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2021 Conference on Reasoning and interaction, ReInAct 2021

作者： Del-Bosque-Trevino, Jorge Hough, Julian Purver, Matthew Education Lab. Computational Linguistics Lab. Cognitive Science Research Group School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Human Interaction Lab. Computational Linguistics Lab. Cognitive Science Research Group School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Jožef Stefan Institute Computational Linguistics Lab. Cognitive Science Research Group School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom

ISBN: (纸本)9781955917070

We present a conversational management act (CMA) annotation schema for one-to-one tutorial dialogue sessions where a tutor uses an analogy to teach a student a concept. CMAs are more fine-grained sub-utterance acts compared to traditional dialogue act markup. The schema achieves an inter-annotator agreement (IAA) Cohen Kappa score of at least 0.66 across all 10 classes. We annotate a corpus of analogical episodes with the schema and develop statistical sequence models from the corpus which predict tutor content related decisions, in terms of the selection of the analogical component (AC) and tutor conversational management act (TCMA) to deploy at the current utterance, given the student’s behaviour. CRF sequence classifiers perform well on AC selection and robustly on TCMA selection, achieving respective accuracies of 61.9% and 56.3% on a cross-validation experiment over the corpus. © 2021 Association for Computational Linguistics.

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Trade-Offs Between Fairness and Privacy in Language Modeling

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arXiv 2023年

作者： Matzken, Cleo Eger, Steffen Habernal, Ivan Trustworthy Human Language Technologies Department of Computer Science Technical University of Darmstadt Germany Natural Language Learning Group Faculty of Technology Universität Bielefeld Germany

Protecting privacy in contemporary NLP models is gaining in importance. So does the need to mitigate social biases of such models. But can we have both at the same time? Existing research suggests that privacy preservation comes at the price of worsening biases in classification tasks. In this paper, we explore the extent to which this tradeoff really holds when we incorporate both privacy preservation and debiasing techniques into training text generation models. How does improving the model along one dimension affect the other dimension as well as the utility of the model? We conduct an extensive set of experiments that include bias detection, privacy attacks, language modeling, and performance on downstream tasks. © 2023, CC BY.

关键词： Economic and social effects

I³: Interactive Iterative Improvement for Few-Shot Action Segmentation

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I³: Interactive Iterative Improvement for Few-Shot Action S...

IEEE International Workshop on Robot and human Communication (ROMAN)

作者： Martina Gassen Frederic Metzler Erik Prescher Lisa Scherf Vignesh Prasad Felix Kaiser Dorothea Koert Interactive AI & Cognitive Models for Human-AI Interaction (IKIDA) TU Darmstadt Germany Centre for Cognitive Science TU Darmstadt Germany Department of Computer Science Institute for Intelligent Autonomous Systems TU Darmstadt Germany Department of Law and Economics Chair for Marketing and Human Resource Management TU Darmstadt Germany

Extracting modular segments from raw video demonstrations of high-level actions is important to understand the underlying building blocks for different tasks in human-robot interaction. While (data-hungry) supervised learning approaches for Action Segmentation show good performance when the underlying segments are predefined, their performance degrades when unseen actions are introduced on-the-go as new data samples are scarce. In this regard, Zero-and Few-Shot Learning approaches have shown good performance in generalizing to unseen examples. In Action Segmentation, where each frame needs to be labeled, annotating new data even for a few tasks can become tedious as the number of tasks scale. In this work, we propose Interactive Iterative Improvement $(I^{3})$ for Few-Shot Action Segmentation, a Semi-Supervised Interactive Meta-Learning approach for Zero-Shot Learning on unlabeled videos and Few-Shot Learning on small amounts of labeled videos. $I^{3}$ consists of a Prototypical Network model for frame-wise prediction coupled with a Hidden-Semi-Markov-Model to prevent over-segmentation. The model is iteratively improved in an interactive manner through users’ annotations provided via a webinterface. This is done in a task-agnostic manner that, in theory, can be reused for a number of different actions. Our model provides sequentially accurate segmentations using only a limited amount of labeled data which shows the efficacy of our learning approach. A lower edit distance compared to baselines indicates a lower number of required user edits making it well suited for non-expert users to smoothly provide annotations enabling them to have more control over the learned model.

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FAIR Data Point:A FAIR-Oriented Approach for Metadata Publication

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Data Intelligence 2023年第1期5卷 163-183页

作者： Luiz Olavo Bonino da Silva Santos Kees Burger Rajaram Kaliyaperumal Mark D.Wilkinson Services and Cybersecurity group Faculty of Electrical EngineeringMathematics and Computer ScienceUniversity of Twente-Enschede7513GBthe Netherlands Biosemantics group Department of Human GeneticsLeiden University Medical Center-LeidenNL 2333 ZCthe Netherlands Escuela Técnica Superior de Ingenieria Agronomica Alimentaria y de BiosistemasCentro de Biotecnologia y Genomica de Plantas UPM-INIAUniversidad Politecnica de Madrid(UPM)-Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria(INIA)MadridES 28223Spain

Metadata,data about other digital objects,play an important role in FAIR with a direct relation to all FAIR *** this paper we present and discuss the FAIR Data Point(FDP),a software architecture aiming to define a common approach to publish semantically-rich and machine-actionable metadata according to the FAIR *** present the core components and features of the FDP,its approach to metadata provision,the criteria to evaluate whether an application adheres to the FDP specifications and the service to register,index and allow users to search for metadata content of available FDPs.

关键词： FAIR FAIR data point FAIR principles Metadata Interoperability Linked data Semantic interoperability

Learning Multimodal Latent Dynamics for human-Robot interaction

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arXiv 2023年

作者： Prasad, Vignesh Heitlinger, Lea Koert, Dorothea Stock-Homburg, Ruth Peters, Jan Chalvatzaki, Georgia Department of Computer Science TU Darmstadt Germany Chair for Marketing and Human Resource Management Department of Law and Economics TU Darmstadt Germany Department of Computer Science TU Darmstadt Germany Centre for Cognitive Science TU Darmstadt Germany Germany Darmstadt Germany Interactive Robot Perception and Learning group Department of Computer Science TU Darmstadt Germany Center for Mind Brain & Behavior Uni. Marburg JLU Giessen Germany

This article presents a method for learning wellcoordinated human-Robot interaction (HRI) from human- human interactions (HHI). We devise a hybrid approach using Hidden Markov Models (HMMs) as the latent space priors for a Variational Autoencoder to model a joint distribution over the interacting agents. We leverage the interaction dynamics learned from HHI to learn HRI and incorporate the conditional generation of robot motions from human observations into the training, thereby predicting more accurate robot trajectories. The generated robot motions are further adapted with Inverse Kinematics to ensure the desired physical proximity with a human, combining the ease of joint space learning and accurate task space reachability. For contact-rich interactions, we modulate the robot's stiffness using HMM segmentation for a compliant interaction. We verify the effectiveness of our approach deployed on a humanoid robot via a user study. Our method generalizes well to various humans despite being trained on data from just two humans. We find that Users perceive our method as more human-like, timely, and accurate and rank our method with a higher degree of preference over other baselines. © 2023, CC BY-NC-SA.

关键词： Anthropomorphic robots

Surface defect identification using Bayesian filtering on a 3D mesh

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arXiv 2025年

作者： Vedove, Matteo Dalle Bonetto, Matteo Lamon, Edoardo Palopoli, Luigi Saveriano, Matteo Fontanelli, Daniele Department of Industrial Engineering Università di Trento Trento Italy DRIM Ph.D. of national interest in Robotics and Intelligent Machines Italy Department of Information Engineering and Computer Science Università di Trento Trento Italy Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy

This paper presents a CAD-based approach for automated surface defect detection. We leverage the a-priori knowledge embedded in a CAD model and integrate it with point cloud data acquired from commercially available stereo and depth cameras. The proposed method first transforms the CAD model into a high-density polygonal mesh, where each vertex represents a state variable in 3D space. Subsequently, a weighted least squares algorithm is employed to iteratively estimate the state of the scanned workpiece based on the captured point cloud measurements. This framework offers the potential to incorporate information from diverse sensors into the CAD domain, facilitating a more comprehensive analysis. Preliminary results demonstrate promising performance, with the algorithm achieving convergence to a sub-millimeter standard deviation in the region of interest using only approximately 50 point cloud samples. This highlights the potential of utilising commercially available stereo cameras for high-precision quality control applications. © 2025, CC BY-SA.

关键词： Mesh generation

Surface defect identification using Bayesian filtering on a 3D mesh

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Measurement: Sensors 2025年 38卷

作者： Dalle Vedove, Matteo Bonetto, Matteo Lamon, Edoardo Palopoli, Luigi Saveriano, Matteo Fontanelli, Daniele Department of Industrial Engineering Universitá di Trento Trento Italy DRIM Ph.D. of National Interest in Robotics and Intelligent Machines Italy Department of Information Engineering and Computer Science Universitá di Trento Trento Italy Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy

关键词： Mesh generation

Augmenting Scientific Creativity with Retrieval across Knowledge Domains

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arXiv 2022年

作者： Kang, Hyeonsu B. Mysore, Sheshera Huang, Kevin Chang, Haw-Shiuan Prein, Thorben McCallum, Andrew Kittur, Aniket Olivetti, Elsa Human-Computer Interaction Institute CMU PA United States Manning College of Information and Computer Sciences UMASS AmherstMA United States Department of Materials Science and Engineering MIT MA United States

Exposure to ideas in domains outside a scientist’s own may benefit her in reformulating existing research problems in novel ways and discovering new application domains for existing solution ideas. While improved performance in scholarly search engines can help scientists efficiently identify relevant advances in domains they may already be familiar with, it may fall short of helping them explore diverse ideas outside such domains. In this paper we explore the design of systems aimed at augmenting the end-user ability in cross-domain exploration with flexible query specification. To this end, we develop an exploratory search system in which end-users can select a portion of text core to their interest from a paper abstract and retrieve papers that have a high similarity to the user-selected core aspect but differ in terms of domains. Furthermore, end-users can ‘zoom in’ to specific domain clusters to retrieve more papers from them and understand nuanced differences within the clusters. Our case studies with scientists uncover opportunities and design implications for systems aimed at facilitating cross-domain exploration and inspiration. © 2022, CC BY.

关键词： Search engines

Towards Robotised Palpation for Cancer Detection through Online Tissue Viscoelastic Characterisation with a Collaborative Robotic Arm

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Towards Robotised Palpation for Cancer Detection through Onl...

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Luca Beber Edoardo Lamon Giacomo Moretti Daniele Fontanelli Matteo Saveriano Luigi Palopoli Department of Information Engineering and Computer Science Università di Trento Trento Italy DRIM Ph.D. of National Interest in Robotics and Intelligent Machines Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy Department of Industrial Engineering Università di Trento Trento Italy

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This paper introduces a new method for online estimating the penetration of the end-effector and the viscoelastic properties of a soft body, through palpation exams using a collaborative robotic arm. The estimator is based on the dimensionality reduction method that simplifies the nonlinear Hunt-Crossley model. In addition, in our algorithm, the model parameters can be found without a force sensor, leveraging only the robotic arm controller data. An extended Kalman filter is employed to achieve online estimation, which embeds the dynamic contact model. The algorithm is tested with various types of silicone, a material that resembles biological tissues, including samples with hard intrusions to simulate cancerous cells within a softer tissue. The results indicate that this technique can accurately determine the model parameters and estimate the penetration of the end-effector into the soft body. These promising preliminary results demonstrate robots’ potential to be an effective tool for early-stage cancer diagnostics.

关键词： Dimensionality reduction Biological system modeling Heuristic algorithms Biological tissues Estimation Collaboration Robot sensing systems Information filters End effectors Kalman filters

A Passivity-Based Variable Impedance Controller for Incremental Learning of Periodic Interactive Tasks

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A Passivity-Based Variable Impedance Controller for Incremen...

IEEE International Conference on Automation science and Engineering (CASE)

作者： Matteo Dalle Vedove Edoardo Lamon Daniele Fontanelli Luigi Palopoli Matteo Saveriano Department of Industrial Engineering Università di Trento Trento Italy DRIM Ph.D. of National Interest in Robotics and Intelligent Machines Department of Information Engineering and Computer Science Università di Trento Trento Italy Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In intelligent manufacturing, robots are asked to dynamically adapt their behaviours without reducing productivity. human teaching, where an operator physically interacts with the robot to demonstrate a new task, is a promising strategy to quickly and intuitively reconfigure the production line. However, physical guidance during task execution poses challenges in terms of both operator safety and system usability. In this paper, we solve this issue by designing a variable impedance control strategy that regulates the interaction with the environment and the physical demonstrations, explicitly preventing at the same time passivity violations. We derive constraints to limit not only the exchanged energy with the environment but also the exchanged power, resulting in smoother interactions. By monitoring the energy flow between the robot and the environment, we are able to distinguish between disturbances (to be rejected) and physical guidance (to be accomplished), enabling smooth and controlled transitions from teaching to execution and vice versa. The effectiveness of the proposed approach is validated in wiping tasks with a real robotic manipulator.

关键词： Surface impedance Incremental learning Tracking Education End effectors Safety Impedance Usability Robots Oscillators