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16th International Conference on Social robotics, ICSR + AI 2024

作者： Marusic, Aleksa Mai Nguyen, Sao Tapus, Adriana Autonomous Systems and Robotics Lab/U2IS ENSTA Paris Institut Polytechnique de Paris Paris France

ISBN: (纸本)9789819635245

Studies indicate that physical rehabilitation exercises recommended by healthcare professionals can enhance physical function, improve quality of life, and promote independence for physically disabled individuals. In response to the lack of immediate expert feedback on performed actions, developing an automated system for monitoring such actions and providing feedback is very much needed. In this work, we focus on skeleton-based exercise assessment, which uses skeleton data to evaluate human motion and provide a score on how well a patient performed a movement. There are several approaches to this issue, with Spatio Temporal Graph Convolutional Networks (GCN) being among the most recent. GCNs model skeleton data as graphs and utilize temporal and spatial convolutions to capture relationships between joints more effectively than previous methods. In this research, we propose a new Transformer based model, PhysioFormer. It is inspired by SkateFormer method for human action recognition, with enhanced structure to fit the task of physical rehabilitation assessment. The model leverages skeletal-temporal self-attention across different groups based on relations between joints. The evaluation is done on the KIMORE, UI-PRMD, and KERAAL datasets, benchmark datasets that provide skeleton data captured by Kinect motion system. Our model is surpassing state-of-the-art methods significantly. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Musculoskeletal system

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Using a Bayesian Network to Predict User Trust in Teleoperation Robots 16th

Using a Bayesian Network to Predict User Trust in Teleope...

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16th International Conference on Social robotics, ICSR + AI 2024

作者： Cárdenas, Juan José García Tapus, Adriana Autonomous Systems and Robotics Lab/U2IS ENSTA Paris Institute Polytechnique de Paris Paris France

ISBN: (纸本)9789819635245

Trust plays a crucial role in human-robot interaction (HRI), especially in teleoperation scenarios where users control robots remotely. This paper develops a static trust prediction model using a Bayesian approach to improve system design, user experience, and robotic reliability. By integrating prior knowledge with empirical data, we constructed a Bayesian model that estimates trust levels in teleoperated robotic systems. Our model incorporates physiological measures and task performance data to provide a comprehensive trust prediction framework. We evaluated the model’s performance using precision, recall, and F1 score, achieving high precision (0.92), good recall (0.84), and a balanced F1 score (0.79). These metrics demonstrate the model’s effectiveness in accurately predicting trust levels in teleoperated robotic systems. The results underscore the importance of trustworthiness in maintaining user confidence and improving system interactions. This study highlights the potential of Bayesian models in enhancing the reliability and user experience of teleoperated robots, offering valuable insights for future developments in HRI research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Human robot interaction

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ActNetFormer: Transformer-ResNet Hybrid Method for Semi-supervised Action Recognition in Videos 27th

ActNetFormer: Transformer-ResNet Hybrid Method for Semi-supe...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Dass, Sharana Dharshikgan Suresh Barua, Hrishav Bakul Krishnasamy, Ganesh Paramesran, Raveendran Phan, Raphaël C.-W. School of Information Technology Monash University Subang Jaya Malaysia Robotics and Autonomous Systems Lab TCS Research Kolkata India

ISBN: (纸本)9783031783531

Human action or activity recognition in videos is a fundamental task in computer vision with applications in surveillance and monitoring, self-driving cars, sports analytics, human-robot interaction and many more. Traditional supervised methods require large annotated datasets for training, which are expensive and time-consuming to acquire. This work proposes a novel approach using cross-architecture pseudo-labeling with contrastive learning for semi-supervised action recognition. Our framework leverages both labeled and unlabeled data to robustly learn action representations in videos, combining pseudo-labeling with contrastive learning for effective learning from both types of samples. We introduce a novel cross-architecture approach where 3D Convolutional Neural Networks (3D CNNs) and video transformers (VIT) are utilized to capture different aspects of action representations;hence we call it ActNetFormer. The 3D CNNs excel at capturing spatial features and local dependencies in the temporal domain, while VIT excels at capturing long-range dependencies across frames. By integrating these complementary architectures within the ActNetFormer framework, our approach can effectively capture both local and global contextual information of an action. This comprehensive representation learning enables the model to achieve better performance in semi-supervised action recognition tasks by leveraging the strengths of each of these architectures. Experimental results on standard action recognition datasets demonstrate that our approach performs better than the existing methods, achieving state-of-the-art performance with only a fraction of labeled data. The official website of this work is available at: https://***/rana2149/ActNetFormer. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Diaphragmatic Breathing Guidance Using a Humanoid Robot with a Soft Robotic Haptic Interface for Anxiety Reduction 16th

Diaphragmatic Breathing Guidance Using a Humanoid Robot wit...

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16th International Conference on Social robotics, ICSR + AI 2024

作者： Saood, Adnan Liu, Yang Tapus, Adriana Autonomous Systems and Robotics Lab/U2IS ENSTA Paris Institute Polytechnique de Paris Palaiseau France Information Processing and Communications Laboratory/LTCI Telecom Paris Institute Polytechnique de Paris Palaiseau France

ISBN: (纸本)9789819635245

Anxiety disorders profoundly affect individuals’ well-being and daily lives, manifesting in both physical and emotional symptoms. Although pharmacological treatments and cognitive-behavioral therapies are commonly used, diaphragmatic breathing exercises have shown promise as a non-pharmacological intervention. This work presents an innovative device: a silicone haptic interface mounted on the chest of a humanoid robot, designed to simulate human heartbeat and breathing. This device, employing a baromorphic system design, offers a good representation of human respiration. Our study assesses the effectiveness of this haptic interface in guiding diaphragmatic breathing exercises to reduce anxiety levels. Eighteen participants underwent a standard anxiety-inducing procedure followed by a guided diaphragmatic breathing exercise with the robot. They were divided into two groups: ten with the haptic interface and eight without it. Both groups experienced similar reductions in anxiety levels. Those using the haptic interface reported the robot as more natural and supportive in post-experiment interviews. This exploratory study illustrates the potential of social robots with haptic interfaces to enhance therapeutic practices for anxiety reduction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Anthropomorphic robots

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InstantPose: Zero-Shot Instance-Level 6D Pose Estimation from a Single View

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IEEE robotics and Automation Letters 2025年第6期10卷 6023-6030页

作者： Felice, Francesco Di Remus, Alberto Gasperini, Stefano Busam, Benjamin Ott, Lionel Thalhammer, Stefan Tombari, Federico Avizzano, Carlo Alberto Scuola Superiore sant'anna Department of Excellence in Robotics & AI Mechanical Intelligence Institute Pisa Italy Leonardo S.p.A. Robotics Laboratory Innovation Hub & Intellectual Property Genoa Italy Technical University of Munich TUM School of Computation Information and Technology Germany ETH Zurich Department of Mechanical and Process Engineering Autonomous Systems Lab Switzerland UAS Technikum Vienna Industrial Engineering Department Austria Google Zurich Switzerland

Object pose estimation using visual data is crucial for robotic interaction with the environment. Many existing instance-level methods are restricted by their requirements for 3D CAD models or multiple object views, which limits their flexibility and generalizability. Overcoming this limitation is critical to enhance the adaptability of pose estimation systems. In this work, a novel pipeline that leverages recent advances in reconstruction techniques is presented to address these challenges. To this end, Large Reconstruction Models (LRM) represent an advanced neural architecture capable of generating 3D object models from a limited set of views. Nevertheless, the resulting 3D models often lack relevant geometric and texture details due to insufficient input information. This research presents InstantPose, an innovative zero-shot instance-level pose estimation method that, building upon LRM, can determine the pose of unseen objects using as little as a single unposed RGB reference and RGB-D query images. Extensive experiments demonstrate that InstantPose achieves remarkable performance in object pose estimation on the YCB-V dataset, compared to methods conceived to rely on a geometrically perfect object's model. Furthermore, the 6D pose provided through the presented approach facilitates successful object grasping, highlighting its practical utility in robotic manipulation tasks. © 2016 IEEE.

关键词： 3D reconstruction

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CERBERUS: autonomous Legged and Aerial Robotic Exploration in the Tunnel and Urban Circuits of the DARPA Subterranean Challenge

Field Robotics

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Field robotics 2025年 2卷 274-324页

作者： Marco Tranzatto Frank Mascarich Lukas Bernreiter Carolina Godinho Marco Camurri Shehryar Khattak Tung Dang Victor Reijgwart Johannes Löje David Wisth Samuel Zimmermann Huan Nguyen Marius Fehr Lukas Solanka Russell Buchanan Marko Bjelonic Nikhil Khedekar Mathieu Valceschini Fabian Jenelten Mihir Dharmadhikari Timon Hornberger Paolo De Petris Lorenz Wellhausen Mihir Kulkami Takahiro Miki Satchel Hirsch Markus Montenegro Christos Papachristos Fabian Tresoldi Jan Carius Giorgio Valsecchi Joonho Lee Konrad Meyer Xiangyu Wu Juan Nieto Andy Smith Marco Hutter Roland Siegwart Mark Mueller Maurice Fallon Kostas Alexis Robotic Systems Lab ETH Zurich Autonomous Robots Lab University of Nevada Reno | Norwegian University of Science and Technology Autonomous Systems Lab ETH Zurich Flyability Oxford Robotics Institute University of Oxford University of California Berkeley Sierra Nevada Corporation Direct correspondence to Marco Tranzatto

autonomous exploration of subterranean environments constitutes a major frontier for robotic systems, as underground settings present key challenges that can render robot autonomy hard to achieve. This problem has motivated the DARPA Subterranean Challenge, where teams of robots search for objects of interest in various underground environments. In response, we present the CERBERUS system-of-systems, as a unified strategy for subterranean exploration using legged and flying robots. Our proposed approach relies on ANYmal quadraped as primary robots, exploiting their endurance and ability to traverse challenging terrain. For aerial robots, we use both conventional and collision-tolerant multirotors to explore spaces too narrow or otherwise unreachable by ground systems. Anticipating degraded sensing conditions, we developed a complementary multimodal sensor-fusion approach, utilizing camera, LiDAR, and inertial data for resilient robot pose estimation. Individual robot pose estimates are refined by a centralized multi-robot map-optimization approach to improve the reported location accuracy of detected objects of interest in the DARPA-defined coordinate frame. Furthermore, a unified exploration path-planning policy is presented to facilitate the autonomous operation of both legged and aerial robots in complex underground networks. Finally, to enable communication among team agents and the base station, CERBERUS utilizes a ground rover with a high-gain antenna and an optical fiber connection to the base station and wireless “breadcrumb” nodes deployed by the legged robots. We report results from the CERBERUS system-of-systems deployment at the DARPA Subterranean Challenge's Tunnel and Urban Circuit events, along with the current limitations and the lessons learned for the benefit of the community.

关键词： Robots Robot kinematics Legged locomotion Circuits Robot sensing systems Collision avoidance Base stations Location awareness autonomous aerial vehicles Pipelines

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MD-BERT: Action Recognition in Dark Videos via Dynamic Multi-Stream Fusion and Temporal Modeling

arXiv

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arXiv 2025年

作者： Dass, Sharana Dharshikgan Suresh Barua, Hrishav Bakul Krishnasamy, Ganesh Paramesran, Raveendran Phan, Raphaël C.-W. School of Information Technology Monash University Malaysia Faculty of Information Technology Monash University Australia Robotics and Autonomous Systems Lab TCS Research India

Action recognition in dark, low-light (underexposed) or noisy videos is a challenging task due to visibility degradation, which can hinder critical spatiotemporal details. This paper proposes MD-BERT, a novel multi-stream approach that integrates complementary pre-processing techniques such as gamma correction and histogram equalization alongside raw dark frames to address these challenges. We introduce the Dynamic Feature Fusion (DFF) module, extending existing attentional fusion methods to a three-stream setting, thereby capturing fine-grained and global contextual information across different brightness and contrast enhancements. The fused spatiotemporal features are then processed by a BERT-based temporal model, which leverages its bidirectional self-attention to effectively capture long-range dependencies and contextual relationships across frames. Extensive experiments on the ARID V1.0 and ARID V1.5 dark video datasets show that MD-BERT outperforms existing methods, establishing a new state-of-the-art performance. Ablation studies further highlight the individual contributions of each input stream and the effectiveness of the proposed DFF and BERT modules. The official website of this work is available at: https://***/HrishavBakulBarua/DarkBERTMSC Codes Artificial intelligence, Computer vision, Machine learning, Deep learning, Human-computer Interaction Copyright © 2025, The Authors. All rights reserved.

关键词： Convolutional neural networks

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FlowMP: Learning Motion Fields for Robot Planning with Conditional Flow Matching

arXiv

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arXiv 2025年

作者： Nguyen, Khang Le, An T. Pham, Tien Huber, Manfred Peters, Jan Vu, Minh Nhat Learning and Adaptive Robotics Lab University of Texas Arlington United States Intelligent Autonomous Systems Lab TU Darmstadt Germany Cognitive Robotics Lab University of Manchester United Kingdom SAIROL Darmstadt Germany Hessian.AI Darmstadt Germany TU Wien Vienna Austria GmbH Vienna Austria

Prior flow matching methods in robotics have primarily learned velocity fields to morph one distribution of trajectories into another. In this work, we extend flow matching to capture second-order trajectory dynamics, incorporating acceleration effects either explicitly in the model or implicitly through the learning objective. Unlike diffusion models, which rely on a noisy forward process and iterative denoising steps, flow matching trains a continuous transformation (flow) that directly maps a simple prior distribution to the target trajectory distribution without any denoising procedure. By modeling trajectories with second-order dynamics, our approach ensures that generated robot motions are smooth and physically executable, avoiding the jerky or dynamically infeasible trajectories that first-order models might produce. We empirically demonstrate that this second-order conditional flow matching yields superior performance on motion planning benchmarks, achieving smoother trajectories and higher success rates than baseline planners. These findings highlight the advantage of learning acceleration-aware motion fields, as our method outperforms existing motion planning methods in terms of trajectory quality and planning success. Copyright © 2025, The Authors. All rights reserved.

关键词： Trajectories

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Enabling Service Robots to Open Self-Closing Doors using Deep RL and Generative Models

Research Square

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Research Square 2025年

作者： Sun, Yufeng Zhang, Lin Ma, Ou Intelligent Robotics and Autonomous Systems Lab University of Cincinnati CincinnatiOH45221 United States Department of Physics and Astronomy University of Central Arkansas 201 Donaghey Avenue ConwayAR72035 United States

Various mobile service robots have been applied in human-centered indoor environments such as hospitals, schools, workshops, and office buildings for tasks like food or material delivery, surveillance, cleaning, and disinfection. However, they still require human assistance to access rooms with doors, particularly self-closing doors. In this paper, we propose a deep reinforcement learning (DRL)-based solution to enhance existing service robots, enabling them to autonomously open self-closing doors using a simple and inexpensive assistive device. This device is utilized to unlatch doors through the integration of the latest object detection and sensor technologies. Focusing on pulling open self-closing doors, we train an end-to-end control policy in a simplified simulation environment and deploy it to a real-world robot. Additionally, generative models, a variational autoenoder (VAE) and a cycle-consistent adversarial network (CycleGAN), are incorporated into both the training and deployment stages to improve the robustness of the door-opening control policy. The proposed solution for opening self-closing doors was validated in both simulation and real-world experiments. © 2025, CC BY.

关键词： Reinforcement learning

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A 3-Step Optimization Framework with Hybrid Models for a Humanoid Robot's Jump Motion

arXiv

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arXiv 2025年

作者： Qi, Haoxiang Yu, Zhangguo Chen, Xuechao Liu, Yaliang Yi, Chuanku Dong, Chencheng Meng, Fei Huang, Qiang The Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing100081 China The International Joint Research Laboratory of Biomimetic Robots and Systems Ministry of Education Beijing100081 China The National Key Lab of Autonomous Intelligent Unmanned Systems Beijing100081 China

High dynamic jump motions are challenging tasks for humanoid robots to achieve environment adaptation and obstacle crossing. The trajectory optimization is a practical method to achieve high-dynamic and explosive jumping. This paper proposes a 3-step trajectory optimization framework for generating a jump motion for a humanoid robot. To improve iteration speed and achieve ideal performance, the framework comprises three sub-optimizations. The first optimization incorporates momentum, inertia, and center of pressure (CoP), treating the robot as a static reaction momentum pendulum (SRMP) model to generate corresponding trajectories. The second optimization maps these trajectories to joint space using effective Quadratic Programming (QP) solvers. Finally, the third optimization generates whole-body joint trajectories utilizing trajectories generated by previous parts. With the combined consideration of momentum and inertia, the robot achieves agile forward jump motions. A simulation and experiments (Fig. 1) of forward jump with a distance of 1.0 m and 0.5 m height are presented in this paper, validating the applicability of the proposed framework. Note to Practitioners-The motivation of this paper stems from the need to improve jumping performance of humanoid robots. By comprehensively considering factors such as robot posture, centroidal angular momentum, and landing foot placement, the algorithm enhances the robot's ability to navigate complex environments. This capability is crucial for applications that require overcoming obstacles, such as in search and rescue or inspection tasks. Improved jumping ability can significantly boost environmental adaptability, allowing robots to perform effectively in diverse conditions, and it also represents an exploration of the high-dynamic motion capabilities of humanoid robots. Future research will focus on integrating visual and perceptual information to enhance decision-making. Copyright © 2025, The Authors. All rights reserve

关键词： Anthropomorphic robots

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