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arXiv 2023年

作者： Matthews, David Spielberg, Andrew Rus, Daniela Kriegman, Sam Bongard, Josh Center for Robotics and Biosystems Northwestern University EvanstonIL60208 United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States Department of Computer Science University of Vermont BurlingtonVT05405 United States

Robots are notoriously difficult to design because of complex interdependencies between their physical structure, sensory and motor layouts, and behavior. Despite this, almost every detail of every robot built to date has been manually determined by a human designer after several months or years of iterative ideation, prototyping, and testing. Inspired by evolutionary design in nature, the automated design of robots using evolutionary algorithms has been attempted for two decades, but it too remains inefficient: days of supercomputing are required to design robots in simulation that, when manufactured, exhibit desired behavior. Here we show for the first time de-novo optimization of a robot’s structure to exhibit a desired behavior, within seconds on a single consumer-grade computer, and the manufactured robot’s retention of that behavior. Unlike other gradient-based robot design methods, this algorithm does not presuppose any particular anatomical form;starting instead from a randomly-generated apodous body plan, it consistently discovers legged locomotion, the most efficient known form of terrestrial movement. If combined with automated fabrication and scaled up to more challenging tasks, this advance promises near instantaneous design, manufacture, and deployment of unique and useful machines for medical, environmental, vehicular, and space-based tasks. © 2023, CC BY.

关键词： Iterative methods

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A Data-Driven Model with Hysteresis Compensation for I2RIS Robot

A Data-Driven Model with Hysteresis Compensation for I2RIS R...

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International Symposium on Medical robotics (ISMR)

作者： Mojtaba Esfandiari Yanlin Zhou Shervin Dehghani Muhammad Hadi Adnan Munawar Henry Phalen David E. Usevitch Peter Gehlbach Iulian Iordachita Department of Mechanical Engineering and Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Department of Computer Science Technische Universität München Munchen Germany Johns Hopkins Hospital Wilmer Eye Institute Baltimore MD USA

ISBN: (数字)9798350377118

ISBN: (纸本)9798350377125

Retinal microsurgery is a high-precision surgery performed on a delicate tissue requiring the skill of highly trained surgeons. Given the restricted range of instrument motion in the confined intraocular space, snake-like robots may prove to be a promising technology to provide surgeons with greater flexibility, dexterity, and positioning accuracy during retinal procedures such as retinal vein cannulation and epireti-nal membrane peeling. Kinematics modeling of these robots is an essential step toward accurate position control. Unlike conventional manipulators, modeling these robots does not fol-low a straightforward method due to their complex mechanical structure and actuation mechanisms. The hysteresis problem can especially impact the positioning accuracy significantly in wire-driven snake-like robots. In this paper, we propose a data-driven kinematics model using a probabilistic Gaussian mixture model (GMM) and Gaussian mixture regression (GMR) approach with a hysteresis compensation algorithm. Experimental results on the two-degree-of-freedom (DOF) integrated robotic intraocular snake (1 2 RIS) show that the proposed model with the hysteresis compensation can predict the snake tip bending angle for pitch and yaw with 0.45° and 0.39° root mean square error (RMSE), respectively. This results in overall 60% and 70% improvements of accuracy for yaw and pitch over the same model without the hysteresis compensation.

关键词： Accuracy Veins Snake robots Kinematics Aerospace electronics Robot sensing systems Retina

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Cooperative vs. Teleoperation Control of the Steady Hand Eye Robot with Adaptive Sclera Force Control: A Comparative Study

arXiv

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arXiv 2023年

作者： Esfandiari, Mojtaba Kim, Ji Woong Zhao, Botao Amirkhani, Golchehr Hadi, Muhammad Gehlbach, Peter Taylor, Russell H. Iordachita, Iulian The Department of Mechanical Engineering Laboratory for Computational Sensing and Robotics The Johns Hopkins University BaltimoreMD21218 United States The Wilmer Eye Institute Johns Hopkins Hospital BaltimoreMD21287 United States The Department of Computer Science The Laboratory for Computational Sensing and Robotics The Johns Hopkins University BaltimoreMD21218 United States

A surgeon's physiological hand tremor can significantly impact the outcome of delicate and precise retinal surgery, such as retinal vein cannulation (RVC) and epiretinal membrane peeling. Robot-assisted eye surgery technology provides ophthalmologists with advanced capabilities such as hand tremor cancellation, hand motion scaling, and safety constraints that enable them to perform these otherwise challenging and high-risk surgeries with high precision and safety. Steady-Hand Eye Robot (SHER) with cooperative control mode can filter out surgeon's hand tremor, yet another important safety feature, that is, minimizing the contact force between the surgical instrument and sclera surface for avoiding tissue damage cannot be met in this control mode. Also, other capabilities, such as hand motion scaling and haptic feedback, require a teleoperation control framework. In this work, for the first time, we implemented a teleoperation control mode incorporated with an adaptive sclera force control algorithm using a PHANTOM Omni haptic device and a force-sensing surgical instrument equipped with Fiber Bragg Grating (FBG) sensors attached to the SHER 2.1 end-effector. This adaptive sclera force control algorithm allows the robot to dynamically minimize the tool-sclera contact force. Moreover, for the first time, we compared the performance of the proposed adaptive teleoperation mode with the cooperative mode by conducting a vessel-following experiment inside an eye phantom under a microscope. © 2023, CC BY.

关键词： Remote control

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Combining Scene Coordinate Regression and Absolute Pose Regression for Visual Relocalization

Combining Scene Coordinate Regression and Absolute Pose Regr...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Jiahao Ruan Li He Yisheng Guan Hong Zhang School of Electromechanical Engineering Guangdong University of Technology Guangzhou China Department of Electrical and Electronic Engineering Shenzhen Key - Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen China

Visual relocalization is a fundamental problem in computer vision and robotics. Recently, regression-based methods become popular and they can be categorized into two classes: absolute pose regression and scene coordinate regression. In this work, we present a combined regression network that jointly learns scene coordinate regression and absolute pose regression for single-image visual relocalization. The proposed network composes of a feature encoder and two regression branches with uncertainty modeling. In particular, we design a deep feature conditioning module, aiming at propagating the coarse pose information in absolute pose regression to inform the predictions in scene coordinate regression. The proposed network is trained in an end-to-end fashion to learn both regression tasks. Moreover, we propose an uncertainty-driven RANSAC algorithm that incorporates the predicted scene coordinates and their uncertainties to solve the camera pose during inference. To the best of our knowledge, this work is the first to combine scene coordinate regression and pose regression in a hierarchical framework for visual relocalization. Experiments on indoor and outdoor benchmarks demonstrate the effectiveness and the superiority of the proposed method over the state-of-the-art methods.

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Friction-Scaled Vibrotactile Feedback for Real-Time Slip Detection in Manipulation using Robotic Sixth Finger

arXiv

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arXiv 2025年

作者： Afzal, Naqash Hasanen, Basma Seneviratne, Lakmal Khatib, Oussama Hussain, Irfan Department of Mechanical and Nuclear Engineering Khalifa University Abu Dhabi127788 United Arab Emirates Khalifa University Abu Dhabi127788 United Arab Emirates Stanford Robotics Laboratory Computer Science Department Stanford University Palo AltoCA94305 United States

The integration of extra-robotic limbs/fingers to enhance and expand motor skills, particularly for grasping and manipulation, possesses significant challenges. The grasping performance of existing limbs/fingers is far inferior to that of human hands. Human hands can detect the onset of slip through tactile feedback originating from tactile receptors during the grasping process, enabling precise and automatic regulation of grip force. This grip force is scaled by the coefficient of friction between the contacting surface and the fingers. The frictional information is perceived by humans depending upon the slip happening between the finger and the object. This ability to perceive friction allows humans to apply just the right amount of force needed to maintain a secure grip, adjusting based on the weight of the object and the friction of the contact surface. Enhancing this capability in extra-robotic limbs or fingers used by humans is challenging. To address this challenge, this paper introduces a novel approach to communicate frictional information to users through encoded vibrotactile cues. These cues are conveyed on the onset of incipient slip thus allowing the users to perceive the friction and ultimately use this information to increase the force to avoid dropping of the object. In a 2-alternative forced-choice protocol, participants gripped and lifted a glass under three different frictional conditions, applying a normal force of 3.5 N. After reaching this force, the glass was gradually released to induce slip. During this slipping phase, vibrations scaled according to the static coefficient of friction were presented to users, reflecting the frictional conditions. The results suggested an accuracy of 94.53±3.05 (mean±SD) in perceiving frictional information upon lifting objects with varying friction. The results indicate the effectiveness of using vibrotactile feedback for sensory feedback, allowing users of extra-robotic limbs or fingers to perceive frictiona

关键词： Friction

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QUADFormer: Learning-based Detection of Cyber Attacks in Quadrotor UAVs

arXiv

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arXiv 2024年

作者： Wang, Pengyu Yang, Zhaohua Yang, Nachuan Wang, Zikai Li, Jialu Zhang, Fan Wang, Chaoqun Wang, Jiankun Meng, Max Q.-H. Shi, Ling Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The School of Control Science and Engineering Shandong University Shandong China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Department of Electrical and Computer Engineering The University of Alberta Canada

Safety-critical intelligent cyber-physical systems, such as quadrotor unmanned aerial vehicles (UAVs), are vulnerable to different types of cyber attacks, and the absence of timely and accurate attack detection can lead to severe consequences. When UAVs are engaged in large outdoor maneuvering flights, their system constitutes highly nonlinear dynamics that include non-Gaussian noises. Therefore, the commonly employed traditional statistics-based and emerging learning-based attack detection methods do not yield satisfactory results. In response to the above challenges, we propose QUADFormer, a novel Quadrotor UAV Attack Detection framework with transFormer-based architecture. This framework includes a residue generator designed to generate a residue sequence sensitive to anomalies. Subsequently, this sequence is fed into a transformer structure with disparity in correlation to specifically learn its statistical characteristics for the purpose of classification and attack detection. Finally, we design an alert module to ensure the safe execution of tasks by UAVs under attack conditions. We conduct extensive simulations and real-world experiments, and the results show that our method has achieved superior detection performance compared with many state-of-the-art methods. (Video1) Copyright © 2024, The Authors. All rights reserved.

关键词： Antennas

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Accessibility-Aware Reinforcement Learning for Inclusive Robotic Navigation

Accessibility-Aware Reinforcement Learning for Inclusive Rob...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Mengsha Hu Yunhuan Wu Yibei Guo Yi Wu Chao Shi Lei Xu Rui Liu Department of Computer Science Kent State University Kent OH USA Robotics Institute Carnegie Mellon University Pittsburgh PA USA Cognitive Robotics and AI Lab (CRAI) College of Aeronautics and Engineering Kent State University Kent OH USA Informatique et Sciences du Numérique (Computer Research Laboratory) CentraleSupélec Paris-Saclay University Gif-sur-Yvette France Department of Systems Science and Industrial Engineering Binghamton University Binghamton NY USA

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Robotic navigation aids humans in essential scenarios, including airport customer boarding, commercial meeting receptions, and disaster site navigation. While individuals benefit from robotic services, these services pose accessibility challenges for people with disabilities. Robots may face limitations in mobility, vision, hearing, and cognitive reasoning, hindering disabled individuals' access to these services. As robotic services become increasingly prevalent, ensuring they are inclusive and accessible is crucial, accommodating human disabilities with adaptations such as slower movements, standing support, and hazard detection. To address this issue, this study introduces a novel Accessibility-Aware Reinforcement Learning model (ARL). ARL extracts disability-related information from human behavioral observations using a contextembedding neural network. It then adjusts robot motions to provide inclusive assistance to individuals with disabilities. To assess the effectiveness of this approach, four assisting services in navigation scenarios ('lead', 'wait', 'assist', and 'protect') and three application scenarios involving navigating elderly individuals in a museum, through traffic, and across a building patio were designed. The results validate ARL's efficacy in enabling robots to make accessibility-aware decisions, thereby enhancing their support for individuals with disabilities.

关键词： Robot motion Navigation Neural networks Reinforcement learning Feature extraction People with disabilities Robot learning Data mining Older adults Robots

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Toward Fine Contact Interactions: Learning to Control Normal Contact Force with Limited Information

Toward Fine Contact Interactions: Learning to Control Normal...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Jinda Cui Jiawei Xu David Saldana Jeff Trinkle Lehigh AIRLab Honda Research Institute USA San Jose CA USA Department of Computer Science and Engineering Autonomous and Intelligent Robotics Laboratory (AIRLab) Lehigh University Bethleham PA USA

Dexterous manipulation of objects through fine control of physical contacts is essential for many important tasks of daily living. A fundamental ability underlying fine contact control is compliant control, i.e., controlling the contact forces while moving. For robots, the most widely explored approaches heavily depend on models of manipulated objects and expensive sensors to gather contact location and force information needed for real-time control. The models are difficult to obtain, and the sensors are costly, hindering personal robots' adoption in our homes and businesses. This study performs model-free reinforcement learning of a normal contact force controller on a robotic manipulation system built with a low-cost, information-poor tactile sensor. Despite the limited sensing capability, our force controller can be combined with a motion controller to enable fine contact interactions during object manipulation. Promising results are demonstrated in non-prehensile, dexterous manipulation experiments.

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Visual Sensor-Based Trajectory Multimodality Prediction via Anchor-Free Query Attention Network

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Wang, Ruiping Cheng, Jun Yu, Junzhi Nanyang Technological University School of Computer Science and Engineering Jurong West Singapore639798 Singapore Peking University College of Engineering Beijing100871 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China

As a core component of intelligent surveillance and autonomous driving systems, visual sensor-based trajectory multimodality prediction can significantly improve their perception and decision-making capabilities. Numerous existing trajectory prediction methods are dedicated to enhance prediction performance. However, they still fail to effectively model the complex spatiotemporal interactions among pedestrians and trajectory multimodality. To address the above challenges, this article presents one new trajectory multimodality prediction method via anchor-free query-based attention network, which can more effectively capture spatiotemporal interactions and model trajectory multimodality. First, the initial trajectory proposal module employs target-centered query-based attention to capture complex spatiotemporal interactions and generate multiple trajectory proposals corresponding to the multiple modes of the future trajectory, which allows the model to use different interaction information when decoding trajectory points at different time steps. Second, the novel trajectory refinement module utilizes the learnable trajectory proposals to construct proposal-level interaction mechanisms to refine future trajectories. Experimental results conducted on ETH and UCY datasets demonstrate that the proposed model can provide the state-of-the-art prediction performance. © 1963-2012 IEEE.

关键词： Prediction models

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Physics Representation Learning for Dexterous Manipulation Planning

Physics Representation Learning for Dexterous Manipulation P...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Yi Wu Mengsha Hu Runxiang Jin Rui Liu Informatique et Sciences du Numérique (Computer Research Laboratory) CentraléSupelec Paris-Saclay University Gif-Sur-Yvette France Cognitive Robotics and AI Lab (CRAI) College of Aeronautics and Engineering Department of Computer Science Kent State University Kent OH USA

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Dexterous manipulation in robotics, particularly with high degrees of freedom (DoF) devices like the 24-joint Shadow Hand, confronts complexities in search space and execution precision. Humans, however, manipulate objects effortlessly, thanks to their innate grasp of physics. Inspired by this, we introduce the Physics Representation Learning (PRL) framework for robotic hand manipulation. PRL uses physics principles for action conceptualization, such as aligning axes at specific angles (Figure 1). It is a natural language that describes the physics laws, thus bridging the gap between high-level semantics planning and joint-level execution through inverse kinematics (IK). Built on top of the physics-informed action space, PRL deploys a Reinforcement Learning (RL) network with expert demonstrations. Its effectiveness was validated in Nvidia Isaac Sim on four tasks: hammering, unscrewing, sweeping, and pinching. Results show that PRL significantly outperforms conventional joint-control algorithms and RL without demonstrations, underscoring the benefits of physics-based action representations for complex tasks.

关键词： Representation learning Semantics Natural languages Reinforcement learning Kinematics Planning Complexity theory Physics Robots

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