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Robot Learning

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2022

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Haptic Intelligence Autonomous Learning Empirical Inference Miscellaneous Demonstration: Minsight - A Soft Vision-Based Tactile Sensor for Robotic Fingertips Andrussow, I., Sun, H., Martius, G., Kuchenbecker, K. J. Hands-on demonstration presented at the Conference on Robot Learning (CoRL), Munich, Germany, November 2024 (Published)
Beyond vision and hearing, tactile sensing enhances a robot's ability to dexterously manipulate unfamiliar objects and safely interact with humans. Giving touch sensitivity to robots requires compact, robust, affordable, and efficient hardware designs, especially for high-resolution tactile sensing. We present a soft vision-based tactile sensor engineered to meet these requirements. Comparable in size to a human fingertip, Minsight uses machine learning to output high-resolution directional contact force distributions at 60 Hz. Minsight's tactile force maps enable precise sensing of fingertip contacts, which we use in this hands-on demonstration to allow a 3-DoF robot arm to physically track contact with a user's finger. While observing the colorful image captured by Minsight's internal camera, attendees can experience how its ability to detect delicate touches in all directions facilitates real-time robot interaction.
BibTeX
Empirical Inference Miscellaneous Evidence for eccentricity in the population of binary black holes observed by LIGO-Virgo-KAGRA Gupte, N., Ramos-Buades, A., Buonanno, A., Gair, J., Miller, M. C., Dax, M., Green, S. R., Pürrer, M., Wildberger, J., Macke, J. H., Romero-Shaw, I. M., Schölkopf, B. April 2024 (Published) URL BibTeX
Empirical Inference Miscellaneous Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models Rajendran, G., Buchholz, S., Aragam, B., Schölkopf, B., Ravikumar, P. 2024 (Published) arXiv BibTeX
Empirical Inference Miscellaneous Multi-Armed Bandits and Quantum Channel Oracles Buchholz, S., Kübler, J. M., Schölkopf, B. 2024 (Published) arXiv BibTeX
Empirical Inference Miscellaneous Analyzing Human Questioning Behavior and Causal Curiosity through Natural Queries Ceraolo, R., Kharlapenko, D., Khan, A., Reymond, A., Mihalcea, R., Sachan, M., Schölkopf, B., Jin, Z. 2024 (Published) URL BibTeX
Empirical Inference Miscellaneous Language Model Alignment in Multilingual Trolley Problems Jin, Z., Levine, S., Kleiman-Weiner, M., Piatti, G., Liu, J., Gonzalez, F., Ortu, F., Strausz, A., Sachan, M., Mihalcea, R., Choi, Y., Schölkopf, B. 2024 (Published) URL BibTeX
Empirical Inference Miscellaneous Borges und die Künstliche Intelligenz Bottou, L., Schölkopf, B. 2023, published in Frankfurter Allgemeine Zeitung, 18 December 2023, Nr. 294 (Published) PDF BibTeX
Haptic Intelligence Autonomous Learning Empirical Inference Miscellaneous A Sequential Group VAE for Robot Learning of Haptic Representations Richardson, B. A., Kuchenbecker, K. J., Martius, G. 1-11, Workshop paper (8 pages) presented at the CoRL Workshop on Aligning Robot Representations with Humans, Auckland, New Zealand, December 2022 (Published)
Haptic representation learning is a difficult task in robotics because information can be gathered only by actively exploring the environment over time, and because different actions elicit different object properties. We propose a Sequential Group VAE that leverages object persistence to learn and update latent general representations of multimodal haptic data. As a robot performs sequences of exploratory procedures on an object, the model accumulates data and learns to distinguish between general object properties, such as size and mass, and trial-to-trial variations, such as initial object position. We demonstrate that after very few observations, the general latent representations are sufficiently refined to accurately encode many haptic object properties.
URL BibTeX
Haptic Intelligence Autonomous Learning Empirical Inference Miscellaneous A Soft Vision-Based Tactile Sensor for Robotic Fingertip Manipulation Andrussow, I., Sun, H., Kuchenbecker, K. J., Martius, G. Workshop paper (1 page) presented at the IROS Workshop on Large-Scale Robotic Skin: Perception, Interaction and Control, Kyoto, Japan, October 2022 (Published)
For robots to become fully dexterous, their hardware needs to provide rich sensory feedback. High-resolution haptic sensing similar to the human fingertip can enable robots to execute delicate manipulation tasks like picking up small objects, inserting a key into a lock, or handing a cup of coffee to a human. Many tactile sensors have emerged in recent years; one especially promising direction is vision-based tactile sensors due to their low cost, low wiring complexity and high-resolution sensing capabilities. In this work, we build on previous findings to create a soft fingertip-sized tactile sensor. It can sense normal and shear contact forces all around its 3D surface with an average prediction error of 0.05 N, and it localizes contact on its shell with an average prediction error of 0.5 mm. The software of this sensor uses a data-efficient machine-learning pipeline to run in real time on hardware with low computational power like a Raspberry Pi. It provides a maximum data frame rate of 60 Hz via USB.
URL BibTeX
Empirical Inference Miscellaneous Inconsistent Few-Shot Relation Classification via Cross-Attentional Prototype Networks with Contrastive Learning Wang, H., Jin, Z., Cao, J., Fung, G. P. C., Wong, K. 2022 (Published) arXiv BibTeX
Empirical Inference Robotics Miscellaneous A Robot Cluster for Reproducible Research in Dexterous Manipulation Wüthrich*, M., Widmaier*, F., Bauer*, S., Funk, N., Urain, J., Peters, J., Watson, J., Chen, C., Srinivasan, K., Zhang, J., Zhang, J., Walter, M. R., Madan, R., Schaff, C., Maeda, T., Yoneda, T., Yarats, D., Allshire, A., Gordon, E. K., Bhattacharjee, T., et al. 2021, *equal contribution (Published) arXiv BibTeX
Empirical Inference Miscellaneous A model of Ponto-Geniculo-Occipital waves supports bidirectional control of cortical plasticity across sleep-stages Shao, K., Villegas, J. F. R., Logothetis, N. K., Besserve, M. 2021 (In preparation) bioRxiv DOI BibTeX
Empirical Inference Miscellaneous Distributionally Robust Trajectory Optimization Under Uncertain Dynamics via Relative-Entropy Trust Regions Abdulsamad, H., Dorau, T., Belousov, B., Zhu, J., Peters, J. 2021 (Published) arXiv BibTeX
Empirical Inference Miscellaneous NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments Prabhoo, S., Bauer, S., Schwab, P. 2021 (Published) arXiv BibTeX
Empirical Inference Miscellaneous On the Impact of Stable Ranks in Deep Nets Georgiev, B., Franken, L., Mukherjee, M., Arvanitidis, G. 2021 (Published) arXiv BibTeX
Empirical Inference Miscellaneous Pulling back information geometry Arvanitidis, G., González Duque, M., Pouplin, A., Kalatzis, D., Hauberg, S. 2021 (Published) arXiv BibTeX
Empirical Inference Miscellaneous Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger Allshire, A., Mittal, M., Lodaya, V., Makoviychuk, V., Makoviichuk, D., Widmaier, F., Wüthrich, M., Bauer, S., Handa, A., Garg, A. 2021 arXiv BibTeX
Empirical Inference Miscellaneous Assaying Large-scale Testing Models to Interpret Covid-19 Case Numbers. A Cross-country Study Besserve, M., Buchholz, S., Schölkopf, B. 2020 (Submitted) arXiv BibTeX
Empirical Inference Miscellaneous Causal learning with sufficient statistics: an information bottleneck approach Chicharro, D., Besserve, M., Panzeri, S. 2020 (Submitted) arXiv BibTeX
Empirical Inference Miscellaneous Dimensionality Reduction of Movement Primitives in Parameter Space Tosatto, S., Stadtmueller, J., Peters, J. 2020 (Published) arXiv BibTeX
Empirical Inference Miscellaneous Is Independence all you need? On the Generalization of Representations Learned from Correlated Data Träuble, F., Creager, E., Kilbertus, N., Goyal, A., Locatello, F., Schölkopf, B., Bauer, S. 2020 (Published) URL BibTeX
Empirical Inference Miscellaneous Learning Neural Causal Models from Unknown Interventions Ke, R., Bilaniuk, O., Goyal, A., Bauer, S., Larochelle, H., Schölkopf, B., Mozer, M. C., Pal, C., Bengio, Y. 2020 (Published) arXiv BibTeX
Empirical Inference Miscellaneous ABCDP: Approximate Bayesian Computation Meets Differential Privacy Park, M., Jitkrittum, W. 2019 (Submitted) arXiv BibTeX
Empirical Inference Miscellaneous Identifying Causal Structure in Large-Scale Kinetic Systems Pfister, N., Bauer, S., Peters, J. 2019 (Published) URL BibTeX
Empirical Inference Miscellaneous Learning Algorithmic Solutions to Symbolic Planning Tasks with a Neural Computer Architecture Tanneberg, D., Rueckert, E., Peters, J. 2019 (Published) arXiv BibTeX
Empirical Inference Miscellaneous Die kybernetische Revolution Schölkopf, B. S{\"u}ddeutsche Zeitung, 2018, (15-Mar-2018) (Published) URL BibTeX
Empirical Inference Miscellaneous In-Hand Object Stabilization by Independent Finger Control Veiga, F. F., Edin, B. B., Peters, J. 2018 (Published) arXiv BibTeX
Empirical Inference Probabilistic Numerics Statistical Learning Theory Miscellaneous Large sample analysis of the median heuristic Garreau, D., Jitkrittum, W., Kanagawa, M. 2018 (In preparation) arXiv BibTeX
Empirical Inference Miscellaneous From Optimal Transport to Generative Modeling: the VEGAN cookbook Bousquet, O., Gelly, S., Tolstikhin, I., Simon-Gabriel, C. J., Schölkopf, B. 2017 (Published) arXiv follow-up paper BibTeX
Empirical Inference Probabilistic Numerics Miscellaneous Probabilistic Active Learning of Functions in Structural Causal Models Rubenstein, P. K., Tolstikhin, I., Hennig, P., Schölkopf, B. 2017 (Published) Arxiv BibTeX
Empirical Inference Miscellaneous The right tool for the right question — beyond the encoding versus decoding dichotomy Weichwald, S., Grosse-Wentrup, M. 2017 (Published) ArXiv PDF BibTeX
Empirical Inference Miscellaneous f-Divergence constrained policy improvement Belousov, B., Peters, J. 2017 (Published) arXiv BibTeX
Empirical Inference Miscellaneous A note on the expected minimum error probability in equientropic channels Weichwald, S., Fomina, T., Schölkopf, B., Grosse-Wentrup, M. 2016 (Published) PDF Arxiv BibTeX
Empirical Inference Miscellaneous Can Modular Finger Control for In-Hand Object Stabilization be accomplished by Independent Tactile Feedback Control Laws? Veiga, F., Peters, J. 2016 (Published) arXiv BibTeX
Empirical Inference Miscellaneous Empirical Inference (2010-2015) Scientific Advisory Board Report, 2016 (Published) pdf BibTeX
Empirical Inference Miscellaneous Unsupervised Domain Adaptation in the Wild : Dealing with Asymmetric Label Set Mittal, A., Raj, A., Namboodiri, V. P., Tuytelaars, T. 2016 (Published) Arxiv BibTeX
Empirical Inference Miscellaneous Mathematik der Wahrnehmung: Wendepunkte Wichman, F., Ernst, M. Akademische Mitteilungen zw{\"o}lf: F{\"u}nf Sinne, 32-37, 2007 BibTeX
Empirical Inference Miscellaneous Statistische Lerntheorie und Empirische Inferenz Schölkopf, B. Jahrbuch der Max-Planck-Gesellschaft, 2004:377-382, 2004
Statistical learning theory studies the process of inferring regularities from empirical data. The fundamental problem is what is called generalization: how it is possible to infer a law which will be valid for an infinite number of future observations, given only a finite amount of data? This problem hinges upon fundamental issues of statistics and science in general, such as the problems of complexity of explanations, a priori knowledge, and representation of data.
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