机器人相关学术速递[7.5]

cs.RO机器人相关，共计23篇

【1】 ReQuBiS -- Reconfigurable Quadrupedal-Bipedal Snake Robots 标题：ReQubiS--可重构的四足两足蛇形机器人

作者：Harshad Zade,Aadesh Varude,Karan Pandya,Ajinkya Kamat,Shital Chiddarwar,Rohan Thakker 机构：India) 1Visvesvaraya National Institute of Technology 备注：Conference: CASE-2021. Video: Experimental results are available at this https URL 链接：https://arxiv.org/abs/2107.01197 摘要：机器人导航移动模式的选择包括各种权衡。蛇形机器人非常适合穿越管道、杂乱和崎岖的地形等受限环境，而两足机器人则更适合于楼梯等结构化环境。最后，四足机器人比两足动物更稳定，可以携带比蛇和两足动物更大的有效载荷，但很难在软土、沙子、冰和受限环境中航行。一个可重构机器人可以实现所有世界上最好的。不幸的是，最先进的可重构机器人依赖于通过复杂的机构重新排列模块，在不同的位置进行分解和组装，从而增加了对尺寸、重量和功率（SWaP）的要求。提出了一种可重构的四足-两足蛇形机器人（rekubis），它可以在不同的移动模式之间进行转换，而无需重新排列模块。因此，只需要一个修改机制。此外，我们的设计允许机器人分成两个代理并行执行两足动物和蛇的移动任务。实验结果证明了这些移动能力在蛇，四足动物和两足动物模式和他们之间的过渡。 摘要：The selection of mobility modes for robot navigation consists of various trade-offs. Snake robots are ideal for traversing through constrained environments such as pipes, cluttered and rough terrain, whereas bipedal robots are more suited for structured environments such as stairs. Finally, quadruped robots are more stable than bipeds and can carry larger payloads than snakes and bipeds but struggle to navigate soft soil, sand, ice, and constrained environments. A reconfigurable robot can achieve the best of all worlds. Unfortunately, state-of-the-art reconfigurable robots rely on the rearrangement of modules through complicated mechanisms to dissemble and assemble at different places, increasing the size, weight, and power (SWaP) requirements. We propose Reconfigurable Quadrupedal-Bipedal Snake Robots (ReQuBiS), which can transform between mobility modes without rearranging modules. Hence, requiring just a single modification mechanism. Furthermore, our design allows the robot to split into two agents to perform tasks in parallel for biped and snake mobility. Experimental results demonstrate these mobility capabilities in snake, quadruped, and biped modes and transitions between them.

【2】 Collaborative Visual Navigation 标题：协作视觉导航

作者：Haiyang Wang,Wenguan Wang,Xizhou Zhu,Jifeng Dai,Liwei Wang 机构： Key Laboratory of Machine Perception, MOE, Peking University, SenseTime Research , Computer Vision Lab, ETH Zurich 链接：https://arxiv.org/abs/2107.01151 摘要：多智能体系统（multi-agent system，MAS）作为人工智能的一个基本问题，在多智能体强化学习（multi-agent reinforcement learning，MARL）技术的推动下得到了迅速的发展。然而，以前的MARL方法主要集中在网格世界或游戏环境中；在视觉丰富的环境中，MAS的研究仍然较少。为了缩小这一差距，强调感知在多智能体视觉导航中的重要作用，我们提出了一个用于多智能体视觉导航的大规模三维数据集CollaVN。在CollaVN中，需要多个代理协同导航，穿越照片真实的环境到达目标位置。探索了不同的MAVN变体，使我们的问题更具一般性。此外，还提出了一种记忆增强的通信框架。每个代理都配备有一个私有的外部内存，用于持久存储通信信息。这使得代理能够更好地利用其过去的通信信息，从而实现更高效的协作和稳健的长期规划。在我们的实验中，我们设计了一些基线和评估指标。我们还通过实验验证了我们提出的MARL方法在不同MAVN任务设置下的有效性。 摘要：As a fundamental problem for Artificial Intelligence, multi-agent system (MAS) is making rapid progress, mainly driven by multi-agent reinforcement learning (MARL) techniques. However, previous MARL methods largely focused on grid-world like or game environments; MAS in visually rich environments has remained less explored. To narrow this gap and emphasize the crucial role of perception in MAS, we propose a large-scale 3D dataset, CollaVN, for multi-agent visual navigation (MAVN). In CollaVN, multiple agents are entailed to cooperatively navigate across photo-realistic environments to reach target locations. Diverse MAVN variants are explored to make our problem more general. Moreover, a memory-augmented communication framework is proposed. Each agent is equipped with a private, external memory to persistently store communication information. This allows agents to make better use of their past communication information, enabling more efficient collaboration and robust long-term planning. In our experiments, several baselines and evaluation metrics are designed. We also empirically verify the efficacy of our proposed MARL approach across different MAVN task settings.

【3】 4C: A Computation, Communication, and Control Co-Design Framework for CAVs 标题：4C：一个面向CAVS的计算、通信和控制协同设计框架

作者：Liangkai Liu,Shaoshan Liu,Weisong Shi 机构：∗Wayne State University, †PerceptIn 备注：7 pages, 4 figures, accepted by IEEE Wireless Communication Magazine 链接：https://arxiv.org/abs/2107.01142 摘要：互联和自主车辆（cav）由于其潜在的安全性和效率优势而具有广阔的发展前景，并吸引了政府机构、工业界和学术界的大量投资和兴趣。随着更多的计算和通信资源可用，车辆和边缘服务器都配备了一组基于摄像头的视觉传感器，也称为视觉物联网（V-IoT）技术，用于感知和感知。为了实现可编程通信、计算和控制，人们付出了巨大的努力。然而，它们主要在竖井模式下进行，限制了在现实世界中处理具有挑战性场景的响应能力和效率。为了提高端到端的性能，我们设想未来的cav需要通信、计算和控制的协同设计。本文提出了CAVs的端到端设计原则4C，它通过提供统一的通信、计算和控制协同设计框架来扩展V-IoT系统。通过可编程通信、细粒度异构计算和4C中高效的车辆控制，CAVs可以处理关键场景并实现节能自动驾驶。最后，我们提出了实现4C框架愿景的几个挑战。 摘要：Connected and autonomous vehicles (CAVs) are promising due to their potential safety and efficiency benefits and have attracted massive investment and interest from government agencies, industry, and academia. With more computing and communication resources are available, both vehicles and edge servers are equipped with a set of camera-based vision sensors, also known as Visual IoT (V-IoT) techniques, for sensing and perception. Tremendous efforts have been made for achieving programmable communication, computation, and control. However, they are conducted mainly in the silo mode, limiting the responsiveness and efficiency of handling challenging scenarios in the real world. To improve the end-to-end performance, we envision that future CAVs require the co-design of communication, computation, and control. This paper presents our vision of the end-to-end design principle for CAVs, called 4C, which extends the V-IoT system by providing a unified communication, computation, and control co-design framework. With programmable communications, fine-grained heterogeneous computation, and efficient vehicle controls in 4C, CAVs can handle critical scenarios and achieve energy-efficient autonomous driving. Finally, we present several challenges to achieving the vision of the 4C framework.

【4】 Decision-Making Technology for Autonomous Vehicles Learning-Based Methods, Applications and Future Outlook 标题：基于学习的自动驾驶车辆决策技术方法、应用及未来展望

作者：Qi Liu,Xueyuan Li,Shihua Yuan,Zirui Li 机构：Civil Engineering and Geosciences, Delft University of Technology, Stevinweg , CN Delft, The Netherlands., learning-based methods are utilized to achieve better, decision-making for autonomous vehicles [,]; in addition, with 备注：8 pages, 1 figure, 5 tables, ITSC2021(accepted) 链接：https://arxiv.org/abs/2107.01110 摘要：自主车辆在民用和军事领域都有着巨大的应用潜力，随着科学和经济的飞速发展，自主车辆已成为研究的热点。基于学习的自主车辆决策技术对于提高自主车辆的安全性和高效性具有重要意义，本文对其进行了简要的综述。首先，给出了决策技术的基本概况。其次，对基于学习的自主车辆决策方法的相关研究进行了综述，并与经典的决策方法进行了比较。此外，还总结了决策方法在现有自主车辆中的应用。最后，对未来自主车辆决策技术的研究方向进行了展望。 摘要：Autonomous vehicles have a great potential in the application of both civil and military fields, and have become the focus of research with the rapid development of science and economy. This article proposes a brief review on learning-based decision-making technology for autonomous vehicles since it is significant for safer and efficient performance of autonomous vehicles. Firstly, the basic outline of decision-making technology is provided. Secondly, related works about learning-based decision-making methods for autonomous vehicles are mainly reviewed with the comparison to classical decision-making methods. In addition, applications of decision-making methods in existing autonomous vehicles are summarized. Finally, promising research topics in the future study of decision-making technology for autonomous vehicles are prospected.

【5】 RMQFMU: Bridging the Real World with Co-simulation Technical Report 标题：RMQFMU：用联合仿真技术报告搭建现实世界的桥梁

作者：Mirgita Frasheri,Henrik Ejersbo,Casper Thule,Lukas Esterle 机构：Centre for Digital Twins, DIGIT, Aarhus University, Finlandsgade , Aarhus N, Denmark 备注：To be submitted in brief format to 19th overture workshop. Will be updated after reviews 链接：https://arxiv.org/abs/2107.01010 摘要：在本文中，我们为RMQ \-FMU提供了一份经验报告，RMQ \-FMU是一种即插即用工具，能够将数据传送到基于AMQP协议的基于FMI2的协同仿真环境中。将协同仿真桥接到外部环境允许一方将历史数据提供给协同仿真，用于不同的目的，例如可视化和/或数据分析。另一方面，这种工具通过将协同仿真和硬件/机器人接近实时地耦合，促进了数字孪生概念的实现。在本文中，我们提出了初始版本的RMQFMU在桥接协同仿真与真实世界的能力方面的局限性。为了提供所需的工具功能，我们以逐步的方式介绍如何减轻这些限制以及随后的限制。我们进行了各种实验，以便对所进行的修改给出理由。最后，我们报告了我们采用RMQFMU的两个案例研究，并提供了旨在帮助从业者使用该工具的指南。 摘要：In this paper we present an experience report for the RMQ\-FMU, a plug and play tool, that enables feeding data to/from an FMI2-based co-simulation environment based on the AMQP protocol. Bridging the co-simulation to an external environment allows on one side to feed historical data to the co-simulation, serving different purposes, such as visualisation and/or data analysis. On the other side, such a tool facilitates the realisation of the digital twin concept by coupling co-simulation and hardware/robots close to real-time. In the paper we present limitations of the initial version of the RMQFMU with respect to the capability of bridging co-simulation with the real world. To provide the desired functionality of the tool, we present in a step-by-step fashion how these limitations, and subsequent limitations, are alleviated. We perform various experiments in order to give reason to the modifications carried out. Finally, we report on two case-studies where we have adopted the RMQFMU, and provide guidelines meant to aid practitioners in the use of the tool.

【6】 Developing system of wireless sensor network and unmaned aerial vehicle for agriculture inspection 标题：农业监测无线传感器网络与无人驾驶飞行器开发系统

作者：Nguyen Truong Son,Quach Cong Hoang,Dang Thi Huong Giang,Vu Minh Trung,Vuong Quang Huy,Mai Anh Tuan 备注：None 链接：https://arxiv.org/abs/2107.01008 摘要：利用高科技进行农业生产是越南农业发展的必然趋势。特别是对于通常需要大面积种植的物质作物，无线传感器网络在提高生产力、监测病虫害、减轻气候变化的影响以及减少种植者的直接劳动等方面显然发挥了重要作用。本文利用LoRa无线传感器网络和无人机相结合，建立了一个农业作物田间监测的实验模型，用于收集气象、土壤状况、植物健康等方面的数据，帮助种植者在灌溉、病虫害防治、农业生产等方面做出正确的决策，对目前种植的作物进行施肥。该系统已在现场开发和试验，以评估一些基本特征，并验证所获得数据的稳定性和可靠性。 摘要：Agricultural production using high technology is an inevitable trend in Vietnam. Especially for material crops which typically need large growing areas, wireless sensor networks has been clearly playing a significant role in increasing productivity, monitoring pests and diseases, mitigating the impact of climate change, and reducing the direct labor of cultivators. This paper constructs an experimental model of agricultural crop field monitoring using a combination of LoRa wireless sensor networks and unmanned aerial vehicles to collect data on conditions of weather and soil, plant health, which helps growers easily making right decisions on solutions for irrigation, pest treatment, and fertilization with the currently planted crops. The system has been developed and experimentized in the field to evaluate some basic features and justified the stability and reliability of the obtained data.

【7】 Brain over Brawn -- Using a Stereo Camera to Detect, Track and Intercept a Faster UAV by Reconstructing Its Trajectory 标题：Brain over Brown--使用立体摄像机通过重建轨迹来探测、跟踪和拦截速度更快的无人机

作者：Antonella Barišić,Frano Petric,Stjepan Bogdan 机构：Laboratory for Robotics and Intelligent Control Systems, University of Zagreb, Unska , Zagreb, Croatia 备注：To be published in Field Robotics. UAV-Eagle dataset available at: this https URL 链接：https://arxiv.org/abs/2107.00962 摘要：本文介绍的工作展示了我们拦截更快的入侵者无人机的方法，灵感来自MBZIRC2020挑战1。通过利用入侵者轨迹形状的知识，我们能够计算出拦截点。目标跟踪是基于一个YOLOv3微型卷积神经网络的图像处理，结合一个框架安装的小型立体相机的深度计算。利用ZED-Mini的RGB和深度数据提取目标的三维位置，并设计了基于深度直方图的图像去噪处理方法。利用所获得的目标位置的三维测量值，计算出一个八字形轨迹的位置、方向和大小，并用Bernoulli的lemniscate近似。一旦近似值被认为足够精确，通过测量值和近似值之间的Hausdorff距离来测量，就计算出一个拦截点，使拦截的无人机正好位于目标的路径上。该方法在MBZIRC比赛中取得了显著的改进，并通过仿真和现场试验进行了验证。结果表明，该系统能够有效地提取目标无人机的运动信息，为目标无人机的拦截提供依据。该系统能够对目标进行跟踪拦截，在大多数仿真实验中比拦截器快30%。在非结构化环境中的测试得到了12个成功结果中的9个。 摘要：The work presented in this paper demonstrates our approach to intercepting a faster intruder UAV, inspired by the MBZIRC2020 Challenge 1. By leveraging the knowledge of the shape of the intruder's trajectory we are able to calculate the interception point. Target tracking is based on image processing by a YOLOv3 Tiny convolutional neural network, combined with depth calculation using a gimbal-mounted ZED Mini stereo camera. We use RGB and depth data from ZED Mini to extract the 3D position of the target, for which we devise a histogram-of-depth based processing to reduce noise. Obtained 3D measurements of target's position are used to calculate the position, the orientation and the size of a figure-eight shaped trajectory, which we approximate using lemniscate of Bernoulli. Once the approximation is deemed sufficiently precise, measured by Hausdorff distance between measurements and the approximation, an interception point is calculated to position the intercepting UAV right on the path of the target. The proposed method, which has been significantly improved based on the experience gathered during the MBZIRC competition, has been validated in simulation and through field experiments. The results confirmed that an efficient visual perception module which extracts information related to the motion of the target UAV as a basis for the interception, has been developed. The system is able to track and intercept the target which is 30% faster than the interceptor in majority of simulation experiments. Tests in the unstructured environment yielded 9 out of 12 successful results.

【8】 Embodiment and Computational Creativity 标题：体现与计算创造力

作者：Christian Guckelsberger,Anna Kantosalo,Santiago Negrete-Yankelevich,Tapio Takala 机构：Finnish Center for Artificial Intelligence, Department of Computer Science, Aalto University, Espoo, Finland, School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK 备注：10 pages, 1 Table, 1 Figure. Accepted as full paper at the International Conference on Computational Creativity (ICCC) 2021 链接：https://arxiv.org/abs/2107.00949 摘要：我们推测，创造力和对创造力的感知，至少在某种程度上，是由化身塑造的。这使得具体化与计算创造力（CC）的研究高度相关，但是现有的研究很少，并且概念的使用非常模糊。我们通过对国际计算创造力会议上的出版物进行系统的回顾和规范性的分析来克服这种情况。我们通过识别和比较概念的不同用法，采用并扩展已建立的具体化类型来解决歧义。我们收集、分析和强调了在CC中拥抱化身的机遇和挑战，作为研究的参考，并提出了进一步推进化身CC研究计划的重要方向。 摘要：We conjecture that creativity and the perception of creativity are, at least to some extent, shaped by embodiment. This makes embodiment highly relevant for Computational Creativity (CC) research, but existing research is scarce and the use of the concept highly ambiguous. We overcome this situation by means of a systematic review and a prescriptive analysis of publications at the International Conference on Computational Creativity. We adopt and extend an established typology of embodiment to resolve ambiguity through identifying and comparing different usages of the concept. We collect, contextualise and highlight opportunities and challenges in embracing embodiment in CC as a reference for research, and put forward important directions to further the embodied CC research programme.

【9】 POMP++: Pomcp-based Active Visual Search in unknown indoor environments 标题：Pomp++：未知室内环境下基于Pomcp的主动视觉搜索

作者：Francesco Giuliari,Alberto Castellini,Riccardo Berra,Alessio Del Bue,Alessandro Farinelli,Marco Cristani,Francesco Setti,Yiming Wang 机构： While online policy learning has been used in 1Department of Electrical, Universita degli Studi di Genova, 3Department of Computer Science, University of Verona 备注：Accepted at 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 链接：https://arxiv.org/abs/2107.00914 摘要：本文主要研究在未知室内环境下，在线学习一个最优的主动视觉搜索策略（AVS）问题。我们提出了POMP++，这是一种在经典的部分可观测montecarlo规划（POMCP）框架基础上引入一种新的规划策略，允许在未知环境中进行免费的在线策略学习。我们提出了一种新的信念重建策略，该策略允许使用具有动态增长状态空间的POMCP来解决地板图的在线生成问题。我们在两个公共基准数据集上对我们的方法进行了评估，这两个数据集是由真实机器人平台获取的AVD和由真实3D场景扫描渲染的Habitat ObjectNav，与最先进的方法相比，获得了>10%的最佳成功率。 摘要：In this paper we focus on the problem of learning online an optimal policy for Active Visual Search (AVS) of objects in unknown indoor environments. We propose POMP++, a planning strategy that introduces a novel formulation on top of the classic Partially Observable Monte Carlo Planning (POMCP) framework, to allow training-free online policy learning in unknown environments. We present a new belief reinvigoration strategy which allows to use POMCP with a dynamically growing state space to address the online generation of the floor map. We evaluate our method on two public benchmark datasets, AVD that is acquired by real robotic platforms and Habitat ObjectNav that is rendered from real 3D scene scans, achieving the best success rate with an improvement of >10% over the state-of-the-art methods.

【10】 Cooperative Autonomous Vehicles that Sympathize with Human Drivers 标题：同情人类驾驶员的合作自主车辆

作者：Behrad Toghi,Rodolfo Valiente,Dorsa Sadigh,Ramtin Pedarsani,Yaser P. Fallah 机构： 3 Department of Electrical and Computer Engineering, University ofCalifornia 备注：Accepted in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 链接：https://arxiv.org/abs/2107.00898 摘要：除非开发出能够使这些智能代理与人类共存的解决方案，否则广泛采用自主车辆将不会成为现实。这包括安全有效地与人类驾驶的车辆进行交互，特别是在冲突和竞争场景中。我们建立在先前关于社会意识导航的工作基础上，借用心理学中的社会价值取向概念——这一概念正式说明了一个人对他人福利的重视程度——以诱导自主驾驶中的利他行为。与现有的明确模拟人类驾驶员行为并依靠其预期反应来创造合作机会的工作不同，我们的交感合作驾驶（SymCoDrive）范式训练利他代理，仅通过经验学习，在没有任何明确协调的情况下，在竞争性驾驶场景中实现安全和平稳的交通流。由于这种利他主义行为，我们在安全性和交通水平指标方面都有了显著的改善，重要的是，我们得出结论，代理的利他主义水平需要适当的调整，因为过于利他主义的代理也会导致次优交通流。代码和补充资料可从以下网址获得：https://symcodrive.toghi.net/ 摘要：Widespread adoption of autonomous vehicles will not become a reality until solutions are developed that enable these intelligent agents to co-exist with humans. This includes safely and efficiently interacting with human-driven vehicles, especially in both conflictive and competitive scenarios. We build up on the prior work on socially-aware navigation and borrow the concept of social value orientation from psychology -- that formalizes how much importance a person allocates to the welfare of others -- in order to induce altruistic behavior in autonomous driving. In contrast with existing works that explicitly model the behavior of human drivers and rely on their expected response to create opportunities for cooperation, our Sympathetic Cooperative Driving (SymCoDrive) paradigm trains altruistic agents that realize safe and smooth traffic flow in competitive driving scenarios only from experiential learning and without any explicit coordination. We demonstrate a significant improvement in both safety and traffic-level metrics as a result of this altruistic behavior and importantly conclude that the level of altruism in agents requires proper tuning as agents that are too altruistic also lead to sub-optimal traffic flow. The code and supplementary material are available at: https://symcodrive.toghi.net/

【11】 F-LOAM: Fast LiDAR Odometry And Mapping 标题：F-LAAM：快速激光雷达里程计与测绘

作者：Han Wang,Chen Wang,Chun-Lin Chen,Lihua Xie 机构：NanyangTechnologicalUniversity 链接：https://arxiv.org/abs/2107.00822 摘要：同时定位与地图（SLAM）在机器人领域有着广泛的应用，如自动驾驶和无人驾驶飞行器等。计算效率和定位精度对一个好的SLAM系统至关重要。现有的基于激光雷达的SLAM研究通常将问题描述为两个模块：扫描到扫描匹配和扫描到地图细化。这两个模块都是通过迭代计算来解决的，计算量很大。在本文中，我们提出了一个通用的解决方案，旨在为基于激光雷达的SLAM提供一个计算效率高、精度高的框架。具体来说，我们采用了一种非迭代的两级失真补偿方法来降低计算量。对于每个扫描输入，提取边缘和平面特征，分别匹配到局部边缘图和局部平面图，并考虑局部平滑度进行迭代姿态优化。通过深入的实验，评估了其在具有挑战性的场景中的性能，包括仓库自动导引车（AGV）的定位和自动驾驶的公共数据集。该方法在公共数据集评估中以10hz以上的处理率获得了有竞争力的定位精度，为实际应用提供了性能和计算成本之间的良好折衷。 摘要：Simultaneous Localization and Mapping (SLAM) has wide robotic applications such as autonomous driving and unmanned aerial vehicles. Both computational efficiency and localization accuracy are of great importance towards a good SLAM system. Existing works on LiDAR based SLAM often formulate the problem as two modules: scan-to-scan match and scan-to-map refinement. Both modules are solved by iterative calculation which are computationally expensive. In this paper, we propose a general solution that aims to provide a computationally efficient and accurate framework for LiDAR based SLAM. Specifically, we adopt a non-iterative two-stage distortion compensation method to reduce the computational cost. For each scan input, the edge and planar features are extracted and matched to a local edge map and a local plane map separately, where the local smoothness is also considered for iterative pose optimization. Thorough experiments are performed to evaluate its performance in challenging scenarios, including localization for a warehouse Automated Guided Vehicle (AGV) and a public dataset on autonomous driving. The proposed method achieves a competitive localization accuracy with a processing rate of more than 10 Hz in the public dataset evaluation, which provides a good trade-off between performance and computational cost for practical applications.

【12】 A Levy Flight based Narrow Passage Sampling Method for Probabilistic Roadmap Planners 标题：基于Levy飞行的概率路线图规划者窄通道抽样方法

作者：Shubham Shukla,Lokesh Kumar,Titas Bera,Ranjan Dasgupta 链接：https://arxiv.org/abs/2107.00817 摘要：基于抽样的概率路线图规划器（PRM）在高自由度机器人的运动规划中取得了成功，但在具有临界狭窄通道的场景中可能无法捕捉到构型空间的连通性。本文提出了一种新的基于Levy飞行的关键样本生成方法，该方法与PRM相结合，提高了规划器的完备性。然而，与基于纯随机游走的方法相比，该技术在牺牲最小额外计算的情况下显著地提高了样本质量，在冲突调用数量、计算开销和样本质量方面仍然优于现有的随机建桥方法。该方法对与窄通道结构有关的参数变化具有鲁棒性，从而增加了通用性。大量的二维和三维运动规划仿真结果表明了该方法的有效性。 摘要：Sampling based probabilistic roadmap planners (PRM) have been successful in motion planning of robots with higher degrees of freedom, but may fail to capture the connectivity of the configuration space in scenarios with a critical narrow passage. In this paper, we show a novel technique based on Levy Flights to generate key samples in the narrow regions of configuration space, which, when combined with a PRM, improves the completeness of the planner. The technique substantially improves sample quality at the expense of a minimal additional computation, when compared with pure random walk based methods, however, still outperforms state of the art random bridge building method, in terms of number of collision calls, computational overhead and sample quality. The method is robust to the changes in the parameters related to the structure of the narrow passage, thus giving an additional generality. A number of 2D & 3D motion planning simulations are presented which shows the effectiveness of the method.

【13】 Target-dependent UNITER: A Transformer-Based Multimodal Language Comprehension Model for Domestic Service Robots 标题：目标依赖单元器：一种基于Transformer的家政服务机器人多模态语言理解模型

作者：Shintaro Ishikawa,Komei Sugiura 机构： we propose a Transformer [ 3]-based methodthat learns the relationship between the target object and 1AuthorsarewithKeioUniversity 备注：Accepted for presentation at IROS2021 链接：https://arxiv.org/abs/2107.00811 摘要：目前，家政服务机器人通过语言进行自然交互的能力不足。这是因为理解人类的指令是复杂的各种含糊不清和信息缺失。在现有的方法中，指定对象之间关系的引用表达式没有得到充分的建模。本文提出了目标相关单位，它通过聚焦于图像中的相关区域，而不是整个图像，来直接学习目标与其他目标之间的关系。我们的方法是基于单元的Transformer的扩展，可以在通用数据集上进行预训练。我们通过引入一个新的体系结构来处理候选目标，从而扩展了UNITER方法。我们的模型在两个标准数据集上进行了验证，结果表明，目标相关单位在分类精度上优于基线方法。 摘要：Currently, domestic service robots have an insufficient ability to interact naturally through language. This is because understanding human instructions is complicated by various ambiguities and missing information. In existing methods, the referring expressions that specify the relationships between objects are insufficiently modeled. In this paper, we propose Target-dependent UNITER, which learns the relationship between the target object and other objects directly by focusing on the relevant regions within an image, rather than the whole image. Our method is an extension of the UNITER-based Transformer that can be pretrained on general-purpose datasets. We extend the UNITER approach by introducing a new architecture for handling the target candidates. Our model is validated on two standard datasets, and the results show that Target-dependent UNITER outperforms the baseline method in terms of classification accuracy.

【14】 Case Relation Transformer: A Crossmodal Language Generation Model for Fetching Instructions 标题：格关系转换器：一种用于取指令的跨模态语言生成模型

作者：Motonari Kambara,Komei Sugiura 机构： 1The authors are with Keio University 备注：Accepted for presentation at IROS2021 链接：https://arxiv.org/abs/2107.00789 摘要：为了提高家政服务机器人的通信能力，机器人学已经有了许多研究。然而，由于训练数据集不够大，大多数研究并没有充分受益于深层神经网络的最新进展。在本文中，我们的目标是在跨模态语言生成模型的基础上扩充数据集。我们提出了一种Case-relationship-Transformer（CRT），它从一幅图像中生成一个提取指令语句，例如“将蓝色触发器移到左下角方框”。与现有的方法不同，CRT使用这种转换器来集成图像中物体的视觉特征和几何特征。由于Case关系块，CRT可以处理对象。我们进行了对比实验和人体评估。实验结果表明，CRT方法优于基线方法。 摘要：There have been many studies in robotics to improve the communication skills of domestic service robots. Most studies, however, have not fully benefited from recent advances in deep neural networks because the training datasets are not large enough. In this paper, our aim is to augment the datasets based on a crossmodal language generation model. We propose the Case Relation Transformer (CRT), which generates a fetching instruction sentence from an image, such as "Move the blue flip-flop to the lower left box." Unlike existing methods, the CRT uses the Transformer to integrate the visual features and geometry features of objects in the image. The CRT can handle the objects because of the Case Relation Block. We conducted comparison experiments and a human evaluation. The experimental results show the CRT outperforms baseline methods.

【15】 Autonomous Navigation for Quadrupedal Robots with Optimized Jumping through Constrained Obstacles 标题：优化跳跃穿越受限障碍物的四足机器人自主导航

作者：Scott Gilroy,Derek Lau,Lizhi Yang,Ed Izaguirre,Kristen Biermayer,Anxing Xiao,Mengti Sun,Ayush Agrawal,Jun Zeng,Zhongyu Li,Koushil Sreenath 备注：Accepted to 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE 2021) 链接：https://arxiv.org/abs/2107.00773 摘要：四足动物因其灵活和动态的设计而成为在充满挑战的环境中航行的有力人选。本文提出了一种方法，通过创建一个端到端的导航框架，利用步行和跳跃模式，扩展了四足机器人的探索范围。为了在避障的同时获得动态跳跃机动，在安全约束条件下，通过基于配置的优化离线优化动态可行轨迹。这样的优化方案使得机器人能够同时考虑空中和地面的障碍物，跳过窗口形状的障碍物。在自主导航管道中采用跳跃模式，利用基于搜索的全局规划器和局部规划器使机器人通过步行到达目标位置。一个状态机和一个决策策略允许系统在绕过障碍物或跳过障碍物之间切换行为。提出的框架在四足机器人迷你猎豹上进行了实验部署和验证，使机器人能够自主地在环境中导航，同时避开障碍物，并跳过13厘米的最大高度，通过一个窗户形状的开口以达到其目标。 摘要：Quadrupeds are strong candidates for navigating challenging environments because of their agile and dynamic designs. This paper presents a methodology that extends the range of exploration for quadrupedal robots by creating an end-to-end navigation framework that exploits walking and jumping modes. To obtain a dynamic jumping maneuver while avoiding obstacles, dynamically-feasible trajectories are optimized offline through collocation-based optimization where safety constraints are imposed. Such optimization schematic allows the robot to jump through window-shaped obstacles by considering both obstacles in the air and on the ground. The resulted jumping mode is utilized in an autonomous navigation pipeline that leverages a search-based global planner and a local planner to enable the robot to reach the goal location by walking. A state machine together with a decision making strategy allows the system to switch behaviors between walking around obstacles or jumping through them. The proposed framework is experimentally deployed and validated on a quadrupedal robot, a Mini Cheetah, to enable the robot to autonomously navigate through an environment while avoiding obstacles and jumping over a maximum height of 13 cm to pass through a window-shaped opening in order to reach its goal.

【16】 Overcoming Obstructions via Bandwidth-Limited Multi-Agent Spatial Handshaking 标题：通过带宽受限的多Agent空间握手克服障碍

作者：Nathaniel Glaser,Yen-Cheng Liu,Junjiao Tian,Zsolt Kira 机构：Georgia Institute of Technology 备注：Accepted to IROS 2021 链接：https://arxiv.org/abs/2107.00771 摘要：在本文中，我们解决带宽有限和障碍倾向的协作感知，特别是在多智能体语义分割的背景下。这种设置提出了几个关键的挑战，包括处理和交换未注册的机器人群图像。为了取得成功，解决方案必须有效地利用多个非静态和间歇性重叠的RGB透视图，同时注意带宽限制和克服不必要的前景障碍。因此，我们提出了一种端到端可学习的多智能体空间握手网络（MASH）来处理、压缩和传播机器人群体中的视觉信息。我们的分布式通信模块直接（并且独占地）对原始图像数据进行操作，而不需要额外的输入要求，例如姿势、深度或扭曲数据。在一个真实的多机器人AirSim环境中，特别是在存在图像遮挡的情况下，与多个基线相比，我们的模型表现出了优越的性能。与强基线相比，我们的方法实现了11%的IoU绝对改善。 摘要：In this paper, we address bandwidth-limited and obstruction-prone collaborative perception, specifically in the context of multi-agent semantic segmentation. This setting presents several key challenges, including processing and exchanging unregistered robotic swarm imagery. To be successful, solutions must effectively leverage multiple non-static and intermittently-overlapping RGB perspectives, while heeding bandwidth constraints and overcoming unwanted foreground obstructions. As such, we propose an end-to-end learn-able Multi-Agent Spatial Handshaking network (MASH) to process, compress, and propagate visual information across a robotic swarm. Our distributed communication module operates directly (and exclusively) on raw image data, without additional input requirements such as pose, depth, or warping data. We demonstrate superior performance of our model compared against several baselines in a photo-realistic multi-robot AirSim environment, especially in the presence of image occlusions. Our method achieves an absolute 11% IoU improvement over strong baselines.

【17】 Enhancing Multi-Robot Perception via Learned Data Association 标题：基于学习数据关联的多机器人感知增强

作者：Nathaniel Glaser,Yen-Cheng Liu,Junjiao Tian,Zsolt Kira 机构：Georgia Institute of Technology 备注：Accepted to ICRA 2020 Workshop on "Emerging Learning and Algorithmic Methods for Data Association in Robotics"; associated spotlight talk available at this https URL&t=16743s 链接：https://arxiv.org/abs/2107.00769 摘要：在本文中，我们讨论了多机器人协作感知问题，特别是在分布式语义分割的多视图填充的背景下。这种设置带来了一些现实世界的挑战，特别是那些与未注册的多代理图像数据有关的挑战。解决方案必须有效地利用多个、非静态和间歇性重叠的RGB透视图。为此，我们提出了多智能体填充网络：一个可扩展的神经结构，可以部署（以分布式方式）到机器人群中的每个智能体。具体来说，每个机器人负责本地编码和解码视觉信息，可扩展的神经机制允许中间特征的不确定性感知和基于上下文的交换。我们在一个真实的多机器人AirSim数据集上展示了改进的性能。 摘要：In this paper, we address the multi-robot collaborative perception problem, specifically in the context of multi-view infilling for distributed semantic segmentation. This setting entails several real-world challenges, especially those relating to unregistered multi-agent image data. Solutions must effectively leverage multiple, non-static, and intermittently-overlapping RGB perspectives. To this end, we propose the Multi-Agent Infilling Network: an extensible neural architecture that can be deployed (in a distributed manner) to each agent in a robotic swarm. Specifically, each robot is in charge of locally encoding and decoding visual information, and an extensible neural mechanism allows for an uncertainty-aware and context-based exchange of intermediate features. We demonstrate improved performance on a realistic multi-robot AirSim dataset.

【18】 Design Optimization of Monoblade Autorotating Pods To Exhibit an Unconventional Descent Technique Using Glauert's Modelling 标题：利用Glauert模型对单叶自转吊舱的设计进行优化以展示一种非传统的下降技术

作者：Kanishk,Shashwat Patnaik 机构：Delhi Technological University, New Delhi, India 链接：https://arxiv.org/abs/2107.00738 摘要：许多非传统的下降机制在自然界进化，以最大限度地分散种子，以增加花卉物种的数量。诱导自转通过不对称的重量分布产生升力，增加了降落持续时间，给种子额外的时间被风吹走。所提出的生物灵感概念被用于生产新型现代吊舱，用于各种航空航天应用，这些应用要求在行星或行星际飞行任务中自由降落或控制速度下降，而不依赖传统技术，如基于推进的下降和使用降落伞。我们提供了一个单叶自动旋转机翼的设计过程和功能的解释。基于Glauert的叶片单元动量理论（BEMT）模型，采用基于单元的计算方法，通过MATLAB的优化工具箱和序列二次规划（SQP）求解器，通过最大化功率系数来估计叶片几何尺寸。利用MATLAB-Simulink六自由度工具箱建立了单翼设计的动力学模型，对单翼进行了自由飞行仿真，验证了其全局稳定性。 摘要：Many unconventional descent mechanisms are evolved in nature to maximize the dispersion of seeds to increase the population of floral species. The induced autorotation produces lift through asymmetrical weight distribution, increasing the fall duration and giving the seed extra time to get drifted away by the wind. The proposed bio-inspired concept was used to produce novel modern pods for various aerospace applications that require free-falling or controlled velocity descent in planetary or interplanetary missions without relying on traditional techniques such as propulsion-based descent and the use of parachutes. We provide an explanation for the design procedure and the functioning of a mono blade auto-rotating wing. An element-based computational method based on Glauert's blade element momentum theory (BEMT) model was employed to estimate the geometry by maximizing the coefficient of power through MATLAB's optimization toolbox using the Sequential quadratic programming (SQP) solver. The dynamic model was developed for the single-wing design through the MATLAB Simulink 6-DOF toolbox to carry out a free-flight simulation of the wing to verify its global stability.

【19】 Neural Task Success Classifiers for Robotic Manipulation from Few Real Demonstrations 标题：基于少数真实示例的机器人操作神经任务成功分类器

作者：Abdalkarim Mohtasib,Amir Ghalamzan E.,Nicola Bellotto,Heriberto Cuayáhuitl 机构：School of Computer Science, University of Lincoln, Lincoln, UK, Lincoln Institute for Agri-Food, Technology, Heriberto Cuay´ahuitl 备注：8 pages 链接：https://arxiv.org/abs/2107.00722 摘要：在不同的工作环境中，越来越多的机器人需要从少量的演示中学习新的操作任务。一个评估动作质量的分类器模型可以预测一个任务的成功完成，智能代理可以利用它进行动作选择。本文提出了一种新的分类器，它只需通过少量的实例就可以对任务完成情况进行分类。我们对不同的神经分类器进行了综合比较，如全连通分类、全卷积分类、序列分类和域自适应分类。我们还提出了一个新的数据集，包括五个机器人操作任务，这是公开的。我们使用我们的数据集和MIME数据集比较了我们的新分类器和现有模型的性能。研究结果表明，领域自适应和基于时间的特征可以提高成功预测。我们的新模型，即具有域自适应和时序特征的全卷积神经网络，在两个数据集中的任务中的平均分类准确率分别为97.3%和95.5%，而没有域自适应和时序特征的最新分类器仅分别达到82.4%和90.3%。 摘要：Robots learning a new manipulation task from a small amount of demonstrations are increasingly demanded in different workspaces. A classifier model assessing the quality of actions can predict the successful completion of a task, which can be used by intelligent agents for action-selection. This paper presents a novel classifier that learns to classify task completion only from a few demonstrations. We carry out a comprehensive comparison of different neural classifiers, e.g. fully connected-based, fully convolutional-based, sequence2sequence-based, and domain adaptation-based classification. We also present a new dataset including five robot manipulation tasks, which is publicly available. We compared the performances of our novel classifier and the existing models using our dataset and the MIME dataset. The results suggest domain adaptation and timing-based features improve success prediction. Our novel model, i.e. fully convolutional neural network with domain adaptation and timing features, achieves an average classification accuracy of 97.3\% and 95.5\% across tasks in both datasets whereas state-of-the-art classifiers without domain adaptation and timing-features only achieve 82.4\% and 90.3\%, respectively.

【20】 Deep Semantic Segmentation at the Edge for Autonomous Navigation in Vineyard Rows 标题：葡萄园行自主导航的边缘深层语义分割

作者：Diego Aghi,Simone Cerrato,Vittorio Mazzia,Marcello Chiaberge 备注：IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021) 链接：https://arxiv.org/abs/2107.00700 摘要：精准农业是一个快速发展的领域，其目标是在农业生产过程中引入经济有效的自动化。如今，葡萄园导航的算法解决方案需要昂贵的传感器和高计算工作量，这使得自主机器人平台在实际商业案例场景中无法大规模应用。从这个角度来看，我们提出的新控制利用机器感知和边缘人工智能技术的最新进展，以较低的计算和功耗在葡萄园行内实现高度经济和可靠的导航。事实上，使用一个定制的分割网络和一个低范围的RGB-D摄像机，我们能够利用环境的语义信息在不同的葡萄园场景中产生平滑的轨迹和稳定的控制。此外，由控制算法本身生成的分割图可以直接用作作物营养状态评估的过滤器。对真实数据和模拟环境的大量实验和评估表明了我们方法的有效性和内在的鲁棒性。 摘要：Precision agriculture is a fast-growing field that aims at introducing affordable and effective automation into agricultural processes. Nowadays, algorithmic solutions for navigation in vineyards require expensive sensors and high computational workloads that preclude large-scale applicability of autonomous robotic platforms in real business case scenarios. From this perspective, our novel proposed control leverages the latest advancement in machine perception and edge AI techniques to achieve highly affordable and reliable navigation inside vineyard rows with low computational and power consumption. Indeed, using a custom-trained segmentation network and a low-range RGB-D camera, we are able to take advantage of the semantic information of the environment to produce smooth trajectories and stable control in different vineyards scenarios. Moreover, the segmentation maps generated by the control algorithm itself could be directly exploited as filters for a vegetative assessment of the crop status. Extensive experimentations and evaluations against real-world data and simulated environments demonstrated the effectiveness and intrinsic robustness of our methodology.

【21】 Trust, Shared Understanding and Locus of Control in Mixed-Initiative Robotic Systems 标题：混合主动式机器人系统中的信任、共享理解和控制点

作者：Manolis Chiou,Faye McCabe,Markella Grigoriou,Rustam Stolkin 机构： University of Birmingham 备注：Pre-print of the accepted paper to appear in IEEE RO-MAN 2021 链接：https://arxiv.org/abs/2107.00690 摘要：本文研究了在混合主动机器人系统中，人与机器人之间的信任、共享理解以及控制点（LoC）人格特质对人与机器人交互（HRI）的影响。随着这类系统变得更加先进，能够与人类操作员并肩行动，机器人从被视为工具转变为团队伙伴。因此，本文研究的团队导向的人因（即信任、共同理解和LoC）对有效的人力资源创新起着至关重要的作用。在这里，我们给出了一个实验的结果，这个实验的灵感来自于一个灾难响应场景，在这个场景中，操作者通过人类主动或混合主动控制，在两个不同的自主水平之间动态切换：遥操作和自主导航。有证据表明，操作员信任并发展了对机器人系统的理解，特别是在混合主动控制中，随着操作员对系统的熟悉程度和执行任务的能力的提高，信任和理解随着时间的推移而增加。最后，就LoC如何影响HRI提出了证据和见解。 摘要：This paper investigates how trust, shared understanding between a human operator and a robot, and the Locus of Control (LoC) personality trait, evolve and affect Human-Robot Interaction (HRI) in mixed-initiative robotic systems. As such systems become more advanced and able to instigate actions alongside human operators, there is a shift from robots being perceived as a tool to being a team-mate. Hence, the team-oriented human factors investigated in this paper (i.e. trust, shared understanding, and LoC) can play a crucial role in efficient HRI. Here, we present the results from an experiment inspired by a disaster response scenario in which operators remotely controlled a mobile robot in navigation tasks, with either human-initiative or mixed-initiative control, switching dynamically between two different levels of autonomy: teleoperation and autonomous navigation. Evidence suggests that operators trusted and developed an understanding of the robotic systems, especially in mixed-initiative control, where trust and understanding increased over time, as operators became more familiar with the system and more capable of performing the task. Lastly, evidence and insights are presented on how LoC affects HRI.

【22】 Aerial Map-Based Navigation Using Semantic Segmentation and Pattern Matching 标题：基于语义分割和模式匹配的航空地图导航

作者：Youngjoo Kim 备注：6 pages, 4 figures 链接：https://arxiv.org/abs/2107.00689 摘要：提出了一种无人机地图导航系统的新方法。提出的系统尝试标签到标签的匹配，而不是航空图像和地图数据库之间的图像到图像的匹配。通过语义分割，对地物进行标注，并根据地物的形态在地图数据库中找到相应的位置。利用深度学习技术提取高层特征，将基于图像的定位问题转化为模式匹配问题。提出了一种不需要高度信息和摄像机模型的模式匹配算法来估计绝对水平位置。通过对仿真图像的可行性分析表明，所提出的模式匹配算法能够实现基于地图的导航，并且能够提供给定目标的位置。 摘要：This paper proposes a novel approach to map-based navigation system for unmanned aircraft. The proposed system attempts label-to-label matching, not image-to-image matching between aerial images and a map database. By using semantic segmentation, the ground objects are labelled and the configuration of the objects is used to find the corresponding location in the map database. The use of the deep learning technique as a tool for extracting high-level features reduces the image-based localization problem to a pattern matching problem. This paper proposes a pattern matching algorithm which does not require altitude information or a camera model to estimate the absolute horizontal position. The feasibility analysis with simulated images shows the proposed map-based navigation can be realized with the proposed pattern matching algorithm and it is able to provide positions given the labelled objects.

【23】 Active Learning of Abstract Plan Feasibility 标题：抽象方案可行性的主动学习

作者：Michael Noseworthy,Caris Moses,Isaiah Brand,Sebastian Castro,Leslie Kaelbling,Tomás Lozano-Pérez,Nicholas Roy 机构：MIT, CSAIL 备注：To appear in Robotics: Science and Systems 2021 链接：https://arxiv.org/abs/2107.00683 摘要：长视距序列操作任务被有效地分层处理：在高层次的抽象中，规划者搜索抽象的动作序列，当找到一个计划时，生成低层次的运动计划。这种策略依赖于可靠地预测一个满足抽象计划的低层可行计划的能力。然而，计算抽象计划可行性（APF）是困难的，因为计划的结果依赖于难以建模的现实世界现象，例如估计和执行中的噪声。在这项工作中，我们提出了一种主动学习的方法，有效地获得一个APF预测通过独立的任务，好奇的机器人探索。机器人识别计划，其结果将是APF的信息，执行这些计划，并从他们的成功或失败中学习。关键的是，我们利用一个不可行的子序列属性来删减主动学习策略中的候选计划，使我们的系统能够从较少的数据中学习。我们评估了我们的策略在仿真和一个真正的Franka-Emika熊猫机器人集成感知，实验，规划和执行。在物体质量分布不均匀的堆积域中，我们证明了我们的系统允许机器人在400个自监督交互中学习APF模型，并且我们的学习模型可以有效地用于多个下游任务。 摘要：Long horizon sequential manipulation tasks are effectively addressed hierarchically: at a high level of abstraction the planner searches over abstract action sequences, and when a plan is found, lower level motion plans are generated. Such a strategy hinges on the ability to reliably predict that a feasible low level plan will be found which satisfies the abstract plan. However, computing Abstract Plan Feasibility (APF) is difficult because the outcome of a plan depends on real-world phenomena that are difficult to model, such as noise in estimation and execution. In this work, we present an active learning approach to efficiently acquire an APF predictor through task-independent, curious exploration on a robot. The robot identifies plans whose outcomes would be informative about APF, executes those plans, and learns from their successes or failures. Critically, we leverage an infeasible subsequence property to prune candidate plans in the active learning strategy, allowing our system to learn from less data. We evaluate our strategy in simulation and on a real Franka Emika Panda robot with integrated perception, experimentation, planning, and execution. In a stacking domain where objects have non-uniform mass distributions, we show that our system permits real robot learning of an APF model in four hundred self-supervised interactions, and that our learned model can be used effectively in multiple downstream tasks.