机器人相关学术速递[8.31]

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cs.RO机器人相关，共计18篇

【1】 RoboRun: A Robot Runtime to Exploit Spatial Heterogeneity 标题：RoboRun：一种利用空间异构性的机器人运行时 链接：https://arxiv.org/abs/2108.13354

作者：Behzad Boroujerdian,Radhika Ghosal,Jonathan Cruz,Brian Plancher,Vijay Janapa Reddi 机构：†The University of Texas at Austin, ‡Harvard University 备注：will be published in Design Automation Conference (DAC) 2021 摘要：自主移动机器人有限的车载能量对实际部署提出了巨大挑战。因此，高效的计算解决方案势在必行。最先进的计算解决方案的一个关键缺点是，它们忽略了机器人的操作环境异构性，并做出静态的最坏情况假设。由于这种异构性会影响系统的计算负载，因此最优系统必须动态捕获环境中的这些变化，并相应地调整其计算资源。本文介绍了RoboRun，一种移动机器人运行时，它动态地利用计算环境的协同作用来提高性能和能量。我们在机器人操作系统（ROS）中实现了RoboRun，并在自主无人机上对其进行了评估。我们将RoboRun与最先进的静态设计进行了比较，结果显示任务时间和能量分别提高了4.5倍和4倍，CPU利用率降低了36%。 摘要：The limited onboard energy of autonomous mobile robots poses a tremendous challenge for practical deployment. Hence, efficient computing solutions are imperative. A crucial shortcoming of state-of-the-art computing solutions is that they ignore the robot's operating environment heterogeneity and make static, worst-case assumptions. As this heterogeneity impacts the system's computing payload, an optimal system must dynamically capture these changes in the environment and adjust its computational resources accordingly. This paper introduces RoboRun, a mobile-robot runtime that dynamically exploits the compute-environment synergy to improve performance and energy. We implement RoboRun in the Robot Operating System (ROS) and evaluate it on autonomous drones. We compare RoboRun against a state-of-the-art static design and show 4.5X and 4X improvements in mission time and energy, respectively, as well as a 36% reduction in CPU utilization.

【2】 The missing link: Developing a safety case for perception components in automated driving 标题：缺失的环节：为自动驾驶中的感知部件开发安全案例 链接：https://arxiv.org/abs/2108.13294

作者：Rick Salay,Krzysztof Czarnecki,Hiroshi Kuwajima,Hirotoshi Yasuoka,Toshihiro Nakae,Vahdat Abdelzad,Chengjie Huang,Maximilian Kahn,Van Duong Nguyen 机构：University of Waterloo, Waterloo, Canada, DENSO CORPORATION, Tokyo, Japan 摘要：安全保证是自动驾驶（AD）系统开发和社会接受的核心问题。感知是AD的一个关键方面，它严重依赖于机器学习（ML）。尽管基于ML的组件的安全保证存在已知的挑战，但最近出现了针对这些组件的单元级安全案例的建议。不幸的是，AD安全案例表达了系统级的安全要求，而这些工作缺少将系统级的安全要求与单元级的组件性能要求联系起来的关键论点。在本文中，我们提出了一个通用的模板，专门为感知组件定制的链接参数。该模板采用演绎和形式化方法来定义级别之间的强可追溯性。我们通过详细的案例研究证明了模板的适用性，并讨论了其作为支持perception组件增量开发的工具的使用。 摘要：Safety assurance is a central concern for the development and societal acceptance of automated driving (AD) systems. Perception is a key aspect of AD that relies heavily on Machine Learning (ML). Despite the known challenges with the safety assurance of ML-based components, proposals have recently emerged for unit-level safety cases addressing these components. Unfortunately, AD safety cases express safety requirements at the system-level and these efforts are missing the critical linking argument connecting safety requirements at the system-level to component performance requirements at the unit-level. In this paper, we propose a generic template for such a linking argument specifically tailored for perception components. The template takes a deductive and formal approach to define strong traceability between levels. We demonstrate the applicability of the template with a detailed case study and discuss its use as a tool to support incremental development of perception components.

【3】 Hierarchical Reinforcement Learning for Sensor-Based Navigation 标题：分层强化学习在传感器导航中的应用 链接：https://arxiv.org/abs/2108.13268

作者：Christopher Gebauer,Maren Bennewitz 机构： University of Bonn 备注：Submitted to ICRA22 摘要：如今，机器人系统能够在现实条件下解决复杂的导航任务。然而，它们的能力本质上仅限于设计师的想象，因此对于最初未经考虑的情况缺乏普遍性。这使得深度强化学习特别有趣，因为这些算法保证了一个只依赖环境反馈的自学习系统。让系统本身寻找最佳解决方案，在解决终身学习问题时，会带来极大的通用性甚至持续改进的好处。在本文中，我们使用深度分层强化学习来解决连续动作空间中的机器人导航问题，而不在状态表示中包含目标位置。我们的代理自行分配内部目标，并学习仅基于本地传感器数据提取合理的航路点以达到所需的目标位置。在我们的实验中，我们证明，与平面结构相比，我们的分层结构在收集奖励和成功率方面提高了导航代理的性能，同时不需要任何全局或目标信息。 摘要：Robotic systems are nowadays capable of solving complex navigation tasks under real-world conditions. However, their capabilities are intrinsically limited to the imagination of the designer and consequently lack generalizability to initially unconsidered situations. This makes deep reinforcement learning especially interesting, as these algorithms promise a self-learning system only relying on feedback from the environment. Having the system itself search for an optimal solution brings the benefit of great generalization or even constant improvement when life-long learning is addressed. In this paper, we address robot navigation in continuous action space using deep hierarchical reinforcement learning without including the target location in the state representation. Our agent self-assigns internal goals and learns to extract reasonable waypoints to reach the desired target position only based on local sensor data. In our experiments we demonstrate that our hierarchical structure improves the performance of the navigation agent in terms of collected reward and success rate in comparison to a flat structure, while not requiring any global or target information.

【4】 Model Predictive Contouring Control for Near-Time-Optimal Quadrotor Flight 标题：近时间最优四旋翼飞行的模型预测轮廓控制 链接：https://arxiv.org/abs/2108.13205

作者：Angel Romero,Sihao Sun,Philipp Foehn,Davide Scaramuzza 机构： University ofZurich 备注：16 pages, 16 figures 摘要：研究了四旋翼多航路点飞行时间最优轨迹问题。最先进的解决方案将问题分解为规划任务（生成全局时间最优轨迹）和控制任务（精确跟踪该轨迹）。然而，在当前状态下，生成考虑全四旋翼模型的时间最优轨迹在计算上要求很高（以分钟甚至小时为单位）。这对干扰情况下的重新规划是有害的。我们通过模型预测轮廓控制（MPCC）同时解决时间最优规划和控制问题来解决这个问题。我们的MPCC在运行时以最佳方式选择平台的未来状态，同时使参考路径上的进度最大化，并使到它的距离最小化。我们表明，即使在跟踪简化的轨迹时，所提出的MPCC也会产生接近实时最优的路径，并且可以实时生成。我们在现实世界中验证了我们的方法，在圈速达到60 km/h方面，我们的方法优于当前最先进的和世界一流的人工驾驶。 摘要：We tackle the problem of flying time-optimal trajectories through multiple waypoints with quadrotors. State-of-the-art solutions split the problem into a planning task - where a global, time-optimal trajectory is generated - and a control task - where this trajectory is accurately tracked. However, at the current state, generating a time-optimal trajectory that takes the full quadrotor model into account is computationally demanding (in the order of minutes or even hours). This is detrimental for replanning in presence of disturbances. We overcome this issue by solving the time-optimal planning and control problems concurrently via Model Predictive Contouring Control (MPCC). Our MPCC optimally selects the future states of the platform at runtime, while maximizing the progress along the reference path and minimizing the distance to it. We show that, even when tracking simplified trajectories, the proposed MPCC results in a path that approaches the true time-optimal one, and which can be generated in real-time. We validate our approach in the real-world, where we show that our method outperforms both the current state-of-the-art and a world-class human pilot in terms of lap time achieving speeds of up to 60 km/h.

【5】 COMPRA: A COMPact Reactive Autonomy framework for subterranean MAV based search-and-rescue operations 标题：COPRA：一种紧凑的基于MAV的地下搜救行动反应性自主框架 链接：https://arxiv.org/abs/2108.13105

作者：Björn Lindqvist,Christoforos Kanellakis,Sina Sharif Mansouri,Ali-akbar Agha-mohammadi,George Nikolakopoulos 机构：Received: ,-,-, Accepted: date 备注：19 pages, 10 figures 摘要：这项工作建立了COMPA，一个紧凑的反应式自治框架，用于在地下搜索和救援任务中快速部署微型飞行器。启用COMPA的微型飞行器能够自主探索以前未知的区域，同时考虑特定的任务标准，例如识别和定位感兴趣的目标、剩余的有效电池寿命、总体预期探索任务持续时间。所提出的体系结构遵循低复杂度算法设计，以促进全面的车载计算，包括非线性控制、状态估计、导航、探测行为和目标定位能力。该框架主要围绕一个反应式局部避让规划器构建，该规划器基于增强的势场概念并使用瞬时3D点云，以及基于瞬时摄像机流轮廓检测的计算高效的航向调节技术。这些技术将无碰撞路径生成与全局地图的依赖性解耦，并且能够处理不精确的定位情况。在相关未知GPS环境中，对总体架构进行了现场实验验证。 摘要：This work establishes COMPRA, a compact and reactive autonomy framework for fast deployment of MAVs in subterranean Search-and-Rescue missions. A COMPRA-enabled MAV is able to autonomously explore previously unknown areas while specific mission criteria are considered e.g. an object of interest is identified and localized, the remaining useful battery life, the overall desired exploration mission duration. The proposed architecture follows a low-complexity algorithmic design to facilitate fully on-board computations, including nonlinear control, state-estimation, navigation, exploration behavior and object localization capabilities. The framework is mainly structured around a reactive local avoidance planner, based on enhanced Potential Field concepts and using instantaneous 3D pointclouds, as well as a computationally efficient heading regulation technique, based on contour detection on an instantaneous camera stream. Those techniques decouple the collision-free path generation from the dependency of a global map and are capable of handling imprecise localization occasions. Field experimental verification of the overall architecture is performed in relevant unknown GPS-denied environments.

【6】 Unsupervised Monocular Depth Perception: Focusing on Moving Objects 标题：无监督单目深度感知：聚焦于运动物体 链接：https://arxiv.org/abs/2108.13062

作者：Hualie Jiang,Laiyan Ding,Zhenglong Sun,Rui Huang 机构： which is also usually obtainedThe authors are with School of Science and Engineering, The ChineseUniversity of Hong Kong, and also with Shenzhen Institute ofArtificial Intelligence and Robotics for Society 备注：Accepted by IEEE Sensors Journal. arXiv admin note: text overlap with arXiv:2003.01360 摘要：作为一种灵活的被动三维传感手段，单目视频深度的无监督学习正成为一个重要的研究课题。它利用目标视图与其相邻源视图的合成视图之间的光度误差作为损失，而不是与地面真实值的差异。尽管最近取得了重大进展，但真实场景中的遮挡和场景动力学仍然会对学习产生不利影响。在本文中，我们表明，故意操纵光度误差可以有效地更好地处理这些困难。我们首先提出了一种孤立点掩蔽技术，该技术将遮挡或动态像素视为光度误差图中的统计孤立点。使用离群点掩蔽，网络可以更准确地学习与摄影机相反方向移动的对象的深度。据我们所知，在以前的工作中没有认真考虑过此类情况，即使它们在自动驾驶等应用中存在高风险。我们还提出了一种有效的加权多尺度方案来减少预测深度图中的伪影。在KITTI数据集上的大量实验和在Cityscapes数据集上的附加实验验证了所提出的方法在深度或自我运动估计上的有效性。此外，我们首次分别对有监督和无监督方法在动态目标和静态背景区域的预测深度进行评估。评估进一步验证了我们提出的技术方法的有效性，并提供了一些有趣的观察结果，这些观察结果可能会激励未来在这方面的研究。 摘要：As a flexible passive 3D sensing means, unsupervised learning of depth from monocular videos is becoming an important research topic. It utilizes the photometric errors between the target view and the synthesized views from its adjacent source views as the loss instead of the difference from the ground truth. Occlusion and scene dynamics in real-world scenes still adversely affect the learning, despite significant progress made recently. In this paper, we show that deliberately manipulating photometric errors can efficiently deal with these difficulties better. We first propose an outlier masking technique that considers the occluded or dynamic pixels as statistical outliers in the photometric error map. With the outlier masking, the network learns the depth of objects that move in the opposite direction to the camera more accurately. To the best of our knowledge, such cases have not been seriously considered in the previous works, even though they pose a high risk in applications like autonomous driving. We also propose an efficient weighted multi-scale scheme to reduce the artifacts in the predicted depth maps. Extensive experiments on the KITTI dataset and additional experiments on the Cityscapes dataset have verified the proposed approach's effectiveness on depth or ego-motion estimation. Furthermore, for the first time, we evaluate the predicted depth on the regions of dynamic objects and static background separately for both supervised and unsupervised methods. The evaluation further verifies the effectiveness of our proposed technical approach and provides some interesting observations that might inspire future research in this direction.

【7】 SurRoL: An Open-source Reinforcement Learning Centered and dVRK Compatible Platform for Surgical Robot Learning 标题：SurRoL：一个开源的以强化学习为中心的兼容dVRK的手术机器人学习平台 链接：https://arxiv.org/abs/2108.13035

作者：Jiaqi Xu,Bin Li,Bo Lu,Yun-Hui Liu,Qi Dou,Pheng-Ann Heng 机构： Heng are also with the T Stone Robotics Institute, Liu are with the Department of Mechanicaland Automation Engineering 备注：8 pages, 8 figures, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 摘要：自主手术执行减轻了繁琐的例行程序和外科医生的疲劳。最近的基于学习的方法，特别是基于强化学习（RL）的方法，在灵巧操作方面取得了很好的性能，这通常需要通过仿真来有效地收集数据并降低硬件成本。现有的基于学习的医疗机器人仿真平台存在场景受限和物理交互简化的问题，这降低了学习策略的真实性能。在这项工作中，我们设计了SurRoL，一个与达芬奇研究工具包（dVRK）兼容的以RL为中心的外科机器人学习仿真平台。设计的SurRoL集成了用于算法开发的用户友好的RL库和实时物理引擎，能够支持更多PSM/ECM场景和更真实的物理交互。该平台构建了10个基于学习的手术任务，这些任务在真正的自主手术执行中是常见的。我们在仿真中使用RL算法对SurRoL进行了评估，提供了深入的分析，在真实的dVRK上部署了经过训练的策略，并表明我们的SurRoL在现实世界中实现了更好的可转移性。 摘要：Autonomous surgical execution relieves tedious routines and surgeon's fatigue. Recent learning-based methods, especially reinforcement learning (RL) based methods, achieve promising performance for dexterous manipulation, which usually requires the simulation to collect data efficiently and reduce the hardware cost. The existing learning-based simulation platforms for medical robots suffer from limited scenarios and simplified physical interactions, which degrades the real-world performance of learned policies. In this work, we designed SurRoL, an RL-centered simulation platform for surgical robot learning compatible with the da Vinci Research Kit (dVRK). The designed SurRoL integrates a user-friendly RL library for algorithm development and a real-time physics engine, which is able to support more PSM/ECM scenarios and more realistic physical interactions. Ten learning-based surgical tasks are built in the platform, which are common in the real autonomous surgical execution. We evaluate SurRoL using RL algorithms in simulation, provide in-depth analysis, deploy the trained policies on the real dVRK, and show that our SurRoL achieves better transferability in the real world.

【8】 Distributed Swarm Collision Avoidance Based on Angular Calculations 标题：基于角度计算的分布式群体避碰 链接：https://arxiv.org/abs/2108.12934

作者：SeyedZahir Qazavi,Samaneh Hosseini Semnani 机构： 1 SeyedZahir Qazavi is with the Department of Electrical and Computer Engineering, Isfahan University of Technology, ir) 2 Samaneh Hosseini Semnani is with the Department of Electrical and Computer Engineering 摘要：碰撞避免是机器人领域最重要的课题之一。目标是将机器人从初始位置移动到目标位置，以便它们在最短的时间内以最少的能量遵循最短的非碰撞路径。本文提出了一种适用于密集复杂二维和三维环境的分布式实时算法。该算法使用角度计算来选择每个机器人的最佳运动方向，并且已经证明，这些单独的计算会导致代理之间的合作行为。我们在各种仿真和实验场景下评估了所提出的方法，并将结果与该领域的两个重要算法FMP和ORCA进行了比较。结果表明，该方法比ORCA方法至少快25%，比FMP方法至少快7%，并且比两种方法都更可靠。所提出的方法被证明能够实现对一群疯狂飞行物的完全自主导航。 摘要：Collision avoidance is one of the most important topics in the robotics field. The goal is to move the robots from initial locations to target locations such that they follow shortest non-colliding paths in the shortest time and with the least amount of energy. In this paper, a distributed and real-time algorithm for dense and complex 2D and 3D environments is proposed. This algorithm uses angular calculations to select the optimal direction for the movement of each robot and it has been shown that these separate calculations lead to a form of cooperative behavior among agents. We evaluated the proposed approach on various simulation and experimental scenarios and compared the results with FMP and ORCA, two important algorithms in this field. The results show that the proposed approach is at least 25% faster than ORCA and at least 7% faster than FMP and also more reliable than both methods. The proposed method is shown to enable fully autonomous navigation of a swarm of crazyflies.

【9】 Flying Through a Narrow Gap Using End-to-end Deep Reinforcement Learning Augmented with Curriculum Learning and Sim2Real 标题：在课程学习和Sim2Real的基础上使用端到端深度强化学习跨越狭窄的鸿沟 链接：https://arxiv.org/abs/2108.12869

作者：Chenxi Xiao,Peng Lu,Qizhi He 机构： 1 Chenxi Xiao was with the Hong Kong Polytechnic University and iscurrently with Purdue University, edu 2 Peng Lu is currently with the Adaptive Robotic Controls Lab at theUniversity of Hong Kong and was previously with the Hong Kong PolytechnicUniversity 摘要：对于强化学习来说，穿过倾斜的窄间隙是一项棘手的任务，主要是因为两个挑战。首先，寻找可行的轨迹并非易事，因为差距背后的目标很难实现。其次，Sim2Real后的误差容限较低，因为与间隙的狭窄尺寸相比，速度相对较高。由于存在碰撞损坏的风险，难以收集真实世界的数据，这一问题更加严重。在本文中，我们提出了一个端到端的强化学习框架，通过解决这两个问题成功地解决了这一任务。为了寻找动态可行的飞行轨迹，我们使用课程学习来引导代理朝向障碍物后面的稀疏奖励。为了解决Sim2Real问题，我们提出了一个Sim2Real框架，该框架可以在不使用真实飞行数据的情况下将控制命令传输到真实的四旋翼。据我们所知，我们的论文是第一个完全利用深度强化学习成功完成跨越间隙任务的工作。 摘要：Traversing through a tilted narrow gap is previously an intractable task for reinforcement learning mainly due to two challenges. First, searching feasible trajectories is not trivial because the goal behind the gap is difficult to reach. Second, the error tolerance after Sim2Real is low due to the relatively high speed in comparison to the gap's narrow dimensions. This problem is aggravated by the intractability of collecting real-world data due to the risk of collision damage. In this paper, we propose an end-to-end reinforcement learning framework that solves this task successfully by addressing both problems. To search for dynamically feasible flight trajectories, we use curriculum learning to guide the agent towards the sparse reward behind the obstacle. To tackle the Sim2Real problem, we propose a Sim2Real framework that can transfer control commands to a real quadrotor without using real flight data. To the best of our knowledge, our paper is the first work that accomplishes successful gap traversing task purely using deep reinforcement learning.

【10】 A Hybrid Rule-Based and Data-Driven Approach to Driver Modeling through Particle Filtering 标题：基于规则和数据驱动的混合粒子滤波驾驶员建模方法 链接：https://arxiv.org/abs/2108.12820

作者：Raunak Bhattacharyya,Soyeon Jung,Liam Kruse,Ransalu Senanayake,Mykel Kochenderfer 机构： Kochender-fer are with the Stanford Intelligent Systems Laboratory in the Departmentof Aeronautics and Astronautics at Stanford University 备注：arXiv admin note: text overlap with arXiv:2005.02597 摘要：自动驾驶车辆需要模拟周围人类驾驶车辆的行为，才能成为安全高效的交通参与者。现有的人类驾驶行为建模方法依赖于数据驱动和基于规则的方法。虽然数据驱动模型更具表现力，但基于规则的模型是可解释的，这是驾驶等安全关键领域的一项重要要求。然而，基于规则的模型不能充分代表数据，并且由于不现实的驾驶行为（如碰撞），数据驱动的模型仍然无法生成真实的交通仿真。在本文中，我们提出了一种将基于规则的建模与数据驱动学习相结合的方法。虽然规则由驾驶员模型的可解释参数控制，但这些参数是使用粒子滤波从驾驶演示数据在线学习的。我们使用来自三个真实驾驶演示数据集的数据，对高速公路驾驶和合并任务进行了驾驶员建模实验。我们的结果表明，基于混合规则和数据驱动方法的驾驶员模型能够准确地捕捉真实世界的驾驶行为。此外，我们通过让人类进行驾驶图灵测试来评估我们的模型生成的驾驶行为的真实性，在测试中，他们被要求区分真实驾驶视频和使用我们的驾驶模型生成的视频。 摘要：Autonomous vehicles need to model the behavior of surrounding human driven vehicles to be safe and efficient traffic participants. Existing approaches to modeling human driving behavior have relied on both data-driven and rule-based methods. While data-driven models are more expressive, rule-based models are interpretable, which is an important requirement for safety-critical domains like driving. However, rule-based models are not sufficiently representative of data, and data-driven models are yet unable to generate realistic traffic simulation due to unrealistic driving behavior such as collisions. In this paper, we propose a methodology that combines rule-based modeling with data-driven learning. While the rules are governed by interpretable parameters of the driver model, these parameters are learned online from driving demonstration data using particle filtering. We perform driver modeling experiments on the task of highway driving and merging using data from three real-world driving demonstration datasets. Our results show that driver models based on our hybrid rule-based and data-driven approach can accurately capture real-world driving behavior. Further, we assess the realism of the driving behavior generated by our model by having humans perform a driving Turing test, where they are asked to distinguish between videos of real driving and those generated using our driver models.

【11】 The rUNSWift SPL Field Segmentation Dataset 标题：rUNSWift SPL字段分段数据集 链接：https://arxiv.org/abs/2108.12809

作者：Wentao Lu 机构：School of Computer Science and Engineering, The University of New South Wales 摘要：在RoboCup SPL中，足球场分割被广泛认为是最关键的机器人视觉问题之一。关键挑战包括动态光照条件、单个机器人的不同校准状态、各种摄像机前景等等。在本文中，我们提出了一个包含20个视频的数据集，这些视频由rUNSWift团队在不同情况下使用Nao V5/V6 humanroid机器人录制。每个视频都包含几个连续的高分辨率帧和相应的字段标签。我们提出这个数据集为联盟提供训练数据，以克服场地分割问题。该数据集将可在线下载。注释的细节和用法示例将在后面的章节中解释。 摘要：In RoboCup SPL, soccer field segmentation has been widely recognised as one of the most critical robot vision problems. Key challenges include dynamic light condition, different calibration status for individual robot, various camera prospective and more. In this paper, we propose a dataset that contains 20 videos recorded with Nao V5/V6 humanroid robots by team rUNSWift under different circumstances. Each of the videos contains several consecutive high resolution frames and the corresponding labels for field. We propose this dataset to provide training data for the league to overcome field segmentation problem. The dataset will be available online for download. Details of annotation and example of usage will be explained in later sections.

【12】 An Experimental Validation and Comparison of Reaching Motion Models for Unconstrained Handovers: Towards Generating Humanlike Motions for Human-Robot Handovers 标题：用于无约束切换的到达运动模型的实验验证和比较：为人-机器人切换生成类人运动 链接：https://arxiv.org/abs/2108.12780

作者：Wesley P. Chan,Tin Tran,Sara Sheikholeslami,Elizabeth Croft 机构： Monash University 2Mechanical Engineering, University of British Columbia© 20 2 1 IEEE 备注：Accepted at Humanoids 2020, "The 2020 IEEE-RAS International Conference on Humanoid Robots"; 6 pages, 7 figures, 1 table 摘要：对于单人任务中的人体点到点到达运动，文献中长期引用了最小挺举运动模型。虽然已经证明，在人类-机器人切换的联合动作任务中，将最小挺举样轨迹应用于机器人到达运动，可以让机器人给予者被认为更谨慎、安全和熟练，但尚未验证人类在切换中的到达运动是否遵循最小挺举模型。为了在实验上测试和验证切换中人体伸展的运动模型，我们检查了无约束切换中的人体伸展运动（允许人移动全身），并根据1）最小冲动模型，2）其变化，解耦最小冲动模型，3）最近提出的椭圆（圆锥）模型。结果表明，二次曲线模型最适合无约束人体运动。此外，我们还发现，与受约束的单人到达运动（已被发现为椭圆）不同，椭圆和双曲二次曲线类型之间存在分裂。我们希望我们的研究结果将有助于指导生成更多类似人类的到达动作，以完成人类-机器人的交接任务。 摘要：The Minimum Jerk motion model has long been cited in literature for human point-to-point reaching motions in single-person tasks. While it has been demonstrated that applying minimum-jerk-like trajectories to robot reaching motions in the joint action task of human-robot handovers allows a robot giver to be perceived as more careful, safe, and skilled, it has not been verified whether human reaching motions in handovers follow the Minimum Jerk model. To experimentally test and verify motion models for human reaches in handovers, we examined human reaching motions in unconstrained handovers (where the person is allowed to move their whole body) and fitted against 1) the Minimum Jerk model, 2) its variation, the Decoupled Minimum Jerk model, and 3) the recently proposed Elliptical (Conic) model. Results showed that Conic model fits unconstrained human handover reaching motions best. Furthermore, we discovered that unlike constrained, single-person reaching motions, which have been found to be elliptical, there is a split between elliptical and hyperbolic conic types. We expect our results will help guide generation of more humanlike reaching motions for human-robot handover tasks.

【13】 Risk Assessment, Prediction, and Avoidance of Collision in Autonomous Drones 标题：自主无人机碰撞风险评估、预测和避免 链接：https://arxiv.org/abs/2108.12770

作者：Anamta Khan 机构：CISUC, Department of Informatics Engineering, University of Coimbra, Portugal 备注：Editor: Marcello Cinque. 17th European Dependable Computing Conference (EDCC 2021), September 13-16, 2021, Munich, Germany. Student Forum Proceedings- EDCC 2021 摘要：无人机（UAV），特别是无人机，在多个领域（主要是军事用途）具有重要意义。最近，我们可以看到在民用空间对自主无人机的接受度也在增长。然而，在无人机能够在没有人类监视的情况下安全使用之前，还有很长的路要走。许多子系统和组件涉及到位置估计、路线规划、软件/数据安全和碰撞避免，以使无人驾驶飞机能够在民用空间飞行而不会对自身、其他无人机、环境或人类造成伤害。本研究的最终目标是通过定量安全风险评估，推进避碰和缓解技术。为此，需要识别无人机飞行/任务期间可能发生的最相关故障/故障/威胁。历史数据分析也是一种相关工具，有助于描述无人机系统中最常见和最相关的问题，这些问题可能导致安全危害。然后，我们需要使用故障注入技术，定量地估计它们的影响。了解无人机日益增长的兴趣及其未来商业应用的巨大潜力，这项工作的预期结果将有助于研究人员进行未来的相关研究。此外，我们设想公司利用预期结果开发更安全的无人机应用，空中交通管制员利用预期结果构建故障预测和防撞解决方案。 摘要：Unmanned Aerial Vehicles (UAVs), in particular Drones, have gained significant importance in diverse sectors, mainly military uses. Recently, we can see a growth in acceptance of autonomous UAVs in civilian spaces as well. However, there is still a long way to go before drones are capable enough to be safely used without human surveillance. A lot of subsystems and components are involved in taking care of position estimation, route planning, software/data security, and collision avoidance to have autonomous drones that fly in civilian spaces without being harmful to themselves, other UAVs, environment, or humans. The ultimate goal of this research is to advance collision avoidance and mitigation techniques through quantitative safety risk assessment. To this end, it is required to identify the most relevant faults/failures/threats that can happen during a drone's flight/mission. The analysis of historical data is also a relevant instrument to help to characterize the most frequent and relevant issues in UAV systems, which may cause safety hazards. Then we need to estimate their impact quantitatively, by using fault injection techniques. Knowing the growing interests in UAVs and their huge potential for future commercial applications, the expected outcome of this work will be helpful to researchers for future related research studies. Furthermore, we envisage the utilization of expected results by companies to develop safer drone applications, and by air traffic controllers for building failure prediction and collision avoidance solutions.

【14】 A Predictive Application Offloading Algorithm Using Small Datasets for Cloud Robotics 标题：一种基于小数据集的云机器人预测性应用卸载算法 链接：https://arxiv.org/abs/2108.12616

作者：Manoj Penmetcha,Shyam Sundar Kannan,Byung-Cheol Min 机构： Department of Computer andInformation Technology, Purdue University 摘要：许多对机器人性能至关重要的机器人应用程序需要即时反馈，因此执行时间是一个关键问题。此外，机器人通常具有固定数量的硬件资源；如果应用程序需要的计算资源超过机器人所能容纳的，则其板载执行可能会扩展到降低机器人性能的程度。另一方面，云计算以按需计算资源为特点；通过使机器人能够利用这些资源，可以减少应用程序的执行时间。使机器人能够使用云计算的关键是设计一种高效的卸载算法，该算法能够最佳地利用机器人的车载能力，并在没有任何应用程序的先验知识或信息的情况下，就何时卸载达成快速共识。在本文中，我们提出了一种预测算法，以预测在给定的应用程序数据输入大小下，借助于少量先前的观察结果执行应用程序所需的时间。为了验证该算法，我们在之前的N个观测值上对其进行训练，其中包括独立（输入数据大小）和依赖（执行时间）变量。为了了解算法性能在预测精度和误差方面的变化，我们使用线性回归和移动机器人路径规划应用程序测试了各种N值。通过实验和分析，我们确定当N>40时，该算法具有可接受的误差和预测精度。 摘要：Many robotic applications that are critical for robot performance require immediate feedback, hence execution time is a critical concern. Furthermore, it is common that robots come with a fixed quantity of hardware resources; if an application requires more computational resources than the robot can accommodate, its onboard execution might be extended to a degree that degrades the robot performance. Cloud computing, on the other hand, features on-demand computational resources; by enabling robots to leverage those resources, application execution time can be reduced. The key to enabling robot use of cloud computing is designing an efficient offloading algorithm that makes optimum use of the robot onboard capabilities and also forms a quick consensus on when to offload without any prior knowledge or information about the application. In this paper, we propose a predictive algorithm to anticipate the time needed to execute an application for a given application data input size with the help of a small number of previous observations. To validate the algorithm, we train it on the previous N observations, which include independent (input data size) and dependent (execution time) variables. To understand how algorithm performance varies in terms of prediction accuracy and error, we tested various N values using linear regression and a mobile robot path planning application. From our experiments and analysis, we determined the algorithm to have acceptable error and prediction accuracy when N>40.

【15】 An implementation of ROS Autonomous Navigation on Parallax Eddie platform 标题：ROS自主导航在视差Eddie平台上的实现 链接：https://arxiv.org/abs/2108.12571

作者：Hafiq Anas,Wee Hong Ong 机构：School of Digital Science, Universiti Brunei Darussalam, Jalan Tungku Link, Brunei, nd Ong Wee Hong 备注：12 pages, 23 figures, 9 tables, 24 equations 摘要：提出了一种基于机器人操作系统（ROS）的轮式差速驱动移动平台Eddie Robot自主导航功能的实现方法。ROS是一个包含许多可重用软件堆栈以及可视化和调试工具的框架，为任何机器人项目开发提供了理想的环境。本文的主要贡献是描述了Eddie robot定制的硬件和软件系统设置，以在ROS中使用称为导航堆栈的自主导航系统，并实现一个自主导航应用用例。在本文中，选择拍照来演示移动机器人的一个用例。 摘要：This paper presents an implementation of autonomous navigation functionality based on Robot Operating System (ROS) on a wheeled differential drive mobile platform called Eddie robot. ROS is a framework that contains many reusable software stacks as well as visualization and debugging tools that provides an ideal environment for any robotic project development. The main contribution of this paper is the description of the customized hardware and software system setup of Eddie robot to work with an autonomous navigation system in ROS called Navigation Stack and to implement one application use case for autonomous navigation. For this paper, photo taking is chosen to demonstrate a use case of the mobile robot.

【16】 Anytime Stochastic Task and Motion Policies 标题：随时随地随机任务和运动策略 链接：https://arxiv.org/abs/2108.12537

作者：Naman Shah,Siddharth Srivastava 机构： It also provides a running estimate of 1Arizona State University, School of Computing, Arizona State University 摘要：为了解决复杂、长视距的任务，智能机器人需要结合运动规划进行高级、抽象的规划和推理。然而，抽象模型通常是有损的，使用它们计算的计划或策略可能是不可执行的。这些问题在随机情况下会加剧，机器人需要对多种意外情况进行推理和计划。我们提出了一种在随机环境下集成任务和运动规划的新方法。与以前在这个方向上的工作相比，我们表明我们的方法可以有效地计算集成的任务和运动策略，这些策略的分支结构编码处理多个执行时间偶然事件的代理行为。我们证明了我们的算法是概率完全的，并且可以随时计算可行解策略，从而使遇到未解决的偶然事件的概率随着时间的推移而降低。一组具有挑战性问题的实证结果表明了我们方法的实用性和适用范围。 摘要：In order to solve complex, long-horizon tasks, intelligent robots need to carry out high-level, abstract planning and reasoning in conjunction with motion planning. However, abstract models are typically lossy and plans or policies computed using them can be inexecutable. These problems are exacerbated in stochastic situations where the robot needs to reason about and plan for multiple contingencies. We present a new approach for integrated task and motion planning in stochastic settings. In contrast to prior work in this direction, we show that our approach can effectively compute integrated task and motion policies whose branching structures encode agent behaviors that handle multiple execution-time contingencies. We prove that our algorithm is probabilistically complete and can compute feasible solution policies in an anytime fashion so that the probability of encountering an unresolved contingency decreases over time. Empirical results on a set of challenging problems show the utility and scope of our method.

【17】 DASH: Modularized Human Manipulation Simulation with Vision and Language for Embodied AI 标题：DASH：面向体验式人工智能的视觉和语言模块化人工操作仿真 链接：https://arxiv.org/abs/2108.12536

作者：Yifeng Jiang,Michelle Guo,Jiangshan Li,Ioannis Exarchos,Jiajun Wu,C. Karen Liu 机构：Stanford University, United States of America 备注：None 摘要：创建具有体现的、类似人的感知和驱动约束的虚拟人有望为许多科学和工程应用提供一个集成的仿真平台。我们介绍了动态和自主模拟人（DASH），一种具体化的虚拟人，在给定自然语言命令的情况下，仅使用其自身的视觉感知、本体感觉和触觉，在物理模拟的杂乱环境中执行抓取和堆叠任务，而不需要人体运动数据。通过将DASH系统分解为两个技能类别的视觉模块、语言模块和操作模块，我们可以混合和匹配不同模块的分析和机器学习技术，使DASH不仅能够以高成功率执行随机安排的任务，但也可以在拟人化的约束下，通过流动和多样的运动来实现。模块化设计还支持对更复杂的操作技能进行分析和扩展。 摘要：Creating virtual humans with embodied, human-like perceptual and actuation constraints has the promise to provide an integrated simulation platform for many scientific and engineering applications. We present Dynamic and Autonomous Simulated Human (DASH), an embodied virtual human that, given natural language commands, performs grasp-and-stack tasks in a physically-simulated cluttered environment solely using its own visual perception, proprioception, and touch, without requiring human motion data. By factoring the DASH system into a vision module, a language module, and manipulation modules of two skill categories, we can mix and match analytical and machine learning techniques for different modules so that DASH is able to not only perform randomly arranged tasks with a high success rate, but also do so under anthropomorphic constraints and with fluid and diverse motions. The modular design also favors analysis and extensibility to more complex manipulation skills.

【18】 Learning Inner-Group Relations on Point Clouds 标题：学习点云上的群内关系 链接：https://arxiv.org/abs/2108.12468

作者：Haoxi Ran,Wei Zhuo,Jun Liu,Li Lu 机构： Sichuan University, † Tencent 备注：ICCV 2021. arXiv admin note: text overlap with arXiv:2011.14285 摘要：计算机视觉中关系网络的流行与未充分探索的基于点的方法形成了鲜明的对比。在本文中，我们探讨了局部关系算子的可能性，并考察了它们的可行性。我们提出了一个可扩展且高效的模块，称为组关系聚合器。该模块根据几何关系和语义关系加权的内部组点的特征聚合来计算组的特征。我们采用这个模块来设计我们的RPNet。我们进一步验证了RPNet在深度和宽度两个方面对分类和分割任务的可扩展性。令人惊讶的是，实证结果表明，较宽的RPNet适合分类，而较深的RPNet更适合分割。RPNet在具有挑战性的基准上实现了最先进的分类和分割。我们还将本地聚合器与PointNet++进行了比较，它们的参数大约为30%，计算量节省了50%。最后，我们通过实验验证了RPNet对刚性变换和噪声的鲁棒性。 摘要：The prevalence of relation networks in computer vision is in stark contrast to underexplored point-based methods. In this paper, we explore the possibilities of local relation operators and survey their feasibility. We propose a scalable and efficient module, called group relation aggregator. The module computes a feature of a group based on the aggregation of the features of the inner-group points weighted by geometric relations and semantic relations. We adopt this module to design our RPNet. We further verify the expandability of RPNet, in terms of both depth and width, on the tasks of classification and segmentation. Surprisingly, empirical results show that wider RPNet fits for classification, while deeper RPNet works better on segmentation. RPNet achieves state-of-the-art for classification and segmentation on challenging benchmarks. We also compare our local aggregator with PointNet++, with around 30% parameters and 50% computation saving. Finally, we conduct experiments to reveal the robustness of RPNet with regard to rigid transformation and noises.

机器翻译，仅供参考