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IEEE Conference on Aerospace

作者： Alexis Lopez Olaoluwayimika Olugbenle Michael C.F. Bazzocchi Department of Mechanical and Aerospace Engineering Astronautics and Robotics Laboratory (ASTRO Lab) Clarkson University Potsdam NY USA Department of Electrical and Computer Engineering Astronautics and Robotics Laboratory (ASTRO Lab) Clarkson University Potsdam NY USA Department of Mechanical and Aerospace Engineering Astronautics and Robotics Laboratory (ASTRO Lab) Clarkson University Potsdam NY Department of Earth and Space Science and Engineering York University Toronto Canada

The investigation and development of space-based food production systems are essential to improve the reliability and availability of fresh sustenance for astronauts. With their compact size and low-cost production, CubeSats can provide a unique platform for plant-based in-space experiments. Additionally, the combination of CubeSats and computer vision can allow for monitoring the health of plants during their growth cycles. This paper investigates the electronics and data handling of a crop growth module, otherwise referred to as an environmental monitoring and control subsystem (EMCS), as well as the integration of computer vision techniques for plant growth and development. Using the Otsu thresholding and holistically-nested edge detection algorithms, image segmentation and edge detection were performed, respectively. A support vector machine (SVM) was also employed to classify foliage and provide feedback on the plant’s health. The results from the system show that the computer vision approach can accurately predict the health of the plants based on color and texture. This study builds a foundation for future plant health monitoring research in deep space environments.

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Towards Prediction of Motor Interference during synchronous Human-Robot Arm Movements Using Subjective Ratings of Anthropomorphism

Towards Prediction of Motor Interference during synchronous ...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Mertcan Kaya Kolja Kühnlenz Department of Electrical Engineering and Computer Science Robotics Research Lab Coburg University of Applied Sciences and Arts Coburg Germany

This paper reports a failed attempt to predict deviations of human arm movements from task elicited by perception of simultaneous robot arm movements (motor interference) using subjective ratings of anthropomorphism. This motor interference effect is known to result from unconscious imitation of movements of a human-like counterpart due to mirror-neuron activation. A study is conducted, with test participants and a robot conducting vertical and horizontal arm movements in front of each other in all combinations, and additionally with and without a robot head being present, in a between-subjects design. Perceived anthropomorphism is acquired using the evaluated tests HRIES and Godspeed. A correlation analysis on the dependency of motor interference on anthropomorphism did not reveal significant results of the constructs targeting human-like appearance, intelligence or movement behavior, which were expected to represent relevant predictor parameters. Further, scatter plots do not show a clear pattern. Only the likability Godspeed sub-scale is significant, which however may not be related to the targeted effect. Additionally, the ‘consciousness’ item within the ‘anthropomorphism sub-scale of Godspeed is significant, however, with unclear effect. So, no evidence could be found, that subjective assessments of anthropomorphism with existing tests may be suitable for prediction of motor interference and further research is required.

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An Perception Enhanced Human-Robot Skill Transfer Method for Reactive Interaction 25th

An Perception Enhanced Human-Robot Skill Transfer Method for...

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25th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2024

作者： Si, Weiyong Dong, Jiale Wang, Ning Yang, Chenguang School of Computer Science and Electronic Engineering University of Essex Wivenhoe Park ColchesterCO4 3SQ United Kingdom South China University of Technology Guangzhou510640 China Bristol Robotics Lab University of the West of England BristolBS16 1QY United Kingdom Department of Computer Science University of Liverpool LiverpoolL69 3BX United Kingdom

ISBN: (纸本)9783031720611

In this paper, we propose a trust human-robot skill transfer framework, interpretive and reactive dynamic system (IRDS), by investigating the behaviour tree (BT) and dynamic movement primitives (DMPs) enhanced by the feedback from perceived information for dynamic and uncertain tasks and environments. Human sensorimotor control allows interactions with various environments and accomplishes complex manipulation tasks with uncertainty;however, it is still hard for robots to own this capability. In this work, we aim to transfer these reactive skills to robots, which enable robots to interact with humans and environments under varying uncertainty. The main challenges of robot skill learning are the generalization, safety and stability issues during the skills learning and execution autonomously. BT was investigated for task planning and reactive interaction during the robot execution. The dynamic system-based model generates action for the low-level compliant controller. The convergence of the proposed IRDS framework under dynamic interaction and disturbance was proved by control theory. We conducted simulations and physical experiments on the real robots to evaluate the generalization performance and the convergence capability under uncertainty. And the results show that the reactivity, convergence and interaction performance can be guaranteed, and the learned skills can be transferred among different physical robot platforms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Human robot interaction

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A Review of Autonomous Glaucomatous Grading via OCT Imagery 2

A Review of Autonomous Glaucomatous Grading via OCT Imagery

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2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Raja, Hina Hassan, Taimur Hassan, Bilal Seneviratne, Lakmal Dias, Jorge Werghi, Naoufel Nust Biomisa Lab Department of Computer and Software Engineering College of Eme Rawalpindi Pakistan Khalifa University C2PS and Kucars Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Khalifa University Kucars Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Khalifa University Kucars Department of Mechanical Engineering & Robotics Abu Dhabi United Arab Emirates

ISBN: (纸本)9781665498197

Glaucoma is a progressive and degenerative optic neuropathy that damages the optic nerve due to increased intraocular pressure and yields various visual field abnormalities. Any damage to the optic nerve (due to glaucoma) is irreversible because of the incapacity of the retinal nerve fibers to regenerate. However, the glaucomatous progression can be properly cured if it is diagnosed in the early stages. Furthermore, health authorities worldwide have prioritized the early detection and prevention of glaucomatous progression, and in this regard, presenting a comprehensive insight on the recent literature serves as a critical benchmark to both researchers and clinicians. Towards this end, this paper presents a detailed overview of the state-of-the-art clinical and technical methods related to glaucomatous detection, grading, and prognosis. The clinical work focuses on glaucoma diagnosis and progression tracking in different retinal regions such as the retinal layers, optic never head, and lamina cribrosa, while the technical works focus on producing lesion-aware screening and grading of glaucomatous pathologies via optical coherence tomography. © 2022 IEEE.

关键词： Grading

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Multi-modal Sensor Fusion for Learning Rich Models for Interacting Soft Robots

Multi-modal Sensor Fusion for Learning Rich Models for Inter...

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Thomas George Thuruthel Fumiya Iida Department of Engineering The Bio-Inspired Robotics Lab University of Cambridge UK Department of Computer Science University College London London UK

Soft robots are typically approximated as low-dimensional systems, especially when learning-based methods are used. This leads to models that are limited in their capability to predict the large number of deformation modes and interactions that a soft robot can have. In this work, we present a deep-learning methodology to learn high-dimensional visual models of a soft robot combining multimodal sensorimotor information. The models are learned in an end-to-end fashion, thereby requiring no intermediate sensor processing or grounding of data. The capabilities and advantages of such a modelling approach are shown on a soft anthropomorphic finger with embedded soft sensors. We also show that how such an approach can be extended to develop higher level cognitive functions like identification of the self and the external environment and acquiring object manipulation skills. This work is a step towards the integration of soft robotics and developmental robotics architectures to create the next generation of intelligent soft robots.

关键词： Visualization Shape control Soft sensors Tactile sensors Soft robotics Predictive models Robot sensing systems

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Exploring Help-Seeking Behavior, Performance, and Cognitive Load in Individual Tutoring: A Comparative Study between Human Tutors and Social Robots

Exploring Help-Seeking Behavior, Performance, and Cognitive ...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Xiaoxuan Hei Heng Zhang Adriana Tapus Computer Science and System Engineering Department (U2IS) Autonomous Systems and Robotics Lab ENSTA Paris Institut Polytechnique de Paris Paris France

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Social robots have become increasingly prevalent in the context of one-on-one tutoring, serving as effective educational aids. In response to this trend, the present study was devised to conduct a comparative analysis between human tutors and robot tutors. Additionally, the study aims to investigate how varying previous knowledge in robots influence students’ tendencies for help-seeking. By examining the performance and physiological signals of participants, this research seeks to provide valuable insights into the effectiveness of social robots in educational contexts. 21 participants were divided into three groups, each seeking assistance from a human tutor (HT), seeking help from a robot without any prior knowledge of robots (RT1), and seeking help from a robot after gaining some understanding of its capabilities (RT2). Our results demonstrated that participants sought more help from robot than from human and participants in Group RT2 performed better than participants in Group RT1. However, participants experienced greater cognitive load when interacting with a robot tutor compared to interacting with a human tutor. Future work could focus on developing interventions to alleviate students’ cognitive load during interactions with robot tutors.

关键词： Social robots Robot vision systems Cognitive load Market research Cameras Physiology Behavioral sciences Robots Thermal stresses Stress

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Fast Spatial Reasoning of Implicit 3D Maps through Explicit Near-Far Sampling Range Prediction

Fast Spatial Reasoning of Implicit 3D Maps through Explicit ...

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

作者： Chaerin Min Sehyun Cha Changhee Won Jongwoo Lim Department of Computer Science Brown University Advanced Robotics Lab. CTO Division LG Electronics MultiplEYE Co. Ltd Department of Mechanical Engineering Seoul National University

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

3D mapping is critical for many robotics applications, such as autonomous navigation and object manipulation. Recently, deep implicit mapping approaches have received much attention for their compactness and ability to represent fine-grained details. However, without explicit guidance, such implicit representations are often cumbersome for searching the full range on the rays to find the object surfaces. As a result, several approaches, including hierarchical sampling, occupancy grids, and zero-level set baking, have been proposed to improve sampling where costly forward passes of the neural network should be performed. However, hierarchical sampling is still suboptimal in that it requires uniform coarse samples. Discrete occupancy grids of Instant NGP and zero-level sets of various baking methods are less suitable for large and noisy real *** this paper, we present a novel framework for adaptively predicting the near-far range for sampling the query positions of the deep implicit map. For this purpose, the truncated signed distance grid for the map is pre-constructed and used to provide hints for near-far prediction during rendering. In addition, our recovery algorithm automatically detects failed near-far predictions and recovers only those rays by directly using the implicit map. We conduct extensive experiments on a synthetic dataset, a public real dataset, and a real dataset captured by our multi-camera robot system. The experimental results show that our algorithm achieves the same rendering quality with surprisingly fewer samples compared to the existing methods, which means that the robot can reason about the image and depth properties of the scene much faster. Finally, a thorough analysis of the sample distribution along the rays is provided to give a better understanding of our method’s strong efficiency, adaptability, and robustness. https://***/TSDF-sampling/

关键词： Three-dimensional displays Neural networks Rendering (computer graphics) Prediction algorithms Search problems Robustness Cognition Noise measurement Intelligent robots Synthetic data

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FusionPortableV2: A Unified Multi-Sensor Dataset for Generalized SLAM Across Diverse Platforms and Scalable Environments

arXiv

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arXiv 2024年

作者： Wei, Hexiang Jiao, Jianhao Hu, Xiangcheng Yu, Jingwen Xie, Xupeng Wu, Jin Zhu, Yilong Liu, Yuxuan Wang, Lujia Liu, Ming Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Robot Perception and Learning Lab Department of Computer Science University College London United Kingdom Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology China Guangzhou China

Simultaneous Localization and Mapping (SLAM) technology has been widely applied in various robotic scenarios, from rescue operations to autonomous driving. However, the generalization of SLAM algorithms remains a significant challenge, as current datasets often lack scalability in terms of platforms and environments. To address this limitation, we present FusionPortableV2, a multi-sensor SLAM dataset featuring sensor diversity, varied motion patterns, and a wide range of environmental scenarios. Our dataset comprises 27 sequences, spanning over 2.5 hours and collected from four distinct platforms: a handheld suite, a legged robot, a unmanned ground vehicle (UGV), and a vehicle. These sequences cover diverse settings, including buildings, campuses, and urban areas, with a total length of 38.7km. Additionally, the dataset includes ground-truth (GT) trajectories and RGB point cloud maps covering approximately 0.3km2. To validate the utility of our dataset in advancing SLAM research, we assess several state-of-the-art (SOTA) SLAM algorithms. Furthermore, we demonstrate the dataset's broad application beyond traditional SLAM tasks by investigating its potential for monocular depth estimation. The completae dataset, including sensor data, GT, and calibration details, is accessible at https://***/dataset/fusionportable v2. Copyright © 2024, The Authors. All rights reserved.

关键词： SLAM robotics

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NavG: Risk-Aware Navigation in Crowded Environments Based on Reinforcement Learning with Guidance Points

arXiv

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arXiv 2025年

作者： Zhang, Qianyi Luo, Wentao Liu, Boyi Zhang, Ziyang Wang, Yaoyuan Liu, Jingtai Institute of Robotics and Automatic Information System Nankai University Tianjin Key Laboratory of Intelligent Robotics Tianjin China Advanced Computing and Storage Lab Huawei 2012 Lab China Department of Electronic and Computer Engineering The Kong Kong University of Science and Technology Hong Kong

Motion planning in navigation systems is highly susceptible to upstream perceptual errors, particularly in human detection and tracking. To mitigate this issue, the concept of guidance points—a novel directional cue within a reinforcement learning-based framework—is introduced. A structured method for identifying guidance points is developed, consisting of obstacle boundary extraction, potential guidance point detection, and redundancy elimination. To integrate guidance points into the navigation pipeline, a perception-to-planning mapping strategy is proposed, unifying guidance points with other perceptual inputs and enabling the RL agent to effectively leverage the complementary relationships among raw laser data, human detection and tracking, and guidance points. Qualitative and quantitative simulations demonstrate that the proposed approach achieves the highest success rate and near-optimal travel times, greatly improving both safety and efficiency. Furthermore, real-world experiments in dynamic corridors and lobbies validate the robot’s ability to confidently navigate around obstacles and robustly avoid pedestrians. © 2025, CC BY.

关键词： Travel time

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ASFM: Augmented Social Force Model for Legged Robot Social Navigation

ASFM: Augmented Social Force Model for Legged Robot Social N...

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IEEE-RAS International Conference on Humanoid Robots

作者： Sebastian Ægidius Rodrigo Chacón-Quesada Andromachi Maria Delfaki Dimitrios Kanoulas Yiannis Demiris Department of Computer Science Robot Perception and Learning Lab University College London Gower Street London UK Department of Electrical and Electronic Engineering Personal Robotics Lab Imperial College London London UK Department of Computer Science AI Centre UCL and the Archimedes/Athena RC Greece

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

Social navigation in robotics primarily involves guiding mobile robots through human-populated areas, with pedestrian comfort balanced with efficient path-finding. Although progress has been seen in this field, a solution for the seamless integration of robots into pedestrian settings remains elusive. In this paper, a social force model for legged robots is developed, utilizing visual perception for human localization. In particular, an augmented social force model is introduced, incorporating refined interpretations of repulsive forces and avoidance behaviors based on pedestrian actions, alongside a target following mechanism. Experimental evaluation on a quadruped robot, through various scenarios, including interactions with oncoming pedestrians, crowds, and obstructed paths, demonstrates that the proposed augmented model significantly improves upon previous baseline methods in terms of chosen path length, average velocity, and time-to-goal for effective and efficient social navigation. The code isopen-source, while video demonstrations can be found on the project’s webpage: https://***/ASFM/

关键词： Legged locomotion Location awareness Pedestrians Codes Navigation Force Humanoid robots Quadrupedal robots Visual perception

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