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FIReStereo: Forest InfraRed Stereo Dataset for UAS Depth Perception in Visually Degraded Environments

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arXiv 2024年

作者： Dhrafani, Devansh Liu, Yifei Jong, Andrew Shin, Ukcheol He, Yao Harp, Tyler Hu, Yaoyu Oh, Jean Scherer, Sebastian Carnegie Mellon University College of Engineering Department of Mechanical Engineering PittsburghPA United States Carnegie Mellon University School of Computer Science The Robotics Institute PittsburghPA United States

Robust depth perception in visually-degraded environments is crucial for autonomous aerial systems. Thermal imaging cameras, which capture infrared radiation, are robust to visual degradation. However, due to lack of a large-scale dataset, the use of thermal cameras for unmanned aerial system (UAS) depth perception has remained largely unexplored. This paper presents a stereo thermal depth perception dataset for autonomous aerial perception applications. The dataset consists of stereo thermal images, LiDAR, IMU and ground truth depth maps captured in urban and forest settings under diverse conditions like day, night, rain, and smoke. We benchmark representative stereo depth estimation algorithms, offering insights into their performance in degraded conditions. Models trained on our dataset generalize well to unseen smoky conditions, highlighting the robustness of stereo thermal imaging for depth perception. We aim for this work to enhance robotic perception in disaster scenarios, allowing for exploration and operations in previously unreachable areas. The dataset and source code are available at https://***. © 2024, CC BY.

关键词： Depth perception

Precision healthcare: A deep dive into machine learning algorithms and feature selection strategies for accurate heart disease prediction

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computers in Biology and Medicine 2024年 176卷 108432-108432页

作者： Islam, Md. Ariful Majumder, Md. Ziaul Hasan Miah, Md. Sohel Jannaty, Sumaia Department of Robotics and Mechatronics Engineering University of Dhaka Dhaka1000 Bangladesh Institute of Electronics Bangladesh Atomic Energy Commision Dhaka Bangladesh Department of Computer Science and Technology Moulvibazar Polytechnic Institute Bangladesh Gonoshasthaya Samaj Vittik Medical College Savar Dhaka Bangladesh

This paper presents a comprehensive exploration of machine learning algorithms (MLAs) and feature selection techniques for accurate heart disease prediction (HDP) in modern healthcare. By focusing on diverse datasets encompassing various challenges, the research sheds light on optimal strategies for early detection. MLAs such as Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), Gaussian Naive Bayes (NB), and others were studied, with precision and recall metrics emphasized for robust predictions. Our study addresses challenges in real-world data through data cleaning and one-hot encoding, enhancing the integrity of our predictive models. Feature extraction techniques—Recursive Feature Extraction (RFE), Principal Component Analysis (PCA), and univariate feature selection—play a crucial role in identifying relevant features and reducing data dimensionality. Our findings showcase the impact of these techniques on improving prediction accuracy. Optimized models for each dataset have been achieved through grid search hyperparameter tuning, with configurations meticulously outlined. Notably, a remarkable 99.12 % accuracy was achieved on the first Kaggle dataset, showcasing the potential for accurate HDP. Model robustness across diverse datasets was highlighted, with caution against overfitting. The study emphasizes the need for validation of unseen data and encourages ongoing research for generalizability. Serving as a practical guide, this research aids researchers and practitioners in HDP model development, influencing clinical decisions and healthcare resource allocation. By providing insights into effective algorithms and techniques, the paper contributes to reducing heart disease-related morbidity and mortality, supporting the healthcare community's ongoing efforts. © 2024 Elsevier Ltd

关键词： Forecasting

QueryCAD: Grounded Question Answering for CAD Models

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arXiv 2024年

作者： Kienle, Claudius Alt, Benjamin Katic, Darko Jäkel, Rainer Peters, Jan ArtiMinds Robotics Karlsruhe Germany IAS Lab Computer Science Department TU Darmstadt Germany AICOR Institute for Artificial Intelligence University of Bremen Germany Stuttgart University of Applied Sciences Germany

CAD models are widely used in industry and are essential for robotic automation processes. However, these models are rarely considered in novel AI-based approaches, such as the automatic synthesis of robot programs, as there are no readily available methods that would allow CAD models to be incorporated for the analysis, interpretation, or extraction of information. To address these limitations, we propose QueryCAD, the first system designed for CAD question answering, enabling the extraction of precise information from CAD models using natural language queries. QueryCAD incorporates SegCAD, an open-vocabulary instance segmentation model we developed to identify and select specific parts of the CAD model based on part descriptions. We further propose a CAD question answering benchmark to evaluate QueryCAD and establish a foundation for future research. Lastly, we integrate QueryCAD within an automatic robot program synthesis framework, validating its ability to enhance deep-learning solutions for robotics by enabling them to process CAD models. https://***/querycad © 2024, CC BY-NC-SA.

关键词： Robot programming

Manipulation-Oriented Object Perception in Clutter through Affordance Coordinate Frames

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Manipulation-Oriented Object Perception in Clutter through A...

IEEE-RAS International Conference on Humanoid Robots

作者： Xiaotong Chen Kaizhi Zheng Zhen Zeng Cameron Kisailus Shreshtha Basu James Cooney Jana Pavlasek Odest Chadwicke Jenkins Department of Electrical Engineering and Computer Science Robotics Institute at the University of Michigan Ann Arbor MI USA

ISBN: (纸本)9798350309805

In order to enable robust operation in unstructured environments, robots should be able to generalize ma-nipulation actions to novel object instances. For example, to pour and serve a drink, a robot should be able to recognize novel containers which afford the task. Most importantly, robots should be able to manipulate these novel containers to fulfill the task. To achieve this, we aim to provide robust and generalized perception of object affordances and their associated manipulation poses for reliable manipulation. In this work, we combine the notions of affordance and category-level pose, and introduce the Affordance Coordinate Frame (ACF). With ACF, we represent each object class in terms of individual affordance parts and the compatibility between them, where each part is associated with a part category-level pose for robot manipulation. In our experiments, we demonstrate that ACF outperforms state-of-the-art methods for object detection, as well as category-level pose estimation for object parts. We further demonstrate the applicability of ACF to robot manipulation tasks through experiments in both simulation and real world environment.

关键词： Deep learning Affordances Robot kinematics Pose estimation Humanoid robots Object detection Containers

Sim-To-Real Transfer for Visual Reinforcement Learning of Deformable Object Manipulation for Robot-Assisted Surgery

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arXiv 2024年

作者： Scheikl, Paul Maria Tagliabue, Eleonora Gyenes, Balázs Wagner, Martin Dall’Alba, Diego Fiorini, Paolo Mathis-Ullrich, Franziska The Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe76131 Germany The Department of Computer Science University of Verona Verona37134 Italy The Department for General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg69120 Germany

Automation holds the potential to assist surgeons in robotic interventions, shifting their mental work load from visuomotor control to high level decision making. Reinforcement learning has shown promising results in learning complex visuomotor policies, especially in simulation environments where many samples can be collected at low cost. A core challenge is learning policies in simulation that can be deployed in the real world, thereby overcoming the sim-to-real gap. In this work, we bridge the visual sim-to-real gap with an image-based reinforcement learning pipeline based on pixel-level domain adaptation and demonstrate its effectiveness on an image-based task in deformable object manipulation. We choose a tissue retraction task because of its importance in clinical reality of precise cancer surgery. After training in simulation on domain-translated images, our policy requires no retraining to perform tissue retraction with a 50% success rate on the real robotic system using raw RGB images. Furthermore, our simto-real transfer method makes no assumptions on the task itself and requires no paired images. This work introduces the first successful application of visual sim-to-real transfer for robotic manipulation of deformable objects in the surgical field, which represents a notable step towards the clinical translation of cognitive surgical robotics. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

Seven Benefits of Using Series Elastic Actuators in the Design of an Affordable, Simple Controlled, and Functional Prosthetic Hand

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Seven Benefits of Using Series Elastic Actuators in the Desi...

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

作者： Erfan Koochakzadeh Alireza Kargar Parsa Sattari Diba Ravanshid Rezvan Nasiri Research Institute for Robotics Artificial Intelligence and Information Science (RAIIS) Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This paper highlights the benefits of using series elastic actuators (SEA) in designing a cost-efficient, easily controlled, and functional prosthetic hand. The designed 3D-printed hand uses only two motors in an antagonistic configuration, transferring power to the fingers via pulleys, cables, and springs; i.e., the motors are in an SEA configuration with the load/fingers. In the designed underactuated prosthetic hand, the thumb is adjustable for various tasks, and the optimization of pulley diameters ensures synchronized finger movement during hand flexion and extension. Thanks to the SEA configuration of the motors and fingers, simple position control of the motor enables features like hand position control, morphological grasp, force control, impedance control, slippage detection, safe interaction, and efficient grasp. An extensive set of experiments has been conducted to evaluate the designed prosthetic hand’s performance. The experiments confirm the hand’s satisfactory performance while also highlighting the importance of improving the proposed design in different aspects. To attain better position control and morphological grasp, minimizing the cable-body and joint friction is recommended. A higher resolution of the current/torque sensor is needed for the precise force control and slippage detection. Finally, a motor brake system is required to achieve efficient grasping.

关键词： Hands Actuators Pulleys Thumb Position control Motors Synchronization Force control Springs Prosthetic hand

Delayed dynamic-feedback controller design for multi-frequency vibration suppression

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IFAC-PapersOnLine 2024年第27期58卷 196-201页

作者： Adrian Saldanha Adam Peichl Wim Michiels Tomas Vyhlidal Department of Computer Science KU Leuven Celestijnenlaan 200A 3001 Leuven Belgium Dept. Instrumentation and Control Engineering Faculty of Mechanical Engineering and Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague 6 Czechia

We present a methodology for designing a dynamic controller with delayed output feedback for achieving non-collocated vibration suppression with a focus on the multi-frequency case. To synthesize the delay-based controller, we first remodel the system of equations as a delay-differential algebraic equation (DDAE) in such a way that existing tools for design of a static output feedback controller can be easily adapted. The problem of achieving non-collocated vibration suppression with sufficient damping is formulated as a constrained optimization problem of minimizing the spectral abscissa in the presence of zero-location constraints, with the constraints exhibiting polynomial dependence on its parameters. We transform the problem into an unconstrained one using elimination, following which we solve the resulting non-convex, non-smooth optimization problem.

关键词： vibration suppression time-delay DDAE

Extremum Seeking Controlled Wiggling for Tactile Insertion

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arXiv 2024年

作者： Burner, Levi Mantripragada, Pavan Caddeo, Gabriele M. Natale, Lorenzo Fermüller, Cornelia Aloimonos, Yiannis Perception and Robotics Group Electrical and Computer Engineering University of Maryland College Park United States Perception and Robotics Group Department of Computer Science University of Maryland College Park United States Istituto Italiano di Tecnologia Via San Quirico 19 D Genova Italy DIBRIS Università di Genova Via All'Opera Pia 13 Genoa Italy Perception and Robotics Group Institute for Advanced Computer Studies University of Maryland College Park United States

When humans perform insertion tasks such as inserting a cup into a cupboard, routing a cable, or key insertion, they wiggle the object and observe the process through tactile and proprioceptive feedback. While recent advances in tactile sensors have resulted in tactile-based approaches, there has not been a generalized formulation based on wiggling similar to human behavior. Thus, we propose an extremum-seeking control law that can insert four keys into four types of locks without control parameter tuning despite significant variation in lock type. The resulting model-free formulation wiggles the end effector pose to maximize insertion depth while minimizing strain as measured by a GelSight Mini tactile sensor that grasps a key. The algorithm achieves a 71% success rate over 120 randomly initialized trials with uncertainty in both translation and orientation. Over 240 deterministically initialized trials, where only one translation or rotation parameter is perturbed, 84% of trials succeeded. Given tactile feedback at 13 Hz, the mean insertion time for these groups of trials are 262 and 147 seconds respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： End effectors

OSSID: Online Self-Supervised Instance Detection by (and for) Pose Estimation

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arXiv 2022年

作者： Gu, Qiao Okorn, Brian Held, David Department of Computer Science University of Toronto Canada The Robotics Institute Carnegie Mellon University United States

Real-time object pose estimation is necessary for many robot manipulation algorithms. However, state-of-the-art methods for object pose estimation are trained for a specific set of objects;these methods thus need to be retrained to estimate the pose of each new object, often requiring tens of GPU-days of training for optimal performance. In this paper, we propose the OSSID framework, leveraging a slow zero-shot pose estimator to self-supervise the training of a fast detection algorithm. This fast detector can then be used to filter the input to the pose estimator, drastically improving its inference speed. We show that this self-supervised training exceeds the performance of existing zero-shot detection methods on two widely used object pose estimation and detection datasets, without requiring any human annotations. Further, we show that the resulting method for pose estimation has a significantly faster inference speed, due to the ability to filter out large parts of the image. Thus, our method for self-supervised online learning of a detector (trained using pseudo-labels from a slow pose estimator) leads to accurate pose estimation at real-time speeds, without requiring human annotations. Supplementary materials and code can be found at https://***/OSSID/ Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection