., Rätsch, G., Schölkopf, B. Proceedings of the 40th International Conference on Machine Learning (ICML), 202:14333-14352, Proceedings of Machine Learning Research, (Editors: A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato and J. Scarlett), PMLR, July 2023 (Published) URL BibTeX
Haptic Intelligence Robotics Miscellaneous Strap Tightness and Tissue Composition Both Affect the Vibration Created by a Wearable Device Rokhmanova, N., Faulkner, R., Martus, J., Fiene, J., Kuchenbecker, K. J. Work-in-progress paper (1 page) presented at the IEEE World Haptics Conference (WHC), Delft, The Netherlands, July 2023 (Published)
Wearable haptic devices can provide salient real-time feedback (typically vibration) for rehabilitation, sports training, and skill acquisition. Although the body provides many sites for such cues, the influence of the mounting location on vibrotactile mechanics is commonly ignored. This study builds on previous research by quantifying how changes in strap tightness and local tissue composition affect the physical acceleration generated by a typical vibrotactile device.
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Physical Intelligence Article Swarming magnetic surface microrollers enable directed locomotion in circular confinements Bozuyuk, U., Han, M., Sitti, M. Research Square, July 2023 BibTeX
Empirical Inference Conference Paper Temporal Label Smoothing for Early Event Prediction Yèche*, H., Pace*, A., Rätsch, G., Kuznetsova, R. Proceedings of the 40th International Conference on Machine Learning (ICML), 202:39913-39938, Proceedings of Machine Learning Research, (Editors: A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato and J. Scarlett), PMLR, July 2023, *equal contribution (Published) arXiv URL BibTeX
Empirical Inference Conference Paper The Hessian perspective into the Nature of Convolutional Neural Networks Singh, S. P., Hofmann, T., Schölkopf, B. Proceedings of the 40th International Conference on Machine Learning (ICML), 202:31930-31968, Proceedings of Machine Learning Research, (Editors: A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato and J. Scarlett), PMLR, July 2023 (Published) URL BibTeX
Haptic Intelligence Miscellaneous The Influence of Amplitude and Sharpness on the Perceived Intensity of Isoenergetic Ultrasonic Signals Gueorguiev, D., Rohou–Claquin, 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 Toward a Device for Reliable Evaluation of Vibrotactile Perception Ballardini, G., Kuchenbecker, K. J. Work-in-progress paper (1 page) presented at the IEEE World Haptics Conference (WHC), Delft, The Netherlands, July 2023 (Published) BibTeX
Rationality Enhancement Conference Paper Toward a normative theory of (self-)management by goal-setting Singhi, N., Mohnert, F., Prystawski, B., Lieder, F. Proceedings of the Annual Meeting of the Cognitive Science Society, Annual Meeting of the Cognitive Science Society, July 2023 (Published) DOI URL BibTeX
Haptic Intelligence Miscellaneous Vibrotactile Playback for Teaching Manual Skills from Expert Recordings Gourishetti, R., Hughes, A. G., Javot, B., Kuchenbecker, K. J. Hands-on demonstration presented at the IEEE World Haptics Conference (WHC), Delft, The Netherlands, July 2023 (Published) BibTeX
Rationality Enhancement Conference Paper What are the mechanisms underlying metacognitive learning? He, R., Lieder, F. In July 2023 (Published) DOI URL BibTeX
Empirical Inference Conference Paper When Does Aggregating Multiple Skills with Multi-Task Learning Work? A Case Study in Financial NLP Ni, J., Jin, Z., Wang, Q., Sachan, M., Leippold, M. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL), Volume 1: Long Papers:7465-7488, (Editors: Rogers, A. and Boyd-Graber, J. L. and Okazaki, N.), Association for Computational Linguistics, July 2023 (Published) DOI BibTeX
Empirical Inference Conference Paper World Models for Math Story Problems Opedal, A., Stoehr, N., Saparov, A., Sachan, M. Findings of the Association for Computational Linguistics (ACL), 9088-9115, (Editors: Anna Rogers, Jordan L. Boyd-Graber and Naoaki Okazaki), Association for Computational Linguistics, July 2023 (Published) URL BibTeX
Empirical Inference Conference Paper ALERT: Adapt Language Models to Reasoning Tasks Yu, P., Wang, T., Golovneva, O., AlKhamissi, B., Verma, S., Jin, Z., Ghosh, G., Diab, M., Celikyilmaz, A. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL), 1:1055-1081, (Editors: Rogers, Anna and Boyd-Graber, Jordan and Okazaki, Naoaki), Association for Computational Linguistics, July 2023 (Published) DOI URL BibTeX
Haptic Intelligence Miscellaneous CAPT Motor: A Strong Direct-Drive Haptic Interface Javot, B., Nguyen, V. H., Ballardini, G., Kuchenbecker, K. J. Hands-on demonstration presented at the IEEE World Haptics Conference (WHC), Delft, The Netherlands, July 2023 (Published) BibTeX
Robotic Materials Patent Capacitive Self-Sensing for Electrostatic Transducers with High Voltage Isolation Correll, N., Ly, K. D., Kellaris, N. A., Keplinger, C. M. (US Patent App. 17/928,453), June 2023
Transducer systems disclosed herein include self-sensing capabilities. In particular, electrostatic transducers include a low voltage electrode and a high voltage electrode. A low voltage sensing unit is coupled with the low voltage electrode of the electrostatic transducer. The low voltage sensing unit is configured to measure a capacitance of the electrostatic transducer, from which displacement of the electrostatic transducer may be calculated. High voltage drive signals received by the high voltage electrode during actuation may be isolated from the low voltage sensing unit. The isolation may be provided by dielectric material of the electrostatic transducer, a voltage suppression component, and/or a voltage suppression module comprising a low impedance ground path. In the event of an electrical failure of the transducer, the low voltage sensing unit may be isolated from high voltages.
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Neural Capture and Synthesis Conference Paper High-Res Facial Appearance Capture from Polarized Smartphone Images Azinovic, D. M. O. H. C. N. M. T. J. In Proceedings 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 16836-16846, Vancouver, CA, CVPR, June 2023 (Published) DOI URL BibTeX
Deep Models and Optimization Conference Paper Resurrecting Recurrent Neural Networks for Long Sequences Orvieto, A., Smith, S. L., Gu, A., Fernando, A., Gulcehre, C., Pascanu, R., De, S. In Proceedings of the Eleventh International Conference on Learning Representations, ICLR, June 2023 (Published) URL BibTeX
Rationality Enhancement Article A Computational Process-Tracing Method for Measuring People’s Planning Strategies and How They Change Over Time Jain, Y. R., Callaway, F., Griffiths, T. L., Dayan, P., He, R., Krueger, P. M., Lieder, F. Behavior Research Methods, 55:20377-2079, June 2023 (Published)
One of the most unique and impressive feats of the human mind is its ability to discover and continuouslyrefine its own cognitive strategies. Elucidating the underlying learning and adaptation mechanisms is verydifficult because changes in cognitive strategies are not directly observable. One important domain in whichstrategies and mechanisms are studied is planning. To enable researchers to uncover how people learn howto plan, we offer a tutorial introduction to a recently developed process-tracing paradigm along with a newcomputational method for inferring people’s planning strategies and their changes over time from the resultingprocess-tracing data. Our method allows researchers to reveal experience-driven changes in people’s choice ofindividual planning operations, planning strategies, strategy types, and the relative contributions of differentdecision systems. We validate our method on simulated and empirical data. On simulated data, its inferencesabout the strategies and the relative influence of different decision systems are accurate. When evaluated on human data generated using our process-tracing paradigm, our computational method correctly detects theplasticity-enhancing effect of feedback and the effect of the structure of the environment on people’s planningstrategies. Together, these methods can be used to investigate the mechanisms of cognitive plasticity and toelucidate how people acquire complex cognitive skills such as planning and problem-solving. Importantly, ourmethods can also be used to measure individual differences in cognitive plasticity and examine how differenttypes (pedagogical) interventions affect the acquisition of cognitive skills.
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Physical Intelligence Article Avian-Inspired Perching Mechanism for Jumping Robots Kim, H., Woodward, M. A., Sitti, M. Advanced Intelligent Systems, 5(6), June 2023 (Published) DOI URL BibTeX