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Robot Learning

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2022

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Empirical Inference Conference Paper USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution Rangnekar, V., Upadhyay, U., Akata, Z., Banerjee, B. Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI), 216:1707-1717, Proceedings of Machine Learning Research, (Editors: Evans, Robin J. and Shpitser, Ilya), PMLR, August 2023 (Published) URL BibTeX
Haptic Intelligence Conference Paper Wear Your Heart on Your Sleeve: Users Prefer Robots with Emotional Reactions to Touch and Ambient Moods Burns, R. B., Ojo, F., Kuchenbecker, K. J. In Proceedings of the IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 1914-1921, Busan, South Korea, August 2023 (Published)
Robots are increasingly being developed as assistants for household, education, therapy, and care settings. Such robots can use adaptive emotional behavior to communicate warmly and effectively with their users and to encourage interest in extended interactions. However, autonomous physical robots often lack a dynamic internal emotional state, instead displaying brief, fixed emotion routines to promote specific user interactions. Furthermore, despite the importance of social touch in human communication, most commercially available robots have limited touch sensing, if any at all. We propose that users' perceptions of a social robotic system will improve when the robot provides emotional responses on both shorter and longer time scales (reactions and moods), based on touch inputs from the user. We evaluated this proposal through an online study in which 51 diverse participants watched nine randomly ordered videos (a three-by-three full-factorial design) of the koala-like robot HERA being touched by a human. Users provided the highest ratings in terms of agency, ambient activity, enjoyability, and touch perceptivity for scenarios in which HERA showed emotional reactions and either neutral or emotional moods in response to social touch gestures. Furthermore, we summarize key qualitative findings about users' preferences for reaction timing, the ability of robot mood to show persisting memory, and perception of neutral behaviors as a curious or self-aware robot.
DOI BibTeX
Perceiving Systems Article BARC: Breed-Augmented Regression Using Classification for 3D Dog Reconstruction from Images Rueegg, N., Zuffi, S., Schindler, K., Black, M. J. Int. J. of Comp. Vis. (IJCV), 131(8):1964–1979, August 2023 (Published)
The goal of this work is to reconstruct 3D dogs from monocular images. We take a model-based approach, where we estimate the shape and pose parameters of a 3D articulated shape model for dogs. We consider dogs as they constitute a challenging problem, given they are highly articulated and come in a variety of shapes and appearances. Recent work has considered a similar task using the multi-animal SMAL model, with additional limb scale parameters, obtaining reconstructions that are limited in terms of realism. Like previous work, we observe that the original SMAL model is not expressive enough to represent dogs of many different breeds. Moreover, we make the hypothesis that the supervision signal used to train the network, that is 2D keypoints and silhouettes, is not sufficient to learn a regressor that can distinguish between the large variety of dog breeds. We therefore go beyond previous work in two important ways. First, we modify the SMAL shape space to be more appropriate for representing dog shape. Second, we formulate novel losses that exploit information about dog breeds. In particular, we exploit the fact that dogs of the same breed have similar body shapes. We formulate a novel breed similarity loss, consisting of two parts: One term is a triplet loss, that encourages the shape of dogs from the same breed to be more similar than dogs of different breeds. The second one is a breed classification loss. With our approach we obtain 3D dogs that, compared to previous work, are quantitatively better in terms of 2D reconstruction, and significantly better according to subjective and quantitative 3D evaluations. Our work shows that a-priori side information about similarity of shape and appearance, as provided by breed labels, can help to compensate for the lack of 3D training data. This concept may be applicable to other animal species or groups of species. We call our method BARC (Breed-Augmented Regression using Classification). Our code is publicly available for research purposes at https://barc.is.tue.mpg.de/.
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Organizational Leadership and Diversity Conference Paper Unlearning the bias: An agent-based simulation for increasing diversere presentation through leadership emergence Smith, A., Heuschkel, S., Keplinger, K., Wu, C. In Proceedings of the 45th Annual Conference of the Cognitive Science Society, https://escholarship.org/uc/item/5mq9v0rm, Sydney, Australia, Proceedings of the 45th Annual Conference of the Cognitive Science Society, July 2023 (Published)
Despite increased interest in creating more diverse and inclusive organizational environments, bias exists in how we choose leaders, who we interact with, and who we consider influential. Drawing from leadership emergence theory, we investigate potential interventions that support diverse leaders. Using agent-based simulations, we model a collective search process on a fitness landscape. Agents combine individual and social learning, and are represented as a feature vector blending relevant (e.g., individual learning characteristics) and irrelevant (e.g., race or gender) features. Agents use rational principles of learning to estimate feature weights on the basis of performance predictions, which are used to dynamically define social influence in their network. We show how biases arise based on historic privilege, but can be drastically reduced through the use of an intervention (e.g. mentorship). This framework allows us to test interventions best suited for unlearning bias in favor of performance-relevant traits.
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Autonomous Learning Article Offline Diversity Maximization under Imitation Constraints Marin, V., Jin, C., Martius, G., Kolev, P. Reinforcement Learning Journal, Offline Diversity Maximization under Imitation Constraints, 3:1377-1409, July 2023 (Published)
There has been significant recent progress in the area of unsupervised skill discovery, utilizing various information-theoretic objectives as measures of diversity. Despite these advances, challenges remain: current methods require significant online interaction, fail to leverage vast amounts of available task-agnostic data and typically lack a quantitative measure of skill utility. We address these challenges by proposing a principled offline algorithm for unsupervised skill discovery that, in addition to maximizing diversity, ensures that each learned skill imitates state-only expert demonstrations to a certain degree. Our main analytical contribution is to connect Fenchel duality, reinforcement learning, and unsupervised skill discovery to maximize a mutual information objective subject to KL-divergence state occupancy constraints. Furthermore, we demonstrate the effectiveness of our method on the standard offline benchmark D4RL and on a custom offline dataset collected from a 12-DoF quadruped robot for which the policies trained in simulation transfer well to the real robotic system.
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Empirical Inference Conference Paper A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Models Stolfo, A., Jin, Z., Shridhar, K., Schölkopf, B., Sachan, M. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL), Volume 1: Long Papers:545-561, (Editors: Rogers, A. and Boyd-Graber, J. L. and Okazaki, N.), Association for Computational Linguistics, July 2023 (Published) DOI BibTeX
Robotic Materials Article A Multifunctional Soft Robotic Shape Display with High-speed Actuation, Sensing, and Control Johnson, B. K., Naris, M., Sundaram, V., Volchko, A., Ly, K., Mitchell, S. K., Acome, E., Kellaris, N., Keplinger, C., Correll, N., Humbert, J. S., Rentschler, M. E. Nature Communications, 14(1), July 2023 (Published)
Shape displays which actively manipulate surface geometry are an expanding robotics domain with applications to haptics, manufacturing, aerodynamics, and more. However, existing displays often lack high-fidelity shape morphing, high-speed deformation, and embedded state sensing, limiting their potential uses. Here, we demonstrate a multifunctional soft shape display driven by a 10 × 10 array of scalable cellular units which combine high-speed electrohydraulic soft actuation, magnetic-based sensing, and control circuitry. We report high-performance reversible shape morphing up to 50 Hz, sensing of surface deformations with 0.1 mm sensitivity and external forces with 50 mN sensitivity in each cell, which we demonstrate across a multitude of applications including user interaction, image display, sensing of object mass, and dynamic manipulation of solids and liquids. This work showcases the rich multifunctionality and high-performance capabilities that arise from tightly-integrating large numbers of electrohydraulic actuators, soft sensors, and controllers at a previously undemonstrated scale in soft robotics.
YouTube video DOI URL BibTeX
Empirical Inference Article A network approach to atomic spectra Wellnitz, D., Kekić, A., Heiss, J., Gertz, M., Weidemüller, M., Spitz, A. Journal of Physics: Complexity, 4(3), July 2023 (Published) DOI BibTeX
Social Foundations of Computation Conference Paper AI and the EU Digital Markets Act: Addressing the Risks of Bigness in Generative AI Yasar, A. G., Chong, A., Dong, E., Gilbert, T. K., Hladikova, S., Maio, R., Mougan, C., Shen, X., Singh, S., Stoica, A., Thais, S., Zilka, M. Proceedings of the 40th International Conference on Machine Learning (ICML 2023), PMLR, The Forty International Conference on Machine Learning (ICML), July 2023 (Accepted)
As AI technology advances rapidly, concerns over the risks of bigness in digital markets are also growing. The EU's Digital Markets Act (DMA) aims to address these risks. Still, the current framework may not adequately cover generative AI systems that could become gateways for AI-based services. This paper argues for integrating certain AI software as core platform services and classifying certain developers as gatekeepers under the DMA. We also propose an assessment of gatekeeper obligations to ensure they cover generative AI services. As the EU considers generative AI-specific rules and possible DMA amendments, this paper provides insights towards diversity and openness in generative AI services.
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Empirical Inference Ph.D. Thesis Advances in Algorithmic Recourse: Ensuring Causal Consistency, Fairness, & Robustness Karimi, A. ETH Zurich, Switzerland, July 2023 (Published) BibTeX
Empirical Inference Conference Paper Adversarial robustness of amortized Bayesian inference Glöckler, M., Deistler, M., Macke, J. H. Proceedings of 40th International Conference on Machine Learning (ICML) , 202:11493-11524, 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 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