机器人相关学术速递[12.14]

cs.RO机器人相关，共计25篇

【1】 Generalization Bounds for Implicit Learning of Nearly Discontinuous Functions 标题：几乎不连续函数内隐学习的广义界 链接：https://arxiv.org/abs/2112.06881

作者：Bibit Bianchini,Mathew Halm,Nikolai Matni,Michael Posa 机构：University of Pennsylvania, Philadelphia, PA 备注：22 pages, 3 figures 摘要：受许多机器人技术任务中内隐学习的经验功效最近取得的进展的启发，我们试图了解在几乎不连续的功能面前，内隐公式的理论优势，以及与环境接触和中断的系统的共同特征，如腿部运动和操纵。我们提出并激励学习函数的三个公式：一个显式公式和两个隐式公式。我们为这三种方法中的每一种都推导了泛化边界，揭示了基于预测误差损失的显式和隐式方法通常无法产生紧边界的地方，与其他基于冲突的损失定义的隐式方法相比，这些方法对陡坡从根本上来说更具鲁棒性。此外，我们证明了这种违反隐式损失可以紧密地结合图距离，这是一个经常有物理根和处理输入和输出中的噪声的量，而不是只考虑输出噪声的预测损失。我们对违反隐式公式的普遍性和物理相关性的见解与先前工作的证据相符，并通过一个玩具问题得到验证，该问题受到刚性接触模型的启发，并在我们的整个理论分析中引用。 摘要：Inspired by recent strides in empirical efficacy of implicit learning in many robotics tasks, we seek to understand the theoretical benefits of implicit formulations in the face of nearly discontinuous functions, common characteristics for systems that make and break contact with the environment such as in legged locomotion and manipulation. We present and motivate three formulations for learning a function: one explicit and two implicit. We derive generalization bounds for each of these three approaches, exposing where explicit and implicit methods alike based on prediction error losses typically fail to produce tight bounds, in contrast to other implicit methods with violation-based loss definitions that can be fundamentally more robust to steep slopes. Furthermore, we demonstrate that this violation implicit loss can tightly bound graph distance, a quantity that often has physical roots and handles noise in inputs and outputs alike, instead of prediction losses which consider output noise only. Our insights into the generalizability and physical relevance of violation implicit formulations match evidence from prior works and are validated through a toy problem, inspired by rigid-contact models and referenced throughout our theoretical analysis.

【2】 Multi-Robot On-site Shared Analytics Information and Computing 标题：多机器人现场共享分析信息与计算 链接：https://arxiv.org/abs/2112.06879

作者：Joshua Vander Hook,Federico Rossi,Tiago Vaquero,Martina Troesch,Marc Sanchez Net,Joshua Schoolcraft,Jean-Pierre de la Croix,Steve Chien 机构： Joshua Schoolcraft 备注：14 pages, 11 figures. Extended version of journal submission in preparation 摘要：跨异构机器人网络的计算负载共享是在极端环境中提高机器人能力和团队效率的一种很有前途的方法。然而，在这种环境中，通信链路可能是断断续续的，与云或互联网的连接可能不存在。在本文中，我们介绍了一个多机器人系统的通信感知计算任务调度问题，并提出了一个整数线性规划（ILP），用于优化异构机器人网络中计算任务的分配，考虑联网机器人的计算能力和可用（可能是时变）通信链路。我们考虑由依赖图建模的一组相互依赖的所需和可选任务的调度。我们为共享世界、分布式系统提供了一个一致支持的调度体系结构。我们在不同的计算平台和偏向月球或行星探测场景的模拟场景中验证了ILP公式和分布式实现。我们的结果表明，与没有计算负载共享的类似系统相比，所提出的实现可以优化调度，允许执行的奖励任务（例如，科学测量）数量增加三倍。 摘要：Computation load-sharing across a network of heterogeneous robots is a promising approach to increase robots capabilities and efficiency as a team in extreme environments. However, in such environments, communication links may be intermittent and connections to the cloud or internet may be nonexistent. In this paper we introduce a communication-aware, computation task scheduling problem for multi-robot systems and propose an integer linear program (ILP) that optimizes the allocation of computational tasks across a network of heterogeneous robots, accounting for the networked robots' computational capabilities and for available (and possibly time-varying) communication links. We consider scheduling of a set of inter-dependent required and optional tasks modeled by a dependency graph. We present a consensus-backed scheduling architecture for shared-world, distributed systems. We validate the ILP formulation and the distributed implementation in different computation platforms and in simulated scenarios with a bias towards lunar or planetary exploration scenarios. Our results show that the proposed implementation can optimize schedules to allow a threefold increase the amount of rewarding tasks performed (e.g., science measurements) compared to an analogous system with no computational load-sharing.

【3】 RSV: Robotic Sonography for Thyroid Volumetry 标题：RSV：用于甲状腺体积测量的机器人超声检查 链接：https://arxiv.org/abs/2112.06761

作者：John Zielke,Christine Eilers,Benjamin Busam,Wolfgang Weber,Nassir Navab,Thomas Wendler 机构： John HopkinsUniversity 备注：This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible 摘要：在核医学中，放射性碘疗法被用于治疗甲亢等疾病。除其他因素外，规定剂量的计算取决于甲状腺体积。这是目前使用常规二维超声成像估计的。然而，这种模式本质上取决于用户，导致容量估计的高度可变性。为了提高再现性和一致性，我们将基于神经网络的分割与自动机器人超声扫描相结合，用于甲状腺容量测定。机器人采集是通过使用6自由度机械臂和附加的超声波探头实现的。它的运动基于对每个甲状腺叶的在线分割和美国图像的外观。在后处理过程中，对美国图像进行分割以获得体积估计。在一项消融研究中，我们展示了机器人手臂运动的运动引导算法相对于由机器人执行的简单线性运动在体积精度方面的优越性。在一项关于人体模型的用户研究中，我们将传统的二维超声测量与我们的机器人系统进行了比较。与地面真实值相比，超声专家用户的平均体积测量误差可从20.85+/-16.10%显著降低至8.23+/-3.10%。这一趋势在非专家用户中更为明显，机器人系统的平均误差改善率高达85\%$，这清楚地表明了机器人支持的优势。 摘要：In nuclear medicine, radioiodine therapy is prescribed to treat diseases like hyperthyroidism. The calculation of the prescribed dose depends, amongst other factors, on the thyroid volume. This is currently estimated using conventional 2D ultrasound imaging. However, this modality is inherently user-dependant, resulting in high variability in volume estimations. To increase reproducibility and consistency, we uniquely combine a neural network-based segmentation with an automatic robotic ultrasound scanning for thyroid volumetry. The robotic acquisition is achieved by using a 6 DOF robotic arm with an attached ultrasound probe. Its movement is based on an online segmentation of each thyroid lobe and the appearance of the US image. During post-processing, the US images are segmented to obtain a volume estimation. In an ablation study, we demonstrated the superiority of the motion guidance algorithms for the robot arm movement compared to a naive linear motion, executed by the robot in terms of volumetric accuracy. In a user study on a phantom, we compared conventional 2D ultrasound measurements with our robotic system. The mean volume measurement error of ultrasound expert users could be significantly decreased from 20.85+/-16.10% to only 8.23+/-3.10% compared to the ground truth. This tendency was observed even more in non-expert users where the mean error improvement with the robotic system was measured to be as high as $85\%$ which clearly shows the advantages of the robotic support.

【4】 Adaptation through prediction: multisensory active inference torque control 标题：预测自适应：多感官主动推理转矩控制 链接：https://arxiv.org/abs/2112.06752

作者：Cristian Meo,Giovanni Franzese,Corrado Pezzato,Max Spahn,Pablo Lanillos 机构： Department of Cognitive Robotics, Delft University of Technology, Donders Institute for Brain, Department ofArtificial Intelligence, Radboud University 备注：arXiv admin note: text overlap with arXiv:2103.04412 摘要：适应外部和内部变化是机器人系统在不确定环境中的主要任务。在这里，我们提出了一种新的多传感器主动推理扭矩控制器的工业武器，显示如何预测可以用来解决适应。我们的控制器受预测性大脑假设的启发，通过结合低维和高维传感器输入（如原始图像）的学习和多模式集成，改进了当前主动推理方法的能力，同时简化了体系结构。我们在7自由度Franka-Emika熊猫机器人手臂上对我们的模型进行了系统评估，将其行为与之前的主动推理基线和经典控制器进行了比较，定性和定量地分析了自适应能力和控制精度。结果表明，多模态滤波提高了目标定向到达的控制精度和高噪声抑制能力，并且能够适应动态惯性变化、弹性约束和人为干扰，无需重新学习模型或参数重新调整。 摘要：Adaptation to external and internal changes is major for robotic systems in uncertain environments. Here we present a novel multisensory active inference torque controller for industrial arms that shows how prediction can be used to resolve adaptation. Our controller, inspired by the predictive brain hypothesis, improves the capabilities of current active inference approaches by incorporating learning and multimodal integration of low and high-dimensional sensor inputs (e.g., raw images) while simplifying the architecture. We performed a systematic evaluation of our model on a 7DoF Franka Emika Panda robot arm by comparing its behavior with previous active inference baselines and classic controllers, analyzing both qualitatively and quantitatively adaptation capabilities and control accuracy. Results showed improved control accuracy in goal-directed reaching with high noise rejection due to multimodal filtering, and adaptability to dynamical inertial changes, elasticity constraints and human disturbances without the need to relearn the model nor parameter retuning.

【5】 Firefly: Supporting Drone Localization With Visible Light Communication 标题：萤火虫：用可见光通信支持无人机定位 链接：https://arxiv.org/abs/2112.06677

作者：Ricardo Ampudia Hernández,Talia Xu,Yanqiu Huang,Marco A. Zúñiga Zamalloa 机构：∗ ‡ Department of Computer Science, University of Twente, The Netherlands, † § Department of Computer Science, TU Delft, The Netherlands 摘要：无人驾驶飞机还没有被完全信任。他们对无线电和摄像机导航的依赖引起了安全和隐私问题。这些系统可能会出现故障，导致事故，或被误用用于未经授权的录制。考虑到最近允许商用无人机仅在夜间运行的法规，我们提出了一种全新的方法，即无人机通过人工照明获取导航信息。在我们的系统中，标准灯泡调节其强度以发送信标，无人机用简单的光电二极管解码这些信息。该光学信息与无人机中的惯性和高度传感器相结合，以提供定位，而无需无线电、GPS或摄像机。我们的框架是第一个提供3D无人机灯光定位的框架，我们通过一个由四个灯光信标和一个小型无人机组成的试验台对其进行评估。我们表明，我们的方法允许将无人机定位在实际位置的几分米范围内，与最先进的定位方法相比，定位误差降低了42%。 摘要：Drones are not fully trusted yet. Their reliance on radios and cameras for navigation raises safety and privacy concerns. These systems can fail, causing accidents, or be misused for unauthorized recordings. Considering recent regulations allowing commercial drones to operate only at night, we propose a radically new approach where drones obtain navigation information from artificial lighting. In our system, standard light bulbs modulate their intensity to send beacons and drones decode this information with a simple photodiode. This optical information is combined with the inertial and altitude sensors in the drones to provide localization without the need for radios, GPS or cameras. Our framework is the first to provide 3D drone localization with light and we evaluate it with a testbed consisting of four light beacons and a mini-drone. We show that, our approach allows to locate the drone within a few decimeters of the actual position and compared to state-of-the-art positioning methods, reduces the localization error by 42%.

【6】 A Review: Challenges and Opportunities for Artificial Intelligence and Robotics in the Offshore Wind Sector 标题：综述：人工智能和机器人在近海风电领域的挑战和机遇 链接：https://arxiv.org/abs/2112.06620

作者：Daniel Mitchell,Jamie Blanche,Sam Harper,Theodore Lim,Ranjeetkumar Gupta,Osama Zaki,Wenshuo Tang,Valentin Robu,Simon Watson,David Flynn 机构：Smart Systems Group, Institute of Sensors, Signals and Systems, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, EH,AS, U.K. 备注：36 figures, 49 pages 摘要：在快速增长的海上风电场市场中，风力涡轮机尺寸和离海岸距离不断增加的全球趋势正在形成。在英国，2019年海上风电行业的发电量为英国最高，比前一年增长19.6%。目前，英国将进一步增加产量，目标是将涡轮机装机容量增加74.7%，这反映在最近的皇冠地产租赁回合中。随着如此巨大的增长，该行业现在正在寻求机器人技术和人工智能（RAI），以解决生命周期服务障碍，从而支持可持续和盈利的海上风能生产。如今，RAI应用程序主要用于支持运行和维护的短期目标。然而，展望未来，RAI有可能在海上风电基础设施的整个生命周期中发挥关键作用，包括测量、规划、设计、后勤、运营支持、训练和退役。本文介绍了海上可再生能源行业RAI的第一次系统回顾。从行业和学术界的角度，根据当前和未来的需求，分析了RAI在海上能源需求方面的最新进展。我们的审查还包括对支持采用RAI所需的投资、监管和技能发展的详细评估。通过对专利和学术出版物数据库的详细分析确定的关键趋势提供了对障碍的洞察，如自主平台的安全合规性和可靠性认证，自主车队中可扩展性的数字架构需求，适应性任务规划，用于弹性常驻操作和优化人机交互，以实现人与自主助手之间的可信伙伴关系。 摘要：A global trend in increasing wind turbine size and distances from shore is emerging within the rapidly growing offshore wind farm market. In the UK, the offshore wind sector produced its highest amount of electricity in the UK in 2019, a 19.6% increase on the year before. Currently, the UK is set to increase production further, targeting a 74.7% increase of installed turbine capacity as reflected in recent Crown Estate leasing rounds. With such tremendous growth, the sector is now looking to Robotics and Artificial Intelligence (RAI) in order to tackle lifecycle service barriers as to support sustainable and profitable offshore wind energy production. Today, RAI applications are predominately being used to support short term objectives in operation and maintenance. However, moving forward, RAI has the potential to play a critical role throughout the full lifecycle of offshore wind infrastructure, from surveying, planning, design, logistics, operational support, training and decommissioning. This paper presents one of the first systematic reviews of RAI for the offshore renewable energy sector. The state-of-the-art in RAI is analyzed with respect to offshore energy requirements, from both industry and academia, in terms of current and future requirements. Our review also includes a detailed evaluation of investment, regulation and skills development required to support the adoption of RAI. The key trends identified through a detailed analysis of patent and academic publication databases provide insights to barriers such as certification of autonomous platforms for safety compliance and reliability, the need for digital architectures for scalability in autonomous fleets, adaptive mission planning for resilient resident operations and optimization of human machine interaction for trusted partnerships between people and autonomous assistants.

【7】 Multi-agent Soft Actor-Critic Based Hybrid Motion Planner for Mobile Robots 标题：基于多智能体软角色-批评者的移动机器人混合运动规划器 链接：https://arxiv.org/abs/2112.06594

作者：Zichen He,Lu Dong,Chunwei Song,Changyin Sun 机构： SoutheastUniversity, Sun is with the School of Automation 摘要：本文提出了一种新型的混合式多机器人运动规划器，可应用于非通信和局部可观测条件下。该规划器是无模型的，可以实现多机器人状态和观测信息的端到端映射到最终平滑连续的轨迹。planner是一个前端和后端分离的体系结构。前端协同航路点搜索模块的设计是在集中训练和分散执行图的基础上，基于多智能体软参与者评判算法。后端轨迹优化模块的设计基于带安全区约束的最小捕捉法。该模块可以输出最终的动态可行可执行轨迹。最后，多组实验结果验证了该运动规划器的有效性。 摘要：In this paper, a novel hybrid multi-robot motion planner that can be applied under non-communication and local observable conditions is presented. The planner is model-free and can realize the end-to-end mapping of multi-robot state and observation information to final smooth and continuous trajectories. The planner is a front-end and back-end separated architecture. The design of the front-end collaborative waypoints searching module is based on the multi-agent soft actor-critic algorithm under the centralized training with decentralized execution diagram. The design of the back-end trajectory optimization module is based on the minimal snap method with safety zone constraints. This module can output the final dynamic-feasible and executable trajectories. Finally, multi-group experimental results verify the effectiveness of the proposed motion planner.

【8】 MinkLoc3D-SI: 3D LiDAR place recognition with sparse convolutions, spherical coordinates, and intensity 标题：MinkLoc3D-SI：使用稀疏卷积、球面坐标和强度的3D LiDAR位置识别 链接：https://arxiv.org/abs/2112.06539

作者：Kamil Żywanowski,Adam Banaszczyk,Michał R. Nowicki,Jacek Komorowski 机构：©, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media 摘要：三维激光雷达位置识别旨在基于旋转三维激光雷达传感器的单次扫描，估计先前所见环境中的粗略定位。该问题的现有解决方案包括手工制作的点云描述符（例如ScanContext、M2DP、LiDAR IRIS）和基于深度学习的解决方案（例如PointNetVLAD、PCAN、LPDNet、DAGC、MinkLoc3D），这些解决方案通常仅根据牛津RobotCar数据集的累积2D扫描进行评估。我们介绍了MinkLoc3D SI，这是一种基于稀疏卷积的解决方案，利用3D点的球坐标并处理3D激光雷达测量的强度，提高了使用单个3D激光雷达扫描时的性能。我们的方法将手工描述符（如ScanContext）的典型改进与最有效的3D稀疏卷积（MinkLoc3D）相结合。我们的实验表明，从三维激光雷达（USyd校园数据集）和强大的泛化能力（KITTI数据集）单次扫描的结果得到了改进。在累积2D扫描（RobotCar intensity dataset）上使用强度信息可以提高性能，即使球形表示不会产生明显的改进。因此，MinkLoc3D SI适用于从3D激光雷达获得的单次扫描，使其适用于自动驾驶车辆。 摘要：The 3D LiDAR place recognition aims to estimate a coarse localization in a previously seen environment based on a single scan from a rotating 3D LiDAR sensor. The existing solutions to this problem include hand-crafted point cloud descriptors (e.g., ScanContext, M2DP, LiDAR IRIS) and deep learning-based solutions (e.g., PointNetVLAD, PCAN, LPDNet, DAGC, MinkLoc3D), which are often only evaluated on accumulated 2D scans from the Oxford RobotCar dataset. We introduce MinkLoc3D-SI, a sparse convolution-based solution that utilizes spherical coordinates of 3D points and processes the intensity of 3D LiDAR measurements, improving the performance when a single 3D LiDAR scan is used. Our method integrates the improvements typical for hand-crafted descriptors (like ScanContext) with the most efficient 3D sparse convolutions (MinkLoc3D). Our experiments show improved results on single scans from 3D LiDARs (USyd Campus dataset) and great generalization ability (KITTI dataset). Using intensity information on accumulated 2D scans (RobotCar Intensity dataset) improves the performance, even though spherical representation doesn't produce a noticeable improvement. As a result, MinkLoc3D-SI is suited for single scans obtained from a 3D LiDAR, making it applicable in autonomous vehicles.

【9】 Aerial Chasing of a Dynamic Target in Complex Environments 标题：复杂环境下动态目标的空中追逐 链接：https://arxiv.org/abs/2112.06474

作者：Boseong Felipe Jeon,Changhyeon Kim,Hojoon Shin,H. Jin Kim 机构： Corresponding author., This material is based upon work supported by the Ministry of Trade, Industry & Energy(MOTIE, Korea) under Industrial Technology Inno- 备注：10 pages 摘要：由于多个冲突目标和非凸约束引起的数值问题，快速生成无人机在障碍物中跟随动态目标的最优追逐运动具有挑战性。本研究建议使用一种快速可靠的管道来解决这些困难，该管道包括1）目标移动预报器和2）追踪计划器。它们基于抽样检查方法，该方法包括生成高质量的候选原语和具有较轻计算负载的可行性测试。我们通过在一组根据过去的观察结果构建的候选对象中选择最佳预测来预测目标的移动。在预测的基础上，我们构造了一组前瞻性的追踪轨迹，减少了高阶导数，同时保持了与预测目标运动的期望相对距离。然后，在没有松散近似约束的情况下，测试候选轨迹对追逐器的安全性和对目标的可见性。在涉及动态障碍物的挑战场景中，对所提出的算法进行了全面评估。此外，从目标识别到追逐运动规划的整个过程在无人机上完全实现，证明了现实世界的适用性。 摘要：Rapidly generating an optimal chasing motion of a drone to follow a dynamic target among obstacles is challenging due to numerical issues rising from multiple conflicting objectives and non-convex constraints. This study proposes to resolve the difficulties with a fast and reliable pipeline that incorporates 1) a target movement forecaster and 2) a chasing planner. They are based on a sample-and-check approach that consists of the generation of high-quality candidate primitives and the feasibility tests with a light computation load. We forecast the movement of the target by selecting an optimal prediction among a set of candidates built from past observations. Based on the prediction, we construct a set of prospective chasing trajectories which reduce the high-order derivatives, while maintaining the desired relative distance from the predicted target movement. Then, the candidate trajectories are tested on safety of the chaser and visibility toward the target without loose approximation of the constraints. The proposed algorithm is thoroughly evaluated in challenging scenarios involving dynamic obstacles. Also, the overall process from the target recognition to the chasing motion planning is implemented fully onboard on a drone, demonstrating real-world applicability.

【10】 Contact-Rich Manipulation of a Flexible Object based on Deep Predictive Learning using Vision and Tactility 标题：基于视觉和触觉的深度预测学习对柔性物体的富接触操作 链接：https://arxiv.org/abs/2112.06442

作者：Hideyuki Ichiwara,Hiroshi Ito,Kenjiro Yamamoto,Hiroki Mori,Tetsuya Ogata 机构： Hiroki Mori and Tetsuya Ogata are with Department of In-termedia Art and Science School of Fundamental Science and Engineering, Waseda University 摘要：实现了单凭视觉难以控制的接触丰富的柔性物体操纵。在我们选择作为验证任务的解压任务中，夹持器抓住拉具，这隐藏了袋子的状态，例如其背后的变形方向和变形量，使得仅凭视觉很难获得执行任务的信息。此外，柔性织物袋的状态在操作过程中不断变化，因此机器人需要动态响应变化。然而，很难提前准备适合所有行李状态的机器人行为。为了解决这个问题，我们开发了一个模型，该模型可以通过实时预测触觉视觉来执行接触丰富的柔性对象操作。我们介绍了一种用于提取图像特征的基于点的注意机制，用于预测运动的softmax变换，以及用于提取触觉特征的卷积神经网络。使用真实机器人手臂的实验结果表明，我们的方法可以实现响应袋子变形的运动，同时减少拉链上的负载。此外，与仅使用视觉相比，使用触觉将成功率从56.7%提高到93.3%，证明了我们方法的有效性和高性能。 摘要：We achieved contact-rich flexible object manipulation, which was difficult to control with vision alone. In the unzipping task we chose as a validation task, the gripper grasps the puller, which hides the bag state such as the direction and amount of deformation behind it, making it difficult to obtain information to perform the task by vision alone. Additionally, the flexible fabric bag state constantly changes during operation, so the robot needs to dynamically respond to the change. However, the appropriate robot behavior for all bag states is difficult to prepare in advance. To solve this problem, we developed a model that can perform contact-rich flexible object manipulation by real-time prediction of vision with tactility. We introduced a point-based attention mechanism for extracting image features, softmax transformation for predicting motions, and convolutional neural network for extracting tactile features. The results of experiments using a real robot arm revealed that our method can realize motions responding to the deformation of the bag while reducing the load on the zipper. Furthermore, using tactility improved the success rate from 56.7% to 93.3% compared with vision alone, demonstrating the effectiveness and high performance of our method.

【11】 Competitive Car Racing with Multiple Vehicles using a Parallelized Optimization with Safety Guarantee 标题：基于安全保障并行优化的好胜多车竞速 链接：https://arxiv.org/abs/2112.06435

作者：Suiyi He,Jun Zeng,Koushil Sreenath 机构： 1Author is with the Department of Mechanical Engineering, Universityof Minnesota-Twin Cities, University of California 备注：8 pages (long version), a brief version submitted to 2022 International Conference on Robotics and Automation (ICRA) 摘要：本文提出了一种新的规划和控制策略，用于在赛车场景中与多辆车竞争。建议的赛车策略在两种模式之间切换。当周围没有车辆时，使用基于学习的模型预测控制（MPC）轨迹规划器来保证ego车辆获得更好的圈速。当ego车辆与周围其他车辆竞争超车时，基于优化的规划器通过并行计算生成多条动态可行的轨迹。每个轨迹在MPC公式下进行优化，不同的同伦Bezier曲线参考路径横向位于周围车辆之间。在这些不同的同伦轨迹之间选择时间最优轨迹，并使用具有避障约束的低级MPC控制器来保证系统的安全临界性能。该算法能够生成无碰撞轨迹并对其进行跟踪，同时以稳定的低计算复杂度提高了单圈计时性能，在计时和赛车环境性能方面均优于现有方法。为了证明我们的赛车策略的性能，我们在赛道上模拟了多个随机生成的移动车辆，并测试了ego车辆的超车动作。 摘要：This paper presents a novel planning and control strategy for competing with multiple vehicles in a car racing scenario. The proposed racing strategy switches between two modes. When there are no surrounding vehicles, a learning-based model predictive control (MPC) trajectory planner is used to guarantee that the ego vehicle achieves better lap timing. When the ego vehicle is competing with other surrounding vehicles to overtake, an optimization-based planner generates multiple dynamically-feasible trajectories through parallel computation. Each trajectory is optimized under a MPC formulation with different homotopic Bezier-curve reference paths lying laterally between surrounding vehicles. The time-optimal trajectory among these different homotopic trajectories is selected and a low-level MPC controller with obstacle avoidance constraints is used to guarantee system safety-critical performance. The proposed algorithm has the capability to generate collision-free trajectories and track them while enhancing the lap timing performance with steady low computational complexity, outperforming existing approaches in both timing and performance for a car racing environment. To demonstrate the performance of our racing strategy, we simulate with multiple randomly generated moving vehicles on the track and test the ego vehicle's overtake maneuvers.

【12】 Human-like Driving Decision at Unsignalized Intersections Based on Game Theory 标题：基于博弈论的无信号交叉口仿人驾驶决策 链接：https://arxiv.org/abs/2112.06415

作者：Daofei Li,Guanming Liu,Bin Xiao 备注：17 pages, 11 figures, 2 tables 摘要：无信号交叉口驾驶对于自动化车辆来说是一项挑战。为了安全高效地运行，应考虑相互作用车辆的多样性和动态行为。基于博弈论框架，提出了一种用于无信号交叉口自动决策的仿人支付设计方法。引入前景理论，将客观碰撞风险映射到驾驶员的主观回报，驾驶风格可以量化为安全性和速度之间的权衡。为了解释相互作用的动力学，进一步引入了一个概率模型来描述驾驶员的加速趋势。仿真结果表明，所提出的决策算法能够描述极限情况下两车相互作用的动态过程。对均匀采样情况的统计仿真表明，在保证速度效率的同时，安全交互成功率达到98%。该方法在四臂交叉口的四种车辆交互场景中得到了进一步应用和验证。 摘要：Unsignalized intersection driving is challenging for automated vehicles. For safe and efficient performances, the diverse and dynamic behaviors of interacting vehicles should be considered. Based on a game-theoretic framework, a human-like payoff design methodology is proposed for the automated decision at unsignalized intersections. Prospect Theory is introduced to map the objective collision risk to the subjective driver payoffs, and the driving style can be quantified as a tradeoff between safety and speed. To account for the dynamics of interaction, a probabilistic model is further introduced to describe the acceleration tendency of drivers. Simulation results show that the proposed decision algorithm can describe the dynamic process of two-vehicle interaction in limit cases. Statistics of uniformly-sampled cases simulation indicate that the success rate of safe interaction reaches 98%, while the speed efficiency can also be guaranteed. The proposed approach is further applied and validated in four-vehicle interaction scenarios at a four-arm intersection.

【13】 MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasets 标题：MotionBenchMaker：一个生成和基准运动规划数据集的工具 链接：https://arxiv.org/abs/2112.06402

作者：Constantinos Chamzas,Carlos Quintero-Peña,Zachary Kingston,Andreas Orthey,Daniel Rakita,Michael Gleicher,Marc Toussaint,Lydia E. Kavraki 机构：RiceUniversity 备注：accepted in IEEE Robotics and Automation Letters (RAL), 2022. Supplementary video: this https URL Code: this https URL 摘要：最近，机器人操作的运动规划有了大量的发展，新的运动规划不断被提出，每一种都有其独特的优点和缺点。然而，评估新的规划师是一项挑战，研究人员经常为基准制定他们自己的特殊问题，这很耗时，容易产生偏见，并且不能直接与其他最先进的规划师进行比较。我们介绍了MotionBenchmarker，一个开源工具，用于为现实机器人操作问题生成基准数据集。MotionBenchmarker是一个可扩展、易于使用的工具，允许用户通过比较运动规划算法生成数据集并对其进行基准测试。从经验上看，我们展示了使用MotionBenchmarker作为程序生成数据集的工具的好处，这有助于公平评估规划者。我们还提供了一套40个预制数据集，其中包括8个环境中5个不同的常用机器人，作为加速运动规划研究的共同基础。 摘要：Recently, there has been a wealth of development in motion planning for robotic manipulation new motion planners are continuously proposed, each with their own unique strengths and weaknesses. However, evaluating new planners is challenging and researchers often create their own ad-hoc problems for benchmarking, which is time-consuming, prone to bias, and does not directly compare against other state-of-the-art planners. We present MotionBenchMaker, an open-source tool to generate benchmarking datasets for realistic robot manipulation problems. MotionBenchMaker is designed to be an extensible, easy-to-use tool that allows users to both generate datasets and benchmark them by comparing motion planning algorithms. Empirically, we show the benefit of using MotionBenchMaker as a tool to procedurally generate datasets which helps in the fair evaluation of planners. We also present a suite of 40 prefabricated datasets, with 5 different commonly used robots in 8 environments, to serve as a common ground to accelerate motion planning research.

【14】 A Cluster-Based Weighted Feature Similarity Moving Target Tracking Algorithm for Automotive FMCW Radar 标题：一种基于聚类加权特征相似度的车载FMCW雷达运动目标跟踪算法 链接：https://arxiv.org/abs/2112.06388

作者：Rongqian Chen,Yingquan Zou,Anyong Gao,Leshi Chen 机构：School of Information Science and, Technology, South-west Jiaotong University, Chengdu, China, School of Computing and Artificial, Intelligence 摘要：研究了一种自主驾驶环境下基于毫米波雷达的目标跟踪算法。针对目标跟踪阶段的聚类匹配问题，提出了一种新的加权特征相似度算法，提高了强环境噪声和多个干扰目标下相邻帧中同一目标的匹配率。针对自主驾驶场景，提出了一种利用运动参数提取和修正运动目标轨迹的方法，解决了车辆运动过程中的运动目标检测和轨迹修正问题。最后，在自主驾驶环境下进行了一系列实验，验证了该方法的可行性。结果表明，该方法具有较高的识别精度和较低的定位误差。 摘要：We studied a target tracking algorithm based on millimeter-wave (MMW) radar in an autonomous driving environment. Aiming at the cluster matching in the target tracking stage, a new weighted feature similarity algorithm is proposed, which increases the matching rate of the same target in adjacent frames under strong environmental noise and multiple interference targets. For autonomous driving scenarios, we constructed a method that uses its motion parameters to extract and correct the trajectory of a moving target, which solves the problem of moving target detection and trajectory correction during vehicle movement. Finally, the feasibility of the proposed method was verified by a series of experiments in autonomous driving environments. The results verify the high recognition accuracy and low positional error of the method.

【15】 Learning Generalizable Vision-Tactile Robotic Grasping Strategy for Deformable Objects via Transformer 标题：基于变形器的可学习泛化视觉触觉机器人抓取策略 链接：https://arxiv.org/abs/2112.06374

作者：Yunhai Han,Rahul Batra,Nathan Boyd,Tuo Zhao,Yu She,Seth Hutchinson,Ye Zhao 机构： 2School of Industrial and Systems Engineering, Georgia Institute ofTechnology (email 备注：This paper is submitted to RA-L 摘要：可靠的机器人抓取，特别是对可变形物体（如水果）的抓取，仍然是一项具有挑战性的任务，因为与抓取器的欠驱动接触交互作用、未知物体动力学和可变物体几何形状。在这项研究中，我们提出了一种基于Transformer的机器人抓取框架，用于利用触觉和视觉信息进行安全物体抓取的刚性抓取器。具体而言，Transformer模型通过执行两个预定义的探索动作（挤压和滑动）学习传感器反馈的物理特征嵌入，并通过具有给定抓取强度的多层感知器（MLP）预测最终抓取结果。使用这些预测，通过推理，以抓取任务的安全抓取强度指令抓取器。与卷积递归网络相比，变换器模型能够捕获图像序列的长期相关性，同时处理图像序列的时空特征。我们首先在一个公共数据集上对提出的Transformer模型进行基准测试，以进行滑动检测。接下来，我们证明了Transformer模型在抓取精度和计算效率方面优于CNN+LSTM模型。我们还收集了自己的水果抓取数据集，并使用所提出的框架对可见和不可见的水果进行在线抓取实验。我们的代码和数据集在GitHub上公开。 摘要：Reliable robotic grasping, especially with deformable objects, (e.g. fruit), remains a challenging task due to underactuated contact interactions with a gripper, unknown object dynamics, and variable object geometries. In this study, we propose a Transformer-based robotic grasping framework for rigid grippers that leverage tactile and visual information for safe object grasping. Specifically, the Transformer models learn physical feature embeddings with sensor feedback through performing two pre-defined explorative actions (pinching and sliding) and predict a final grasping outcome through a multilayer perceptron (MLP) with a given grasping strength. Using these predictions, the gripper is commanded with a safe grasping strength for the grasping tasks via inference. Compared with convolutional recurrent networks, the Transformer models can capture the long-term dependencies across the image sequences and process the spatial-temporal features simultaneously. We first benchmark the proposed Transformer models on a public dataset for slip detection. Following that, we show that the Transformer models outperform a CNN+LSTM model in terms of grasping accuracy and computational efficiency. We also collect our own fruit grasping dataset and conduct the online grasping experiments using the proposed framework for both seen and unseen fruits. Our codes and dataset are made public on GitHub.

【16】 Image Reconstruction from Events. Why learn it? 标题：从事件中重建图像。为什么要学呢？ 链接：https://arxiv.org/abs/2112.06242

作者：Zelin Zhang,Anthony Yezzi,Guillermo Gallego 机构：Georgia Tech, TU Berlin, ECDF, SCIoI 备注：18 pages, 13 figures, 5 tables 摘要：传统相机测量图像强度。相反，事件摄影机异步测量每像素的时间强度变化。从事件中恢复强度是一个热门的研究课题，因为重建图像继承了事件的高动态范围（HDR）和高速特性；因此，它们可以用于许多机器人视觉应用，并生成慢动作HDR视频。然而，最先进的方法通过训练一个事件到图像的递归神经网络（RNN）来解决这个问题，RNN缺乏可解释性，难以调整。在这项工作中，我们首次展示了如何解决运动和强度估计的联合问题，从而将基于事件的图像重建建模为一个线性逆问题，该问题可以在不训练图像重建RNN的情况下解决。取而代之的是，可以使用经典的和基于学习的图像先验来解决问题，并从重建图像中去除伪影。实验表明，所提出的方法生成视觉质量与PAR与现有技术的最先进的方法，尽管只使用数据从一个短的时间间隔（即，没有经常性的连接）。我们的方法也可以用于改善通过首先估计图像拉普拉斯函数的方法重建的图像的质量；在这里，我们的方法可以解释为由图像先验引导的泊松重建。 摘要：Traditional cameras measure image intensity. Event cameras, by contrast, measure per-pixel temporal intensity changes asynchronously. Recovering intensity from events is a popular research topic since the reconstructed images inherit the high dynamic range (HDR) and high-speed properties of events; hence they can be used in many robotic vision applications and to generate slow-motion HDR videos. However, state-of-the-art methods tackle this problem by training an event-to-image recurrent neural network (RNN), which lacks explainability and is difficult to tune. In this work we show, for the first time, how tackling the joint problem of motion and intensity estimation leads us to model event-based image reconstruction as a linear inverse problem that can be solved without training an image reconstruction RNN. Instead, classical and learning-based image priors can be used to solve the problem and remove artifacts from the reconstructed images. The experiments show that the proposed approach generates images with visual quality on par with state-of-the-art methods despite only using data from a short time interval (i.e., without recurrent connections). Our method can also be used to improve the quality of images reconstructed by approaches that first estimate the image Laplacian; here our method can be interpreted as Poisson reconstruction guided by image priors.

【17】 Multi-Agent Vulnerability Discovery for Autonomous Driving with Hazard Arbitration Reward 标题：基于危险仲裁奖励的自动驾驶多Agent漏洞发现 链接：https://arxiv.org/abs/2112.06185

作者：Weilin Liu,Ye Mu,Chao Yu,Xuefei Ning,Zhong Cao,Yi Wu,Shuang Liang,Huazhong Yang,Yu Wang 机构： Cao is with the School of Vehicle and Mobility 摘要：发现危险场景对于测试和进一步改进驾驶政策至关重要。然而，进行有效的驾驶政策测试面临两个关键挑战。一方面，在测试训练有素的自动驾驶策略时，自然遇到危险场景的概率较低。因此，通过纯粹的真实道路测试发现这些场景的成本极高。另一方面，这项任务需要正确确定事故责任。收集错误归因责任的场景将导致过度保守的自主驾驶策略。更具体地说，我们的目标是发现与自动驾驶车辆（AV）相关的危险场景，即测试驾驶政策的漏洞。为此，本工作提出了一个基于多智能体强化学习的安全测试框架，通过寻找Av责任场景（STAR）。STARS通过引入危险仲裁奖励（HAR），引导其他交通参与者产生Av责任场景，并使测试中的驾驶政策行为不当。HAR使我们的框架能够发现多样化、复杂和与AV相关的危险场景。在三种环境中针对四种不同驾驶策略的实验结果表明，STARS能够有效地发现与AV相关的危险场景。这些场景确实对应于测试中驾驶政策的漏洞，因此对其进一步改进具有重要意义。 摘要：Discovering hazardous scenarios is crucial in testing and further improving driving policies. However, conducting efficient driving policy testing faces two key challenges. On the one hand, the probability of naturally encountering hazardous scenarios is low when testing a well-trained autonomous driving strategy. Thus, discovering these scenarios by purely real-world road testing is extremely costly. On the other hand, a proper determination of accident responsibility is necessary for this task. Collecting scenarios with wrong-attributed responsibilities will lead to an overly conservative autonomous driving strategy. To be more specific, we aim to discover hazardous scenarios that are autonomous-vehicle responsible (AV-responsible), i.e., the vulnerabilities of the under-test driving policy. To this end, this work proposes a Safety Test framework by finding Av-Responsible Scenarios (STARS) based on multi-agent reinforcement learning. STARS guides other traffic participants to produce Av-Responsible Scenarios and make the under-test driving policy misbehave via introducing Hazard Arbitration Reward (HAR). HAR enables our framework to discover diverse, complex, and AV-responsible hazardous scenarios. Experimental results against four different driving policies in three environments demonstrate that STARS can effectively discover AV-responsible hazardous scenarios. These scenarios indeed correspond to the vulnerabilities of the under-test driving policies, thus are meaningful for their further improvements.

【18】 3D LiDAR Aided GNSS NLOS Mitigation in Urban Canyons 标题：三维激光雷达辅助城市峡谷GNSS非视距缓解 链接：https://arxiv.org/abs/2112.06108

作者：Weisong Wen,Li-Ta Hsu 机构：and understanding surrounding environments is the key to, improving GNSS positioning in urban areas, as GNSS, positioning relies heavily on sky view visibility. The most, well-known method to cope with the GNSS NLOS receptions is 备注：13 pages, 11 figures 摘要：在本文中，我们提出了一种三维激光雷达辅助全球导航卫星系统（GNSS）的非视距（NLOS）缓解方法，该方法可同时缓解静态建筑物和动态物体造成的非视距。基于3D激光雷达传感器的实时3D点云，首先生成描述ego车辆周围环境的滑动窗口地图。然后，使用所提出的快速搜索方法，基于滑动窗口映射检测NLOS接收，该方法不需要对GNSS接收机的位置进行初始猜测。本文不是直接将检测到的NLOS卫星排除在进一步的定位估计之外，而是通过（1）如果在滑动窗口地图内检测到NLOS信号的反射点，则校正伪距测量模型，（2）采用一种新的加权方案重构非视距伪距测量的不确定度。我们评估了所提出的方法在香港的几个典型的城市峡谷使用汽车级GNSS接收器的性能。此外，我们还通过因子图优化评估了所提出的NLOS缓解方法在GNSS和惯性导航系统集成中的潜力。 摘要：In this paper, we propose a 3D LiDAR aided global navigation satellite system (GNSS) non-line-of-sight (NLOS) mitigation method caused by both static buildings and dynamic objects. A sliding window map describing the surrounding of the ego-vehicle is first generated, based on real-time 3D point clouds from a 3D LiDAR sensor. Then, NLOS receptions are detected based on the sliding window map using a proposed fast searching method which is free of the initial guess of the position of the GNSS receiver. Instead of directly excluding the detected NLOS satellites from further positioning estimation, this paper rectifies the pseudorange measurement model by (1) correcting the pseudorange measurements if the reflecting point of NLOS signals is detected inside the sliding window map, and (2) remodeling the uncertainty of the NLOS pseudorange measurement using a novel weighting scheme. We evaluated the performance of the proposed method in several typical urban canyons in Hong Kong using an automobile-level GNSS receiver. Moreover, we also evaluate the potential of the proposed NLOS mitigation method in GNSS and inertial navigation systems integration via factor graph optimization.

【19】 OstrichRL: A Musculoskeletal Ostrich Simulation to Study Bio-mechanical Locomotion 标题：OstrichRL：研究生物机械运动的肌肉骨骼鸵鸟模拟 链接：https://arxiv.org/abs/2112.06061

作者：Vittorio La Barbera,Fabio Pardo,Yuval Tassa,Monica Daley,Christopher Richards,Petar Kormushev,John Hutchinson 机构：Monica A. Daley, Christopher T. Richards, John R. Hutchinson, Royal Veterinary College, London,Imperial College London, DeepMind, London,University of California, Irvine 备注：this https URL 摘要：肌肉驱动控制是一个跨不同领域的研究课题，特别是生物力学、机器人学和图形学。这种类型的控制尤其具有挑战性，因为模型经常被过度驱动，并且动力学是延迟的和非线性的。然而，它是一个经过了良好测试和调整的驱动模型，经历了数百万年的进化，涉及利用肌腱单位的被动力和高效能量储存和释放的有趣特性。为了促进肌肉驱动模拟的研究，我们发布了基于MuJoCo模拟器的鸵鸟三维肌肉骨骼模拟。鸵鸟是地球上速度最快的两足动物之一，因此是研究肌肉驱动的两足动物运动的极好模型。该模型基于CT扫描和解剖，用于收集实际肌肉数据，如插入位置、长度和角度。除了这个模型，我们还提供了一组强化学习任务，包括参考运动跟踪和颈部到达任务。参考运动数据基于各种行为的运动捕捉剪辑，我们对这些剪辑进行了预处理，并根据我们的模型进行了调整。本文描述了如何使用任务构建和迭代改进模型。我们通过将肌肉驱动模式与实验收集的来自运动鸟类的肌电图数据进行比较，来评估肌肉驱动模式的准确性。我们相信，通过提供快速且易于使用的模拟，这项工作可以成为生物力学、强化学习、图形学和机器人学社区之间的有用桥梁。 摘要：Muscle-actuated control is a research topic of interest spanning different fields, in particular biomechanics, robotics and graphics. This type of control is particularly challenging because models are often overactuated, and dynamics are delayed and non-linear. It is however a very well tested and tuned actuation model that has undergone millions of years of evolution and that involves interesting properties exploiting passive forces of muscle-tendon units and efficient energy storage and release. To facilitate research on muscle-actuated simulation, we release a 3D musculoskeletal simulation of an ostrich based on the MuJoCo simulator. Ostriches are one of the fastest bipeds on earth and are therefore an excellent model for studying muscle-actuated bipedal locomotion. The model is based on CT scans and dissections used to gather actual muscle data such as insertion sites, lengths and pennation angles. Along with this model, we also provide a set of reinforcement learning tasks, including reference motion tracking and a reaching task with the neck. The reference motion data are based on motion capture clips of various behaviors which we pre-processed and adapted to our model. This paper describes how the model was built and iteratively improved using the tasks. We evaluate the accuracy of the muscle actuation patterns by comparing them to experimentally collected electromyographic data from locomoting birds. We believe that this work can be a useful bridge between the biomechanics, reinforcement learning, graphics and robotics communities, by providing a fast and easy to use simulation.

【20】 Learn from Human Teams: a Probabilistic Solution to Real-Time Collaborative Robot Handling with Dynamic Gesture Commands 标题：向人类团队学习：实时协作机器人处理动态手势命令的概率解决方案 链接：https://arxiv.org/abs/2112.06020

作者：Rui Chen,Alvin Shek,Changliu Liu 机构： 1CarnegieMellonUniversity 备注：Submitted to IEEE Transactions on Robotics 摘要：我们研究了实时协作机器人（cobot）操作，其中cobot在人类命令下操纵工件。当人类直接处理工件有风险时，这是有用的。然而，很难使cobot在可能的操作中既易于指挥又灵活。在这项工作中，我们提出了一个实时协作机器人处理（RTCoHand）框架，允许通过用户自定义的动态手势控制cobot。由于用户之间的差异、人体运动的不确定性和人为输入的噪声，这是很难做到的。我们将任务建模为一个概率生成过程，称为条件协同处理过程（CCHP），并从人类协作中学习。我们全面评估了CCHP的适应性和鲁棒性，并将我们的方法应用于Kinova Gen3机器人手臂的实时cobot处理任务。我们与经验丰富的用户和新用户实现无缝人机协作。与经典控制器相比，RTCoHand允许更复杂的操作和更低的用户认知负担。它还消除了反复试验的需要，使其在安全关键任务中具有优势。 摘要：We study real-time collaborative robot (cobot) handling, where the cobot maneuvers a workpiece under human commands. This is useful when it is risky for humans to directly handle the workpiece. However, it is hard to make the cobot both easy to command and flexible in possible operations. In this work, we propose a Real-Time Collaborative Robot Handling (RTCoHand) framework that allows the control of cobot via user-customized dynamic gestures. This is hard due to variations among users, human motion uncertainties, and noisy human input. We model the task as a probabilistic generative process, referred to as Conditional Collaborative Handling Process (CCHP), and learn from human-human collaboration. We thoroughly evaluate the adaptability and robustness of CCHP and apply our approach to a real-time cobot handling task with Kinova Gen3 robot arm. We achieve seamless human-robot collaboration with both experienced and new users. Compared to classical controllers, RTCoHand allows significantly more complex maneuvers and lower user cognitive burden. It also eliminates the need for trial-and-error, rendering it advantageous in safety-critical tasks.

【21】 Learning a Sequential Policy of Efficient Actions for Tangled-Prone Parts in Robotic Bin Picking 标题：机器人拣箱中易缠绕零件高效动作的序贯策略学习 链接：https://arxiv.org/abs/2112.05941

作者：Xinyi Zhang,Yukiyasu Domae,Weiwei Wan,Kensuke Harada 机构：jp 1Graduate School of Engineering Science, Osaka University, National Instituteof Advanced Industrial Science and Technology (AIST) 备注：8 pages 摘要：本文介绍了一种电缆线束自动拣箱系统，这是拣箱任务中一个极具挑战性的目标。目前，由于其长度和难以捉摸的结构，电缆束不适合进口用于自动化生产。考虑到吊带缠绕严重的情况下，机器人拾取箱子的任务，使用传统的箱子拾取方法逐个拾取吊带是一个挑战。在本文中，我们提出了一种有效的方法来克服困难时，处理纠缠容易部分。我们为机器人开发了几种运动方案，以拾取单个线束，避免任何纠缠。此外，我们提出了一种基于学习的仓位拾取策略来选择抓取，并以合理的顺序设计运动方案。我们的方法是独特的，因为它的新颖性足以解决拾取杂乱电缆线束时的纠缠问题。我们在一组真实世界的实验中演示了我们的方法，在此过程中，所提出的方法能够在各种杂乱场景下有效且准确地执行顺序拣箱任务。 摘要：This paper introduces an autonomous bin picking system for cable harnesses - an extremely challenging object in bin picking task. Currently cable harnesses are unsuitable to be imported to automated production due to their length and elusive structures. Considering the task of robotic bin picking where the harnesses are heavily entangled, it is challenging for a robot to pick harnesses up one by one using conventional bin picking methods. In this paper, we present an efficient approach to overcoming the difficulties when dealing with entangled-prone parts. We develop several motion schemes for the robot to pick up a single harness avoiding any entanglement. Moreover, we proposed a learning-based bin picking policy to select both grasps and designed motion schemes in a reasonable sequence. Our method is unique due to the novelty for sufficiently solving the entanglement problem in picking cluttered cable harnesses. We demonstrate our approach on a set of real-world experiments, during which the proposed method is capable to perform the sequential bin picking task with both effectiveness and accuracy under a variety of cluttered scenarios.

【22】 Personalized Highway Pilot Assist Considering Leading Vehicle's Lateral Behaviours 标题：考虑主导车辆横向行为的个性化公路驾驶员辅助 链接：https://arxiv.org/abs/2112.05913

作者：Daofei Li,Ao Liu 机构： With many improvements by the 1 Institute of Power Machinery and Vehicular Engineering, Zhejiang University 备注：18 pages, 12 figures, 3 tables 摘要：公路驾驶员辅助系统已成为先进驾驶员辅助系统的竞争前沿。对安全性和用户接受度的要求越来越高，要求在此类系统的开发过程中实现个性化。受驾驶员横向跟车偏好的启发，提出了一种个性化的高速公路驾驶员辅助算法，该算法由基于智能驾驶员模型（IDM）的速度控制模型和考虑领先车辆横向运动的新型车道保持模型组成。通过模拟驾驶实验，分析了自由驾驶和跟驰驾驶情况下驾驶员的注视和车道保持行为。根据受领先车辆影响的驾驶行为，将驾驶员分为两个驾驶风格组，然后优化每个特定主题驾驶员的个性化参数。基于移动基座模拟器的驱动在环实验验证了该算法的有效性。结果表明，与非个性化算法相比，个性化高速公路引导算法可以显著减少用户的心理负担，提高用户对辅助功能的接受度。 摘要：Highway pilot assist has become the front line of competition in advanced driver assistance systems. The increasing requirements on safety and user acceptance are calling for personalization in the development process of such systems. Inspired by a finding on drivers' car-following preferences on lateral direction, a personalized highway pilot assist algorithm is proposed, which consists of an Intelligent Driver Model (IDM) based speed control model and a novel lane-keeping model considering the leading vehicle's lateral movement. A simulated driving experiment is conducted to analyse driver gaze and lane-keeping Behaviours in free-driving and following driving scenario. Drivers are clustered into two driving style groups referring to their driving Behaviours affected by the leading vehicle, and then the personalization parameters for every specific subject driver are optimized. The proposed algorithm is validated through driver-in-the-loop experiment based on a moving-base simulator. Results show that, compared with the un-personalized algorithms, the personalized highway pilot algorithm can significantly reduce the mental workload and improve user acceptance of the assist functions.

【23】 Online Information-Aware Motion Planning with Inertial Parameter Learning for Robotic Free-Flyers 标题：基于惯性参数学习的机器人自由飞在线信息感知运动规划 链接：https://arxiv.org/abs/2112.05878

作者：Monica Ekal,Keenan Albee,Brian Coltin,Rodrigo Ventura,Richard Linares,David W. Miller 机构： Massachusetts Institute ofTechnology 备注：8 pages, 8 figures, IROS 2021 preprint (accepted) 摘要：像目前在国际空间站上运行的Astrobee机器人这样的无空间飞行器必须在固有的系统不确定性下运行。质量和惯性矩等参数不确定性对于这些安全关键空间系统的量化尤为重要，并且在轨道货物移动等场景中可能会发生变化，其中未知抓斗有效载荷会显著改变系统动力学。在途中谨慎地学习这些不确定性有可能避免耗时耗油的纯系统辨识操作。认识到这一点，这项工作提出了Crattle，一种在线信息感知运动规划算法，该算法明确加权参数模型学习，并结合实时重新规划能力，可利用改进的系统模型。该方法包括一个两层（全局和局部）规划器、一个低级模型预测控制器和一个在线参数估计器，该估计器产生机器人惯性特性的估计值，以便在飞行中进行更明智的控制和重新规划；所有级别的计划和控制功能都可以在线更新模型。除了硬件演示的结果外，还展示了Astrobee free flyer抓斗不确定有效载荷时的嘎嘎声仿真结果，展示了明确鼓励模型参数学习，同时实现其他有用运动的能力。 摘要：Space free-flyers like the Astrobee robots currently operating aboard the International Space Station must operate with inherent system uncertainties. Parametric uncertainties like mass and moment of inertia are especially important to quantify in these safety-critical space systems and can change in scenarios such as on-orbit cargo movement, where unknown grappled payloads significantly change the system dynamics. Cautiously learning these uncertainties en route can potentially avoid time- and fuel-consuming pure system identification maneuvers. Recognizing this, this work proposes RATTLE, an online information-aware motion planning algorithm that explicitly weights parametric model-learning coupled with real-time replanning capability that can take advantage of improved system models. The method consists of a two-tiered (global and local) planner, a low-level model predictive controller, and an online parameter estimator that produces estimates of the robot's inertial properties for more informed control and replanning on-the-fly; all levels of the planning and control feature online update-able models. Simulation results of RATTLE for the Astrobee free-flyer grappling an uncertain payload are presented alongside results of a hardware demonstration showcasing the ability to explicitly encourage model parametric learning while achieving otherwise useful motion.

【24】 A Novel Gaussian Process Based Ground Segmentation Algorithm with Local-Smoothness Estimation 标题：一种新的基于高斯过程的局部光滑度估计地面分割算法 链接：https://arxiv.org/abs/2112.05847

作者：Pouria Mehrabi,Hamid D. Taghirad 机构： Toosi University of Technology 备注：arXiv admin note: substantial text overlap with arXiv:2111.10638 摘要：自动陆地车辆（ALV）应能在未知环境中有效识别地面。提出了一种基于$\mathcal{GP}$的粗糙驾驶场景下的地面分割方法。非平稳协方差函数用作$\mathcal{GP}$的核。假定地面行为仅显示局部平滑度。这样，就得到了核长度尺度的点估计。因此，引入了两个高斯过程来分别模拟数据的观测和局部特征。当使用\textit{observation process}对地面建模时，将\textit{潜伏过程}放在长度刻度值上，以估计每个输入位置的长度刻度点值。这一潜在过程的输入位置是在物理激励程序中选择的，以表示对地面条件的直觉。此外，通过假设环境中存在假设曲面，可以表示长度刻度值的直观猜测，假设每一组数据点都是由该曲面的测量结果产生的。贝叶斯推理是使用\text{maximum a Posteriori}标准实现的。假定对数边际似然函数是一个多任务目标函数，以表示每一帧地面的整个帧无偏视图。仿真结果表明，即使在不均匀、粗糙的场景中，该方法的效果也优于基于相似高斯过程的地面分割方法。在不均匀场景中，相邻线段没有相似的地面结构，该方法基于全帧视点进行有效的地面估计，而不是仅估计分段可能的地面。 摘要：Autonomous Land Vehicles (ALV) shall efficiently recognize the ground in unknown environments. A novel $\mathcal{GP}$-based method is proposed for the ground segmentation task in rough driving scenarios. A non-stationary covariance function is utilized as the kernel for the $\mathcal{GP}$. The ground surface behavior is assumed to only demonstrate local-smoothness. Thus, point estimates of the kernel's length-scales are obtained. Thus, two Gaussian processes are introduced to separately model the observation and local characteristics of the data. While, the \textit{observation process} is used to model the ground, the \textit{latent process} is put on length-scale values to estimate point values of length-scales at each input location. Input locations for this latent process are chosen in a physically-motivated procedure to represent an intuition about ground condition. Furthermore, an intuitive guess of length-scale value is represented by assuming the existence of hypothetical surfaces in the environment that every bunch of data points may be assumed to be resulted from measurements from this surfaces. Bayesian inference is implemented using \textit{maximum a Posteriori} criterion. The log-marginal likelihood function is assumed to be a multi-task objective function, to represent a whole-frame unbiased view of the ground at each frame. Simulation results shows the effectiveness of the proposed method even in an uneven, rough scene which outperforms similar Gaussian process based ground segmentation methods. While adjacent segments do not have similar ground structure in an uneven scene, the proposed method gives an efficient ground estimation based on a whole-frame viewpoint instead of just estimating segment-wise probable ground surfaces.

【25】 Semantic Interaction in Augmented Reality Environments for Microsoft HoloLens 标题：Microsoft HoloLens增强现实环境中的语义交互 链接：https://arxiv.org/abs/2112.05846

作者：Peer Schüett,Max Schwarz,Sven Behnke 机构： University of Bonn 备注：None 摘要：增强现实是一种很有前途的人机交互技术。尤其是在机器人技术中，它总是在其环境中考虑系统，在该环境中直接显示可视化和接收用户输入是非常有益的。我们使用MicrosoftHoloLens探索这个想法，通过它我们可以捕捉室内环境并显示与已知对象类的交互提示。HoloLens记录的3D网格在用户移动时在线注释，语义类使用投影方法，这使我们能够使用最先进的2D语义分割方法。将结果融合到网格上；突出的对象段被识别并以3D形式显示给用户。最后，用户可以通过向对象做手势来触发动作。我们给出了定性结果，并在室内数据集上详细分析了我们方法的准确性和性能。 摘要：Augmented Reality is a promising technique for human-machine interaction. Especially in robotics, which always considers systems in their environment, it is highly beneficial to display visualizations and receive user input directly in exactly that environment. We explore this idea using the Microsoft HoloLens, with which we capture indoor environments and display interaction cues with known object classes. The 3D mesh recorded by the HoloLens is annotated on-line, as the user moves, with semantic classes using a projective approach, which allows us to use a state-of-the-art 2D semantic segmentation method. The results are fused onto the mesh; prominent object segments are identified and displayed in 3D to the user. Finally, the user can trigger actions by gesturing at the object. We both present qualitative results and analyze the accuracy and performance of our method in detail on an indoor dataset.