机器人相关学术速递[11.8]

cs.RO机器人相关，共计17篇

【1】 Dynamic Human-Robot Role Allocation based on Human Ergonomics Risk Prediction and Robot Actions Adaptation 标题：基于人体工效学风险预测和机器人动作适应的动态人-机器人角色分配 链接：https://arxiv.org/abs/2111.03630

作者：Elena Merlo,Edoardo Lamon,Fabio Fusaro,Marta Lorenzini,Alessandro Carfì,Fulvio Mastrogiovanni,Arash Ajoudani,. 机构： University of Genoa 备注：7 pages, 11 figures, submitted to 2022 IEEE International Conference on Robotics and Automation (ICRA) 摘要：尽管COBOT在制造和物流过程中具有很高的潜力，但其在不断变化的环境中的快速（重新）部署仍然有限。为了快速适应新产品的需求，提高人类工作人员对分配任务的适应能力，我们提出了一种新的方法，在人类-机器人协作任务中优化装配策略并在工作人员之间分配工作。合作模型利用了我们用来解决角色分配问题的和/或图。分配算法考虑在线计算的定量测量，以描述操作员的人机工程学状态和任务属性。我们进行了初步实验，以证明所提出的方法成功地控制了任务分配过程，从而确保了人类工作者的安全和符合人体工程学的条件。 摘要：Despite cobots have high potential in bringing several benefits in the manufacturing and logistic processes, but their rapid (re-)deployment in changing environments is still limited. To enable fast adaptation to new product demands and to boost the fitness of the human workers to the allocated tasks, we propose a novel method that optimizes assembly strategies and distributes the effort among the workers in human-robot cooperative tasks. The cooperation model exploits AND/OR Graphs that we adapted to solve also the role allocation problem. The allocation algorithm considers quantitative measurements that are computed online to describe human operator's ergonomic status and task properties. We conducted preliminary experiments to demonstrate that the proposed approach succeeds in controlling the task allocation process to ensure safe and ergonomic conditions for the human worker.

【2】 Synchronized Smartphone Video Recording System of Depth and RGB Image Frames with Sub-millisecond Precision 标题：亚毫秒精度深度和RGB图像帧同步智能手机视频记录系统 链接：https://arxiv.org/abs/2111.03552

作者：Marsel Faizullin,Anastasiia Kornilova,Azat Akhmetyanov,Konstantin Pakulev,Andrey Sadkov,Gonzalo Ferrer 机构：Skolkovo Institute of Science and Technology, Moscow, Russia 备注：IEEE Sensors Journal submitted paper 摘要：在本文中，我们提出了一种具有高时间同步（sync）精度的记录系统，该系统由智能手机、深度摄像头、IMU等异构传感器组成。由于智能手机的普遍兴趣和广泛采用，我们在系统中至少包括一个此类设备。这种异构系统需要对两种不同的时间管理器进行混合同步：智能手机和MCU，我们将基于硬件有线的触发同步与软件同步相结合。我们在一个自定义和新颖的系统上评估了我们的同步结果，该系统将主动红外深度与RGB相机混合。我们的系统实现了亚毫秒的时间同步精度。此外，我们的系统以这种精度同时曝光每个RGB深度图像对。我们特别展示了一个配置，但是我们系统背后的一般原则可以被其他项目复制。 摘要：In this paper, we propose a recording system with high time synchronization (sync) precision which consists of heterogeneous sensors such as smartphone, depth camera, IMU, etc. Due to the general interest and mass adoption of smartphones, we include at least one of such devices into our system. This heterogeneous system requires a hybrid synchronization for the two different time authorities: smartphone and MCU, where we combine a hardware wired-based trigger sync with software sync. We evaluate our sync results on a custom and novel system mixing active infra-red depth with RGB camera. Our system achieves sub-millisecond precision of time sync. Moreover, our system exposes every RGB-depth image pair at the same time with this precision. We showcase a configuration in particular but the general principles behind our system could be replicated by other projects.

【3】 Optimal Inverted Landing in a Small Aerial Robot with Varied Approach Velocities and Landing Gear Designs 标题：小型飞行机器人变速进场最优倒着陆及起落架设计 链接：https://arxiv.org/abs/2111.03539

作者：Bryan Habas,Bader AlAttar,Brian Davis,Jack W. Langelaan,Bo Cheng 机构： Department of AerospaceEngineering, The Pennsylvania State University, University Park 备注：7 pages, 9 figures, Submitted to ICRA 2022 conference 摘要：在空中机器人中进行反向着陆是一项具有挑战性的壮举，尤其是在没有外部定位的情况下。然而，它通常由蜜蜂、苍蝇和蝙蝠等生物传单执行。我们先前对苍蝇着陆行为的观察表明，苍蝇假定的视觉线索与执行的空中机动运动学之间存在开环因果关系。例如，旋转动作的程度（因此着陆前身体倒置）和腿部辅助身体摆动的程度都取决于苍蝇接近天花板时的初始身体状态。在这项工作中，通过基于物理的模拟和实验验证，我们系统地研究了优化的反向着陆机动如何依赖于不同大小和方向的初始进近速度。这是通过分析最佳机动轨迹期间假定的视觉线索（可从车载测量中得出）来实现的。我们确定了一个三维政策区域，从该区域可以在不使用外部定位数据的情况下制定全球反向着陆政策的映射。此外，我们还研究了一系列起落架设计对优化着陆性能的影响，并确定了它们的优缺点。上述结果已通过有限的实验测试得到部分验证，并将继续为我们未来的实验提供信息和指导，例如通过应用计算得出的全球政策。 摘要：Inverted landing is a challenging feat to perform in aerial robots, especially without external positioning. However, it is routinely performed by biological fliers such as bees, flies, and bats. Our previous observations of landing behaviors in flies suggest an open-loop causal relationship between their putative visual cues and the kinematics of the aerial maneuvers executed. For example, the degree of rotational maneuver (therefore the body inversion prior to touchdown) and the amount of leg-assisted body swing both depend on the flies' initial body states while approaching the ceiling. In this work, by using a physics-based simulation with experimental validation, we systematically investigated how optimized inverted landing maneuvers depend on the initial approach velocities with varied magnitude and direction. This was done by analyzing the putative visual cues (that can be derived from onboard measurements) during optimal maneuvering trajectories. We identified a three-dimensional policy region, from which a mapping to a global inverted landing policy can be developed without the use of external positioning data. In addition, we also investigated the effects of an array of landing gear designs on the optimized landing performance and identified their advantages and disadvantages. The above results have been partially validated using limited experimental testing and will continue to inform and guide our future experiments, for example by applying the calculated global policy.

【4】 Disengagement Cause-and-Effect Relationships Extraction Using an NLP Pipeline 标题：基于NLP管道的脱离因果关系提取 链接：https://arxiv.org/abs/2111.03511

作者：Yangtao Zhang,X. Jessie Yang,Feng Zhou 机构： The CaliforniaDepartment of Motor Vehicles (CA DMV) has launched theAutonomous Vehicle Tester Program, The University of Michigan 摘要：机器学习和人工智能的进步正在推动自动驾驶汽车（AVs）在公路上的测试和部署。加利福尼亚州机动车部门（CA DMV）启动了自动车辆测试程序，该程序收集并发布与自动驾驶车辆脱离（AVD）相关的报告。了解AVD的原因对于提高AV系统的安全性和稳定性至关重要，并为AV测试和部署提供指导。在这项工作中，构建了一个可扩展的端到端管道，使用自然语言处理深度迁移学习来收集、处理、建模和分析2014年至2020年发布的脱离报告。使用分类学、可视化和统计测试对脱离数据进行分析，揭示了AVD测试的趋势、分类原因频率以及AVD的原因和影响之间的重要关系。我们发现，（1）制造商在春季和/或冬季对AVs进行了密集测试，（2）测试驾驶员启动了80%以上的脱离，而超过75%的脱离是由感知、定位和映射、AV系统本身的规划和控制错误导致的，以及（3）AVD的始作俑者与病因类别之间存在显著关系。这项研究是使用预先训练的模型进行深度迁移学习的成功实践，并生成一个整合的脱离数据库，以便其他研究人员进行进一步调查。 摘要：The advancement in machine learning and artificial intelligence is promoting the testing and deployment of autonomous vehicles (AVs) on public roads. The California Department of Motor Vehicles (CA DMV) has launched the Autonomous Vehicle Tester Program, which collects and releases reports related to Autonomous Vehicle Disengagement (AVD) from autonomous driving. Understanding the causes of AVD is critical to improving the safety and stability of the AV system and provide guidance for AV testing and deployment. In this work, a scalable end-to-end pipeline is constructed to collect, process, model, and analyze the disengagement reports released from 2014 to 2020 using natural language processing deep transfer learning. The analysis of disengagement data using taxonomy, visualization and statistical tests revealed the trends of AV testing, categorized cause frequency, and significant relationships between causes and effects of AVD. We found that (1) manufacturers tested AVs intensively during the Spring and/or Winter, (2) test drivers initiated more than 80% of the disengagement while more than 75% of the disengagement were led by errors in perception, localization & mapping, planning and control of the AV system itself, and (3) there was a significant relationship between the initiator of AVD and the cause category. This study serves as a successful practice of deep transfer learning using pre-trained models and generates a consolidated disengagement database allowing further investigation for other researchers.

【5】 Event-based Motion Segmentation by Cascaded Two-Level Multi-Model Fitting 标题：级联两级多模型拟合的基于事件的运动分割 链接：https://arxiv.org/abs/2111.03483

作者：Xiuyuan Lu,Yi Zhou,Shaojie Shen 机构： Hong Kong University ofScience and Technology 备注：Accepted for presentation at the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021) 摘要：在合成代理与动态场景交互的先决条件中，识别独立移动对象的能力尤为重要。然而，从应用的角度来看，标准相机在剧烈运动和具有挑战性的照明条件下可能会显著退化。相比之下，基于事件的摄像机作为一种新颖的生物传感器，在应对这些挑战方面具有优势。其快速响应和异步特性使其能够以与场景动力学完全相同的速率捕获视觉刺激。在本文中，我们提出了一种级联的两级多模型拟合方法，用于用单目事件相机识别独立运动对象（即运动分割问题）。第一级利用事件特征的跟踪，在渐进式多模型拟合方案下解决特征聚类问题。第二级使用生成的运动模型实例初始化，使用时空图切割方法进一步解决事件聚类问题。这种组合导致高效和准确的事件式运动分割，这是任何一种方法都无法单独实现的。实验证明了该方法在具有不同运动模式和未知数量独立运动对象的真实场景中的有效性和通用性。 摘要：Among prerequisites for a synthetic agent to interact with dynamic scenes, the ability to identify independently moving objects is specifically important. From an application perspective, nevertheless, standard cameras may deteriorate remarkably under aggressive motion and challenging illumination conditions. In contrast, event-based cameras, as a category of novel biologically inspired sensors, deliver advantages to deal with these challenges. Its rapid response and asynchronous nature enables it to capture visual stimuli at exactly the same rate of the scene dynamics. In this paper, we present a cascaded two-level multi-model fitting method for identifying independently moving objects (i.e., the motion segmentation problem) with a monocular event camera. The first level leverages tracking of event features and solves the feature clustering problem under a progressive multi-model fitting scheme. Initialized with the resulting motion model instances, the second level further addresses the event clustering problem using a spatio-temporal graph-cut method. This combination leads to efficient and accurate event-wise motion segmentation that cannot be achieved by any of them alone. Experiments demonstrate the effectiveness and versatility of our method in real-world scenes with different motion patterns and an unknown number of independently moving objects.

【6】 DriveGuard: Robustification of Automated Driving Systems with Deep Spatio-Temporal Convolutional Autoencoder 标题：DriveGuard：采用深时空卷积自动编码器的自动驾驶系统的ROBUS化 链接：https://arxiv.org/abs/2111.03480

作者：Andreas Papachristodoulou,Christos Kyrkou,Theocharis Theocharides 机构：KIOS Research and Innovation Center of Excellence, Department of Electrical and Computer Engineering, University of Cyprus, Panepistimiou Avenue, Nicosia Cyprus 备注：2021 IEEE Winter Conference on Applications of Computer Vision Workshops (WACVW) 摘要：自动驾驶车辆越来越依赖于摄像头为感知和场景理解提供输入，这些模型在不利条件和图像噪声下对其环境和对象进行分类的能力至关重要。当输入在无意中或通过有针对性的攻击而恶化时，自动驾驶车辆的可靠性就会受到损害。为了缓解这种现象，我们提出了一种轻量级时空自动编码器DriveGuard，作为一种解决方案，用于增强自动车辆图像分割过程的鲁棒性。通过首先使用DriveGuard处理摄像头图像，我们提供了一个比使用噪声输入重新训练每个感知模型更通用的解决方案。我们探索了不同自动编码器架构的空间，并在使用真实图像和合成图像创建的不同数据集上对其进行了评估，证明通过利用时空信息结合多分量损失，我们显著提高了对负面图像影响的鲁棒性，达到原始图像的5-6%在干净的图像上建模。 摘要：Autonomous vehicles increasingly rely on cameras to provide the input for perception and scene understanding and the ability of these models to classify their environment and objects, under adverse conditions and image noise is crucial. When the input is, either unintentionally or through targeted attacks, deteriorated, the reliability of autonomous vehicle is compromised. In order to mitigate such phenomena, we propose DriveGuard, a lightweight spatio-temporal autoencoder, as a solution to robustify the image segmentation process for autonomous vehicles. By first processing camera images with DriveGuard, we offer a more universal solution than having to re-train each perception model with noisy input. We explore the space of different autoencoder architectures and evaluate them on a diverse dataset created with real and synthetic images demonstrating that by exploiting spatio-temporal information combined with multi-component loss we significantly increase robustness against adverse image effects reaching within 5-6% of that of the original model on clean images.

【7】 Small UAVs-supported Autonomous Generation of Fine-grained 3D Indoor Radio Environmental Maps 标题：小型无人机支持的细粒度三维室内射电环境地图自动生成 链接：https://arxiv.org/abs/2111.03451

作者：Ken Mendes,Filip Lemic,Jeroen Famaey 机构：Internet and Data Lab (IDLab), Universiteit Antwerpen - imec, Belgium 摘要：无线电环境地图（REMs）是增强各种通信和网络代理性能的强大工具。然而，生成REMs是一项艰巨的任务，特别是在复杂的三维（3D）环境中，如室内。为了解决这个问题，我们提出了一个用于自主生成室内三维空间细粒度REMs的系统。在该系统中，多个小型室内无人机（UAV）依次用于信号质量指示器的3D采样。收集的读数被简化为机器学习（ML）系统进行训练，一旦训练，系统能够预测未知3D位置的信号质量。该系统支持自动和自主REM生成，并可直接部署在新环境中。此外，只要REM采样接收器具有无人机携带的合适尺寸和重量，该系统支持REM采样而不受自身干扰，且与技术无关。在演示中，我们使用两台无人机来演示系统设计，并展示其访问72个航路点和收集数千个Wi-Fi数据样本的能力。我们的结果还包括一个ML系统的实例，用于预测无人机未访问位置处已知Wi-Fi接入点（AP）的接收信号强度（RSS）。 摘要：Radio Environmental Maps (REMs) are a powerful tool for enhancing the performance of various communication and networked agents. However, generating REMs is a laborious undertaking, especially in complex 3-Dimensional (3D) environments, such as indoors. To address this issue, we propose a system for autonomous generation of fine-grained REMs of indoor 3D spaces. In the system, multiple small indoor Unmanned Aerial Vehicles (UAVs) are sequentially used for 3D sampling of signal quality indicators. The collected readings are streamlined to a Machine Learning (ML) system for its training and, once trained, the system is able to predict the signal quality at unknown 3D locations. The system enables automated and autonomous REM generation, and can be straightforwardly deployed in new environments. In addition, the system supports REM sampling without self-interference and is technology-agnostic, as long as the REM-sampling receivers features suitable sizes and weights to be carried by the UAVs. In the demonstration, we instantiate the system design using two UAVs and show its capability of visiting 72 waypoints and gathering thousands of Wi-Fi data samples. Our results also include an instantiation of the ML system for predicting the Received Signal Strength (RSS) of known Wi-Fi Access Points (APs) at locations not visited by the UAVs.

【8】 MSC-VO: Exploiting Manhattan and Structural Constraints for Visual Odometry 标题：MSC-VO：利用曼哈顿和结构约束进行视觉里程计 链接：https://arxiv.org/abs/2111.03408

作者：Joan P. Company-Corcoles,Emilio Garcia-Fidalgo,Alberto Ortiz 机构：All authors are with the Department of Mathematics and Com-puter Science (University of the Balearic Islands) and IDISBA (Institutd’Investigacio Sanitaria de les Illes Balears) 备注：Submitted to RAL + ICRA 2022 摘要：视觉里程计算法在面对纹理较低的场景（例如，人造环境）时往往会退化，在这些场景中，通常很难找到足够数量的点特征。替代的几何视觉线索，如线，通常可以在这些场景中找到，可以变得特别有用。此外，这些场景通常呈现结构规律，如平行性或正交性，并符合曼哈顿世界假设。在这些前提下，在这项工作中，我们介绍了MSC-VO，一种基于RGB-D的视觉里程计方法，它结合了点和线特征，并利用（如果存在）这些结构规律和场景的曼哈顿轴。在我们的方法中，这些结构约束最初用于精确估计提取线的三维位置。然后将这些约束与估计的曼哈顿轴以及点和线的重投影误差相结合，通过局部地图优化来优化相机姿态。这样的组合使我们的方法即使在没有上述约束的情况下也能运行，从而使该方法适用于更广泛的场景。此外，我们提出了一种新的多视图曼哈顿轴估计方法，主要依赖于线特征。MSC-VO使用多个公共数据集进行评估，优于其他最先进的解决方案，甚至与一些SLAM方法进行了比较。 摘要：Visual odometry algorithms tend to degrade when facing low-textured scenes -from e.g. human-made environments-, where it is often difficult to find a sufficient number of point features. Alternative geometrical visual cues, such as lines, which can often be found within these scenarios, can become particularly useful. Moreover, these scenarios typically present structural regularities, such as parallelism or orthogonality, and hold the Manhattan World assumption. Under these premises, in this work, we introduce MSC-VO, an RGB-D -based visual odometry approach that combines both point and line features and leverages, if exist, those structural regularities and the Manhattan axes of the scene. Within our approach, these structural constraints are initially used to estimate accurately the 3D position of the extracted lines. These constraints are also combined next with the estimated Manhattan axes and the reprojection errors of points and lines to refine the camera pose by means of local map optimization. Such a combination enables our approach to operate even in the absence of the aforementioned constraints, allowing the method to work for a wider variety of scenarios. Furthermore, we propose a novel multi-view Manhattan axes estimation procedure that mainly relies on line features. MSC-VO is assessed using several public datasets, outperforming other state-of-the-art solutions, and comparing favourably even with some SLAM methods.

【9】 LiODOM: Adaptive Local Mapping for Robust LiDAR-Only Odometry 标题：LiODOM：用于鲁棒LiDAR里程计的自适应局部映射 链接：https://arxiv.org/abs/2111.03393

作者：Emilio Garcia-Fidalgo,Joan P. Company-Corcoles,Francisco Bonnin-Pascual,Alberto Ortiz 机构：All authors are with the Department of Mathematics and Computer Sci-ence (University of the Balearic Islands) and IDISBA (Institut d’InvestigacioSanitaria de les Illes Balears) 备注：Submitted to IEEE Robotics and Automation Letters 摘要：在过去的几十年中，光探测和测距（LiDAR）技术作为一种强大的自我定位和测绘替代方案得到了广泛的探索。这些方法通常将自我运动估计视为一个非线性优化问题，取决于当前点云和地图之间建立的对应关系，无论其范围是局部的还是全局的。本文提出了一种用于姿态估计和地图构建的新型激光雷达里程计和地图绘制方法LiODOM，该方法基于最小化由一组加权点对线对应关系导出的损失函数，并从可用点云集合中提取局部地图。此外，鉴于地图表示与快速数据关联的相关性，这项工作特别强调地图表示。为了有效地表示环境，我们提出了一种数据结构，该结构与哈希方案相结合，允许快速访问地图的任何部分。LiODOM通过在公共数据集上的一组实验进行了验证，与其他解决方案相比，LiODOM的效果更好。还报告了其在空中平台上的性能。 摘要：In the last decades, Light Detection And Ranging (LiDAR) technology has been extensively explored as a robust alternative for self-localization and mapping. These approaches typically state ego-motion estimation as a non-linear optimization problem dependent on the correspondences established between the current point cloud and a map, whatever its scope, local or global. This paper proposes LiODOM, a novel LiDAR-only ODOmetry and Mapping approach for pose estimation and map-building, based on minimizing a loss function derived from a set of weighted point-to-line correspondences with a local map abstracted from the set of available point clouds. Furthermore, this work places a particular emphasis on map representation given its relevance for quick data association. To efficiently represent the environment, we propose a data structure that combined with a hashing scheme allows for fast access to any section of the map. LiODOM is validated by means of a set of experiments on public datasets, for which it compares favourably against other solutions. Its performance on-board an aerial platform is also reported.

【10】 Cooperative Transportation of UAVs Without Inter-UAV Communication 标题：无无人机间通信的无人机协同运输 链接：https://arxiv.org/abs/2111.03283

作者：Pin-Xian Wu,Cheng-Cheng Yang,Teng-Hu Cheng 机构：NationalChiaoTungUniversity 摘要：提出了一种用于协同运输的主从式系统。据我们所知，这是第一项不需要无人机间通信的工作，可以实时修改有效载荷的参考轨迹，以便将其应用于动态变化的环境。为了在无通信条件下实时跟踪修改后的参考轨迹，将主从系统视为一个非完整系统，其中为主从系统设计了一个控制器以实现有效载荷的渐近跟踪。为了消除安装力传感器的需要，开发了UKFs（unscented Kalman filters）来估计领导者和追随者施加的力。通过稳定性分析证明了闭环系统的跟踪误差。仿真结果表明了跟踪控制器的良好性能。实验结果表明，该控制器在实际环境中可以工作，但跟踪误差受受限空间气流干扰的影响。 摘要：A leader-follower system is developed for cooperative transportation. To the best of our knowledge, this is the first work that inter-UAV communication is not required and the reference trajectory of the payload can be modified in real time, so that it can be applied to a dynamically changing environment. To track the modified reference trajectory in real time under the communication-free condition, the leader-follower system is considered as a nonholonomic system in which a controller is developed for the leader to achieve asymptotic tracking of the payload. To eliminate the need to install force sensors, UKFs (unscented Kalman filters) are developed to estimate the forces applied by the leader and follower. Stability analysis is conducted to prove the tracking error of the closed-loop system. Simulation results demonstrate the good performance of the tracking controller. The experiments show the controllers of the leader and the follower can work in the real world, but the tracking errors were affected by the disturbance of airflow in a restricted space.

【11】 RASEC: Rescaling Acquisition Strategy with Energy Constraints under SE-OU Fusion Kernel for Active Trachea Palpation and Incision Recommendation in Laryngeal Region 标题：RASEC：SE-OU融合核下基于能量约束的喉部主动气管触诊和切开建议的重缩放采集策略 链接：https://arxiv.org/abs/2111.03235

作者：Wenchao Yue,Fan Bai,Jianbang Liu,Feng Ju,Max Q-H Meng,Chwee Ming Lim,Hongliang Ren 机构：Senior Member, IEEE 备注：Submitted to RA-L(ICRA option) 摘要：在这封信中提出了一种新的基于触诊的喉区切口检测策略，可能用于机器人气管切开术。介绍了一种触觉传感器，通过温和接触测量特定喉区的组织硬度。提出了核融合方法，将平方指数（SE）核与Ornstein-Uhlenbeck（OU）核相结合，以解决现有核函数在这种情况下不够最优的缺点。此外，我们将探索因子和贪婪因子进一步正则化，并将切口定位过程中触觉传感器的移动距离和机器人基杆的旋转角度作为捕获策略中的新因素。我们进行了模拟和物理实验，以比较新提出的算法-能量约束重缩放捕获策略（RASEC）在气管检测中与当前基于触诊的捕获策略。结果表明，所提出的融合核捕获策略能够成功定位切口，算法性能最高（平均精度0.932，平均召回率0.973，平均F1得分0.952）。在机器人触诊过程中，累积移动距离减少了50%，累积旋转角度减少了71.4%，而综合性能没有损失。因此，RASEC可以有效地建议喉区的切口区域，并大大减少能量损失。 摘要：A novel palpation-based incision detection strategy in the laryngeal region, potentially for robotic tracheotomy, is proposed in this letter. A tactile sensor is introduced to measure tissue hardness in the specific laryngeal region by gentle contact. The kernel fusion method is proposed to combine the Squared Exponential (SE) kernel with Ornstein-Uhlenbeck (OU) kernel to figure out the drawbacks that the existing kernel functions are not sufficiently optimal in this scenario. Moreover, we further regularize exploration factor and greed factor, and the tactile sensor's moving distance and the robotic base link's rotation angle during the incision localization process are considered as new factors in the acquisition strategy. We conducted simulation and physical experiments to compare the newly proposed algorithm - Rescaling Acquisition Strategy with Energy Constraints (RASEC) in trachea detection with current palpation-based acquisition strategies. The result indicates that the proposed acquisition strategy with fusion kernel can successfully localize the incision with the highest algorithm performance (Average Precision 0.932, Average Recall 0.973, Average F1 score 0.952). During the robotic palpation process, the cumulative moving distance is reduced by 50%, and the cumulative rotation angle is reduced by 71.4% with no sacrifice in the comprehensive performance capabilities. Therefore, it proves that RASEC can efficiently suggest the incision zone in the laryngeal region and greatly reduced the energy loss.

【12】 A First-Order Approach to Model Simultaneous Control of Multiple Microrobots 标题：多个微型机器人模型同时控制的一阶方法 链接：https://arxiv.org/abs/2111.03232

作者：Logan E. Beaver,Sambeeta Das,Andreas A. Malikopoulos 机构： INTRODUCTIONLiving organisms that are capable of remarkable large-scale organization and coordination—the kind of which seenin fish schools, The authors are with the Department of Mechanical Engineeringat the University of Delaware 备注：7 pages, 2 figures 摘要：在宏观尺度上，对于简单的机器人平台，群体系统的控制相对来说是很好理解的。然而，关于如何在微型机器人上获得类似的结果，仍然有几个未回答的问题。在本文中，我们提出了一个基于磁化自推进Janus微型机器人在全局磁场下的动力学模型的建模框架。我们通过实验验证了我们的模型，并提供了一些方法，这些方法可以在对微机器人的同时控制进行建模时，准确地描述微机器人的行为。该模型可以推广到低雷诺数环境下的其他微机器人平台。 摘要：The control of swarm systems is relatively well understood for simple robotic platforms at the macro scale. However, there are still several unanswered questions about how similar results can be achieved for microrobots. In this paper, we propose a modeling framework based on a dynamic model of magnetized self-propelling Janus microrobots under a global magnetic field. We verify our model experimentally and provide methods that can aim at accurately describing the behavior of microrobots while modeling their simultaneous control. The model can be generalized to other microrobotic platforms in low Reynolds number environments.

【13】 LILA: Language-Informed Latent Actions 标题：莱拉：语言知晓的潜在行动 链接：https://arxiv.org/abs/2111.03205

作者：Siddharth Karamcheti,Megha Srivastava,Percy Liang,Dorsa Sadigh 机构：Department of Computer Science, Stanford University 备注：Accepted at the 5th Conference on Robot Learning (CoRL). Joint first authorship. 21 Pages, 11 Figures 摘要：我们介绍了语言信息潜在动作（LILA），一个在人-机器人协作环境中学习自然语言界面的框架。LILA属于共享自治模式：除了提供离散的语言输入外，人类还获得了一个低维控制器$-$，例如，一个2自由度（DoF）操纵杆，可以左/右和上/下移动$-$来操作机器人。LILA学习使用语言来调节该控制器，为用户提供一个语言控制空间：如果给出类似“将谷物碗放在托盘上”的指令，LILA可以学习一个2-DoF空间，其中一维控制机器人末端执行器到碗的距离，另一个维度控制机器人末端执行器相对于碗上抓取点的姿势。我们通过现实世界的用户研究来评估LILA，用户可以在操作7自由度Franka-Emika熊猫手臂以完成一系列复杂操作任务的同时提供语言指导。我们表明，LILA模型不仅比模仿学习和末端效应器控制基线更具样本效率和性能，而且在质量上也受到用户的青睐。 摘要：We introduce Language-Informed Latent Actions (LILA), a framework for learning natural language interfaces in the context of human-robot collaboration. LILA falls under the shared autonomy paradigm: in addition to providing discrete language inputs, humans are given a low-dimensional controller $-$ e.g., a 2 degree-of-freedom (DoF) joystick that can move left/right and up/down $-$ for operating the robot. LILA learns to use language to modulate this controller, providing users with a language-informed control space: given an instruction like "place the cereal bowl on the tray," LILA may learn a 2-DoF space where one dimension controls the distance from the robot's end-effector to the bowl, and the other dimension controls the robot's end-effector pose relative to the grasp point on the bowl. We evaluate LILA with real-world user studies, where users can provide a language instruction while operating a 7-DoF Franka Emika Panda Arm to complete a series of complex manipulation tasks. We show that LILA models are not only more sample efficient and performant than imitation learning and end-effector control baselines, but that they are also qualitatively preferred by users.

【14】 Value Function Spaces: Skill-Centric State Abstractions for Long-Horizon Reasoning 标题：值函数空间：面向长视野推理的以技能为中心的状态抽象 链接：https://arxiv.org/abs/2111.03189

作者：Dhruv Shah,Peng Xu,Yao Lu,Ted Xiao,Alexander Toshev,Sergey Levine,Brian Ichter 机构：Berkeley AI Research, UC Berkeley 摘要：强化学习可以训练有效执行复杂任务的策略。然而，对于长时间的任务，这些方法的性能会随着时间的推移而下降，通常需要对较低级别的技能进行推理和组合。分层强化学习旨在通过提供一系列作为动作抽象的低级技能来实现这一点。层次结构还可以通过抽象空间状态来进一步改进这一点。我们假定，合适的状态抽象应该取决于可用的较低级别策略的能力。我们提出了值函数空间：一种简单的方法，通过使用对应于每个较低级别技能的值函数来产生这种表示。这些值函数捕捉场景的启示，从而形成一个表示，该表示紧凑地抽象了任务相关信息，并稳健地忽略了干扰因素。对迷宫求解和机器人操作任务的实证评估表明，与其他无模型和基于模型的方法相比，我们的方法提高了长视野性能，并实现了更好的Zero-Shot泛化。 摘要：Reinforcement learning can train policies that effectively perform complex tasks. However for long-horizon tasks, the performance of these methods degrades with horizon, often necessitating reasoning over and composing lower-level skills. Hierarchical reinforcement learning aims to enable this by providing a bank of low-level skills as action abstractions. Hierarchies can further improve on this by abstracting the space states as well. We posit that a suitable state abstraction should depend on the capabilities of the available lower-level policies. We propose Value Function Spaces: a simple approach that produces such a representation by using the value functions corresponding to each lower-level skill. These value functions capture the affordances of the scene, thus forming a representation that compactly abstracts task relevant information and robustly ignores distractors. Empirical evaluations for maze-solving and robotic manipulation tasks demonstrate that our approach improves long-horizon performance and enables better zero-shot generalization than alternative model-free and model-based methods.

【15】 My House, My Rules: Learning Tidying Preferences with Graph Neural Networks 标题：我的房子，我的规则：用图形神经网络学习整理偏好 链接：https://arxiv.org/abs/2111.03112

作者：Ivan Kapelyukh,Edward Johns 机构：The Robot Learning Lab, Imperial College London 备注：Published at CoRL 2021. Webpage and video: this https URL 摘要：安排家庭物品的机器人应该根据用户的喜好来做，这本身就是主观的，很难建模。我们提出了NeatNet：一种新的使用图形神经网络层的变分自动编码器结构，它可以通过观察用户如何安排场景从用户那里提取低维潜在偏好向量。给定任何一组对象，然后可以使用该向量生成一个根据用户的空间偏好定制的排列，并使用单词嵌入来概括新对象。我们开发了一个整理模拟器来收集来自75个用户的重新安排示例，并以经验证明我们的方法在各种重新安排场景中始终产生整洁和个性化的安排。 摘要：Robots that arrange household objects should do so according to the user's preferences, which are inherently subjective and difficult to model. We present NeatNet: a novel Variational Autoencoder architecture using Graph Neural Network layers, which can extract a low-dimensional latent preference vector from a user by observing how they arrange scenes. Given any set of objects, this vector can then be used to generate an arrangement which is tailored to that user's spatial preferences, with word embeddings used for generalisation to new objects. We develop a tidying simulator to gather rearrangement examples from 75 users, and demonstrate empirically that our method consistently produces neat and personalised arrangements across a variety of rearrangement scenarios.

【16】 Modeling and Control of an Omnidirectional Micro Aerial Vehicle Equipped with a Soft Robotic Arm 标题：带软机械臂的全方位微型飞行器建模与控制 链接：https://arxiv.org/abs/2111.03111

作者：Róbert Szász,Mike Allenspach,Minghao Han,Marco Tognon,Robert. K. Katzschmann 摘要：飞行机械手是带有刚性机械臂的空中无人机，属于机器人学中最新和最活跃的研究领域。这些手臂的刚性通常缺乏柔韧性、灵活性和运动的平稳性。这项工作建议使用连接到全向微型飞行器（OMAV）的软体机械臂来利用机械臂的柔顺和灵活行为，同时由于全向无人机作为浮动基座，保持机动性和动态性。arm与无人机的统一对这种组合平台的建模和控制提出了挑战；这项工作解决了这些挑战。我们提出了一个基于三个建模原则的飞行机械手统一模型：用于软连续介质机器人建模的分段常曲率（PCC）和增强刚体模型（ARBM）假设，以及借鉴传统刚体机器人学文献的浮动基座方法。为了证明这种参数化的有效性和实用性，实现了一种基于分层模型的反馈控制器。在各种动力学任务的仿真中，对控制器进行了验证和评估，其中对平台的零空间运动、干扰恢复和轨迹跟踪能力进行了检查和验证。该柔性飞行机械手平台可以在空中建筑、货物运输、人工辅助、维护和仓库自动化等领域开辟新的应用领域。 摘要：Flying manipulators are aerial drones with attached rigid-bodied robotic arms and belong to the latest and most actively developed research areas in robotics. The rigid nature of these arms often lack compliance, flexibility, and smoothness in movement. This work proposes to use a soft-bodied robotic arm attached to an omnidirectional micro aerial vehicle (OMAV) to leverage the compliant and flexible behavior of the arm, while remaining maneuverable and dynamic thanks to the omnidirectional drone as the floating base. The unification of the arm with the drone poses challenges in the modeling and control of such a combined platform; these challenges are addressed with this work. We propose a unified model for the flying manipulator based on three modeling principles: the Piecewise Constant Curvature (PCC) and Augmented Rigid Body Model (ARBM) hypotheses for modeling soft continuum robots and a floating-base approach borrowed from the traditional rigid-body robotics literature. To demonstrate the validity and usefulness of this parametrisation, a hierarchical model-based feedback controller is implemented. The controller is verified and evaluated in simulation on various dynamical tasks, where the nullspace motions, disturbance recovery, and trajectory tracking capabilities of the platform are examined and validated. The soft flying manipulator platform could open new application fields in aerial construction, goods delivery, human assistance, maintenance, and warehouse automation.

【17】 Learning to Manipulate Tools by Aligning Simulation to Video Demonstration 标题：通过使模拟与视频演示保持一致来学习操作工具 链接：https://arxiv.org/abs/2111.03088

作者：Kateryna Zorina,Justin Carpentier,Josef Sivic,Vladimír Petrík 机构：Petr´ıkarewithCzechInstituteofInformatics, CzechTechnicalUniversityinPrague{kateryna 备注：Accepted to IEEE Robotics and Automation Letters (RA-L) 摘要：机器人与人类环境的无缝集成要求机器人学会如何使用现有的人类工具。当前学习工具操作技能的方法主要依赖于目标机器人环境中提供的专家演示，例如，通过手动引导机器人操作器或通过遥控操作。在这项工作中，我们介绍了一种自动方法，用Youtube视频代替专家演示，用于学习工具操作策略。主要贡献有两方面。首先，我们设计了一个对齐过程，将模拟环境与视频中观察到的真实场景对齐。这被描述为一个优化问题，该问题找到刀具轨迹的空间对齐，以最大化环境给予的稀疏目标回报。其次，我们描述了一种模仿学习方法，该方法侧重于工具的轨迹，而不是人的运动。为此，我们将强化学习与优化过程相结合，根据对齐环境中的刀具运动找到控制策略和机器人的位置。我们在仿真中展示了所提出的方法在铁锹、镰刀和锤子工具上的应用，并在一个真实的Franka Emika熊猫机器人演示中展示了铁锹训练策略的有效性。 摘要：A seamless integration of robots into human environments requires robots to learn how to use existing human tools. Current approaches for learning tool manipulation skills mostly rely on expert demonstrations provided in the target robot environment, for example, by manually guiding the robot manipulator or by teleoperation. In this work, we introduce an automated approach that replaces an expert demonstration with a Youtube video for learning a tool manipulation strategy. The main contributions are twofold. First, we design an alignment procedure that aligns the simulated environment with the real-world scene observed in the video. This is formulated as an optimization problem that finds a spatial alignment of the tool trajectory to maximize the sparse goal reward given by the environment. Second, we describe an imitation learning approach that focuses on the trajectory of the tool rather than the motion of the human. For this we combine reinforcement learning with an optimization procedure to find a control policy and the placement of the robot based on the tool motion in the aligned environment. We demonstrate the proposed approach on spade, scythe and hammer tools in simulation, and show the effectiveness of the trained policy for the spade on a real Franka Emika Panda robot demonstration.