机器人相关学术速递[8.17]

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cs.RO机器人相关，共计24篇

【1】 The Integrated Probabilistic Data Association Filter Adapted to Lie Groups 标题：适应李群的综合概率数据协会过滤 链接：https://arxiv.org/abs/2108.07265

作者：Mark E. Petersen,Randal W. Beard 机构： The authors are with the Electrical and Computer Engineering Departmentat Brigham Young University 备注：15 pages, 7 figures, will be submitted 摘要：综合概率数据关联滤波器是一种基于概率数据关联滤波器（PDAF）的目标跟踪算法，该算法计算统计度量值，该统计度量值指示是否应拒绝或确认轨迹以表示目标。本文的主要贡献是使IPDA滤波器适应在连接的单模李群上发展的目标模型，其中测量模型也涉及一个李群。本文从较高的层次介绍了李群，然后展示了该理论在利用摄像机信息从高空无人机跟踪汽车方面的应用。 摘要：The Integrated Probabilistic Data Association Filter is a target tracking algorithm based on the Probabilistic Data Association Filter (PDAF) that calculates a statistical measure that indicates if a track should be rejected or confirmed to represent a target. The main contribution of this paper is to adapt the IPDA filter to target models that evolve on connected unimodular Lie groups, and where the measurements models also involve a Lie group. The paper contains a high level introduction to Lie groups, and then shows applications of the theory to tracking a car from an overhead UAV using camera information.

【2】 APReL: A Library for Active Preference-based Reward Learning Algorithms 标题：APREL：一个基于主动偏好的奖励学习算法库 链接：https://arxiv.org/abs/2108.07259

作者：Erdem Bıyı k,Aditi Talati,Dorsa Sadigh 机构： Department of Electrical Engineering, Stanford University, Department of Computer Science, Stanford University 备注：5 pages, 1 figures. Library is available at: this https URL 摘要：奖励学习是机器人学中的一个基本问题，即让机器人按照人类用户的需求进行操作。许多基于偏好的学习算法和主动查询技术已经被提出来解决这个问题。在本文中，我们介绍了APReL，一个基于主动偏好的奖励学习算法库，它使研究人员和实践者能够使用现有技术进行实验，并轻松地为问题的各个模块开发自己的算法。 摘要：Reward learning is a fundamental problem in robotics to have robots that operate in alignment with what their human user wants. Many preference-based learning algorithms and active querying techniques have been proposed as a solution to this problem. In this paper, we present APReL, a library for active preference-based reward learning algorithms, which enable researchers and practitioners to experiment with the existing techniques and easily develop their own algorithms for various modules of the problem.

【3】 Proximity Perception in Human-Centered Robotics: A Survey on Sensing Systems and Applications 标题：以人为中心的机器人近距离感知：传感系统及其应用综述 链接：https://arxiv.org/abs/2108.07206

作者：Stefan Escaida Navarro,Stephan Mühlbacher-Karrer,Hosam Alagi,Hubert Zangl,Keisuke Koyama,Björn Hein,Christian Duriez,Joshua R. Smith 机构： Institute of SmartSystem Technologies 摘要：近距离感知是一种有潜力在未来机器人技术中发挥重要作用的技术。它可以实现工业和日常生活中安全、健壮和自治系统的承诺，与人类并驾齐驱，以及在太空和水下的偏远地区。在这篇综述文章中，我们介绍了从早期到现在这一领域的发展，重点是以人为中心的机器人。这里，接近传感器通常部署在两种情况下：第一，在机械臂的外部，以支持安全和交互功能，第二，在手爪或手的内侧，用于支持抓取和探索。从这一观察出发，我们对文献中发现的方法进行了分类。为了为理解这些方法提供基础，我们致力于介绍多年来开发的技术和不同的测量原理，并以表格的形式提供总结。然后，我们展示了文献中所展示的应用的多样性。最后，我们概述了影响该领域未来发展的最重要趋势。 摘要：Proximity perception is a technology that has the potential to play an essential role in the future of robotics. It can fulfill the promise of safe, robust, and autonomous systems in industry and everyday life, alongside humans, as well as in remote locations in space and underwater. In this survey paper, we cover the developments of this field from the early days up to the present, \textcolor{Reviewer4}{with a focus on human-centered robotics.} Here, proximity sensors are typically deployed in two scenarios: first, on the exterior of manipulator arms to support safety and interaction functionality, and second, on the inside of grippers or hands to support grasping and exploration. Starting from this observation, we propose a categorization for the approaches found in the literature. To provide a basis for understanding these approaches, we devote effort to present the technologies and different measuring principles that were developed over the years, also providing a summary in form of a table. Then, we show the diversity of applications that have been presented in the literature. Finally, we give an overview of the most important trends that will shape the future of this domain.

【4】 Learning Friction Model for Tethered Capsule Robot 标题：系泊胶囊机器人的摩擦学习模型 链接：https://arxiv.org/abs/2108.07151

作者：Yi Wang,Yuchen He,Xutian Deng,Ziwei Lei,Yiting Chen,Miao Li 机构：School of Power and Mechanical, Wuhan University, Wuhan, China, School of Mechanical, Engineering and Automation, Wuhan Textile University 备注：ICRAE 2021 Conference paper 摘要：随着胶囊机器人在医学内窥镜领域的潜在应用，胶囊机器人的精确动态控制变得越来越重要。在胶囊机器人的规模中，胶囊与环境之间的摩擦力在动力学模型中起着至关重要的作用，而动力学模型通常很难事先建模。本文建立了一个由机器人机械手驱动的系留式胶囊机器人系统，在机器人末端执行器上安装一个强磁Halbach阵列来调节胶囊的状态。为了提高控制精度，通过演示轨迹学习胶囊与环境之间的摩擦力。通过学习摩擦模型，实验结果表明，跟踪误差提高了5.6%。 摘要：With the potential applications of capsule robots in medical endoscopy, accurate dynamic control of the capsule robot is becoming more and more important. In the scale of a capsule robot, the friction between capsule and the environment plays an essential role in the dynamic model, which is usually difficult to model beforehand. In the paper, a tethered capsule robot system driven by a robot manipulator is built, where a strong magnetic Halbach array is mounted on the robot's end-effector to adjust the state of the capsule. To increase the control accuracy, the friction between capsule and the environment is learned with demonstrated trajectories. With the learned friction model, experimental results demonstrate an improvement of 5.6% in terms of tracking error.

【5】 Tracking Multiple Fast Targets With Swarms: Interplay Between Social Interaction and Agent Memory 标题：群体跟踪多个快速目标：社会交互与Agent记忆的相互作用 链接：https://arxiv.org/abs/2108.07122

作者：Hian Lee Kwa,Jabez Leong Kit,Roland Bouffanais 机构：Singapore University of Technology and Design, Singapore, Thales Solutions Asia, Singapore, University of Ottawa, Canada 备注：None 摘要：搜索和跟踪多个目标是一项具有挑战性的任务。然而，在这一领域的大多数作品没有考虑比包含多机器人系统的代理更快的规避目标。这是由于这样一种假设，即此类目标的运动模式，再加上其过快的速度，将使任务几乎无法完成。在这项工作中，我们证明了情况并非如此，我们提出了一种分散搜索和跟踪策略，其中swarm进行的探索和开发水平是可调的。通过调整群体的勘探和开发动态，我们证明了勘探和开发水平之间存在最佳平衡。该优化最大限度地提高了其跟踪性能，并根据目标数量和目标的运动轮廓进行变化。我们还表明，使用基于代理的内存对于跟踪规避目标至关重要。通过六个分散式机器人群跟踪虚拟快速运动目标的实验验证了所获得的仿真结果。 摘要：The task of searching for and tracking of multiple targets is a challenging one. However, most works in this area do not consider evasive targets that move faster than the agents comprising the multi-robot system. This is due to the assumption that the movement patterns of such targets, combined with their excessive speed, would make the task nearly impossible to accomplish. In this work, we show that this is not the case and we propose a decentralized search and tracking strategy in which the level of exploration and exploitation carried out by the swarm is adjustable. By tuning a swarm's exploration and exploitation dynamics, we demonstrate that there exists an optimal balance between the level of exploration and exploitation performed. This optimum maximizes its tracking performance and changes depending on the number of targets and the targets' movement profiles. We also show that the use of agent-based memory is critical in enabling the tracking of an evasive target. The obtained simulation results are validated through experimental tests with a decentralized swarm of six robots tracking a virtual fast-moving target.

【6】 Smart Pointers and Shared Memory Synchronisation for Efficient Inter-process Communication in ROS on an Autonomous Vehicle 标题：智能指针和共享内存同步，用于在自主车辆的ROS中进行有效的进程间通信 链接：https://arxiv.org/abs/2108.07085

作者：Costin Iordache,Stephen M. Fendyke,Mike J. Jones,Robert A. Buckley 备注：2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021). 8 pages, 8 figures 摘要：尽管实时系统要求严格，但机器人操作系统（ROS）对环回网络接口的依赖给高带宽数据的传输带来了相当大的开销，而nodelet包是进程内通信的有效机制，无法解决有效的本地进程间通信（IPC）问题。为了解决这个问题，我们提出了一种新的集成到ROS中的智能指针和存储在共享内存中的同步原语。这些遵循相同的语义，更重要的是，表现出与它们的C++标准库对应的性能，使得它们更适合于其他本地IPC机制。我们为我们的机制提出了一系列基准测试——我们称之为LOT（低开销传输）——并使用它们评估其在基于Five的自动车辆（AV）系统的真实数据负载上的性能，并将我们的分析扩展到多个ROS节点在Docker容器中运行的情况。我们发现，通过本地环回，我们的机制比标准IPC的性能高出两个数量级。最后，我们应用行业标准的评测技术来探索在用户和内核空间中运行的代码热点，并将我们的实现与其他实现进行比较。 摘要：Despite the stringent requirements of a real-time system, the reliance of the Robot Operating System (ROS) on the loopback network interface imposes a considerable overhead on the transport of high bandwidth data, while the nodelet package, which is an efficient mechanism for intra-process communication, does not address the problem of efficient local inter-process communication (IPC). To remedy this, we propose a novel integration into ROS of smart pointers and synchronisation primitives stored in shared memory. These obey the same semantics and, more importantly, exhibit the same performance as their C++ standard library counterparts, making them preferable to other local IPC mechanisms. We present a series of benchmarks for our mechanism - which we call LOT (Low Overhead Transport) - and use them to assess its performance on realistic data loads based on Five's Autonomous Vehicle (AV) system, and extend our analysis to the case where multiple ROS nodes are running in Docker containers. We find that our mechanism performs up to two orders of magnitude better than the standard IPC via local loopback. Finally, we apply industry-standard profiling techniques to explore the hotspots of code running in both user and kernel space, comparing our implementation against alternatives.

【7】 Flying Guide Dog: Walkable Path Discovery for the Visually Impaired Utilizing Drones and Transformer-based Semantic Segmentation 标题：飞行导盲犬：基于无人机和Transformer的视障人士可行走路径发现 链接：https://arxiv.org/abs/2108.07007

作者：Haobin Tan,Chang Chen,Xinyu Luo,Jiaming Zhang,Constantin Seibold,Kailun Yang,Rainer Stiefelhagen 机构：Walkable path: "slightly left", Semantic Segmentation 备注：Code, dataset, and video demo will be made publicly available at this https URL 摘要：由于缺乏有效感知周围环境的能力，盲人和视力受损者（BVIP）在户外行走时面临困难，尤其是在城市地区。因此，协助BVIP的工具非常重要。在本文中，我们提出了一个新的“飞行导盲犬”原型BVIP援助使用无人机和街景语义分割。基于从分割预测中提取的可行走区域，无人机可以自动调整其运动，从而引导用户沿着可行走路径行走。通过识别行人交通灯的颜色，我们的原型可以帮助用户安全地穿过街道。此外，我们引入了一个新的数据集，名为行人和车辆交通灯（PVTL），该数据集专门用于交通灯识别。我们在真实场景中的用户研究结果表明，我们的原型有效且易于使用，为BVIP援助提供了新的见解。 摘要：Lacking the ability to sense ambient environments effectively, blind and visually impaired people (BVIP) face difficulty in walking outdoors, especially in urban areas. Therefore, tools for assisting BVIP are of great importance. In this paper, we propose a novel "flying guide dog" prototype for BVIP assistance using drone and street view semantic segmentation. Based on the walkable areas extracted from the segmentation prediction, the drone can adjust its movement automatically and thus lead the user to walk along the walkable path. By recognizing the color of pedestrian traffic lights, our prototype can help the user to cross a street safely. Furthermore, we introduce a new dataset named Pedestrian and Vehicle Traffic Lights (PVTL), which is dedicated to traffic light recognition. The result of our user study in real-world scenarios shows that our prototype is effective and easy to use, providing new insight into BVIP assistance.

【8】 A Vision-based Irregular Obstacle Avoidance Framework via Deep Reinforcement Learning 标题：一种基于视觉的深度强化学习不规则避障框架 链接：https://arxiv.org/abs/2108.06887

作者：Lingping Gao,Jianchuan Ding,Wenxi Liu,Haiyin Piao,Yuxin Wang,Xin Yang,Baocai Yin 机构： Fuzhou University 摘要：深度强化学习在基于激光的避碰工作中取得了巨大的成功，因为激光可以在没有太多冗余数据的情况下感知准确的深度信息，这可以保持算法从模拟环境迁移到现实世界时的鲁棒性。然而，高成本的激光设备不仅难以大规模应用，而且对不规则物体（如桌子、椅子、架子等）的鲁棒性较差。本文提出了一种基于视觉的碰撞避免框架来解决这一难题。我们的方法试图估计深度并结合RGB数据中的语义信息来获得一种新的数据形式，即伪激光数据，它结合了视觉信息和激光信息的优点。与传统的激光数据只包含一定高度的一维距离信息相比，我们提出的伪激光数据编码了图像中的深度信息和语义信息，使得我们的方法对不规则障碍物更加有效。此外，由于估计的深度信息不准确，我们在训练阶段自适应地向激光数据添加噪声，以增强我们的模型在现实世界中的鲁棒性。实验结果表明，我们的框架在一些看不见的虚拟和现实场景中实现了最先进的性能。 摘要：Deep reinforcement learning has achieved great success in laser-based collision avoidance work because the laser can sense accurate depth information without too much redundant data, which can maintain the robustness of the algorithm when it is migrated from the simulation environment to the real world. However, high-cost laser devices are not only difficult to apply on a large scale but also have poor robustness to irregular objects, e.g., tables, chairs, shelves, etc. In this paper, we propose a vision-based collision avoidance framework to solve the challenging problem. Our method attempts to estimate the depth and incorporate the semantic information from RGB data to obtain a new form of data, pseudo-laser data, which combines the advantages of visual information and laser information. Compared to traditional laser data that only contains the one-dimensional distance information captured at a certain height, our proposed pseudo-laser data encodes the depth information and semantic information within the image, which makes our method more effective for irregular obstacles. Besides, we adaptively add noise to the laser data during the training stage to increase the robustness of our model in the real world, due to the estimated depth information is not accurate. Experimental results show that our framework achieves state-of-the-art performance in several unseen virtual and real-world scenarios.

【9】 Decentralized Multi-AGV Task Allocation based on Multi-Agent Reinforcement Learning with Information Potential Field Rewards 标题：基于信息势场奖励的多Agent强化学习分布式多AGV任务分配 链接：https://arxiv.org/abs/2108.06886

作者：Mengyuan Li,Bin Guo,Jiangshan Zhang,Jiaqi Liu,Sicong Liu,Zhiwen Yu,Zhetao Li,Liyao Xiang 机构：School of Computer Science, Northwestern Polytechnical University, Xi’an , China, College of Computer Science, Xiangtan University, Xiangtan , China, John Hopcroft Center for Computer Science, Shanghai Jiao Tong University, Shanghai , China 摘要：自动引导车辆（AGV）已广泛用于柔性车间的物料搬运。每个产品在生产过程中都需要各种原材料来完成装配。AGV用于在不同位置实现原材料的自动搬运。有效的AGVs任务分配策略可以降低运输成本，提高配送效率。然而，传统的集中式方法对控制中心的计算能力和实时性提出了很高的要求。在本文中，我们提出了分散的解决方案，以实现灵活和自组织的AGVs任务分配。特别地，我们提出了两种改进的多智能体强化学习算法MADDPG-IPF（Information Potential Field）和BiCNet IPF，以实现AGV之间适应不同场景的协调。为了解决奖励稀疏问题，我们提出了一种基于信息势场的奖励形成策略，该策略提供逐步奖励并隐式引导AGV到达不同的物质目标。我们在不同的设置（3个AGV和6个AGV）下进行了实验，实验结果表明，与基线方法相比，我们的工作获得了高达47%的任务响应改善和22%的训练迭代减少。 摘要：Automated Guided Vehicles (AGVs) have been widely used for material handling in flexible shop floors. Each product requires various raw materials to complete the assembly in production process. AGVs are used to realize the automatic handling of raw materials in different locations. Efficient AGVs task allocation strategy can reduce transportation costs and improve distribution efficiency. However, the traditional centralized approaches make high demands on the control center's computing power and real-time capability. In this paper, we present decentralized solutions to achieve flexible and self-organized AGVs task allocation. In particular, we propose two improved multi-agent reinforcement learning algorithms, MADDPG-IPF (Information Potential Field) and BiCNet-IPF, to realize the coordination among AGVs adapting to different scenarios. To address the reward-sparsity issue, we propose a reward shaping strategy based on information potential field, which provides stepwise rewards and implicitly guides the AGVs to different material targets. We conduct experiments under different settings (3 AGVs and 6 AGVs), and the experiment results indicate that, compared with baseline methods, our work obtains up to 47\% task response improvement and 22\% training iterations reduction.

【10】 The Marine Debris Dataset for Forward-Looking Sonar Semantic Segmentation 标题：面向前视声纳语义分割的海洋废弃物数据集 链接：https://arxiv.org/abs/2108.06800

作者：Deepak Singh,Matias Valdenegro-Toro 机构：Netaji Subhas Institute Of Technology, Dwarka Sec-, Delhi, India, German Research Center for Artificial Intelligence, Robert-Hooke-Str , Bremen, Germany 备注：OceanVision 2021 ICCV Worshop, Camera Ready, 9 pages, 13 figures, 6 Tables 摘要：准确检测和分割海洋废弃物对于保持水体清洁非常重要。本文提出了一种利用前视声纳（FLS）采集的海洋废弃物分割新数据集。该数据集由使用ARIS Explorer 3000传感器捕获的1868幅FLS图像组成。用于生成此数据集的对象包含典型的室内海洋废弃物和干扰物海洋对象（轮胎、挂钩、阀门等），分为11个类和一个背景类。在此数据集上分析了各种编码器的最新语义分割体系结构的性能，并将其作为基线结果呈现。由于图像是灰度的，因此没有使用预训练权重。使用联合上的交集（IoU）进行比较。性能最好的机型是Unet，其ResNet34主干网的容量为0.74.81兆。该数据集可在https://github.com/mvaldenegro/marine-debris-fls-datasets/ 摘要：Accurate detection and segmentation of marine debris is important for keeping the water bodies clean. This paper presents a novel dataset for marine debris segmentation collected using a Forward Looking Sonar (FLS). The dataset consists of 1868 FLS images captured using ARIS Explorer 3000 sensor. The objects used to produce this dataset contain typical house-hold marine debris and distractor marine objects (tires, hooks, valves,etc), divided in 11 classes plus a background class. Performance of state of the art semantic segmentation architectures with a variety of encoders have been analyzed on this dataset and presented as baseline results. Since the images are grayscale, no pretrained weights have been used. Comparisons are made using Intersection over Union (IoU). The best performing model is Unet with ResNet34 backbone at 0.7481 mIoU. The dataset is available at https://github.com/mvaldenegro/marine-debris-fls-datasets/

【11】 Augmenting GRIPS with Heuristic Sampling for Planning Feasible Trajectories of a Car-Like Robot 标题：用启发式采样增强夹点规划汽车机器人可行轨迹 链接：https://arxiv.org/abs/2108.06789

作者：Brian Angulo,Konstantin Yakovlev,Ivan Radionov 备注：6 pages, 6 figures 摘要：非全息移动机器人的动力学运动规划是一个具有挑战性的问题，目前还没有一个通用的解决方案。解决这一问题的一个有效的计算方法是首先构建一条几何路径，然后将该路径转换为运动学上可行的路径。梯度信息路径平滑（GRAPS）是最近引入的一种用于此类变换的方法。夹点迭代地使路径变形，并添加/删除航路点，同时尝试通过提供的符合运动学约束的转向功能连接每对连续的航路点。该算法相对较快，但不幸的是，不能保证它会成功。在实践中，它往往无法为具有大转弯半径的类车机器人生成可行的轨迹。在这项工作中，我们介绍了一系列的修改，旨在提高汽车机器人夹持的成功率。主要的改进是增加了额外的步骤，即沿几何路径的瓶颈部分（如急转弯）启发式采样航路点。实验评估的结果清楚地表明，与原始夹点相比，建议算法的成功率高达40%，命中率高达90%，而运行时间较低。 摘要：Kinodynamic motion planning for non-holomonic mobile robots is a challenging problem that is lacking a universal solution. One of the computationally efficient ways to solve it is to build a geometric path first and then transform this path into a kinematically feasible one. Gradient-informed Path Smoothing (GRIPS) is a recently introduced method for such transformation. GRIPS iteratively deforms the path and adds/deletes the waypoints while trying to connect each consecutive pair of them via the provided steering function that respects the kinematic constraints. The algorithm is relatively fast but, unfortunately, does not provide any guarantees that it will succeed. In practice, it often fails to produce feasible trajectories for car-like robots with large turning radius. In this work, we introduce a range of modifications that are aimed at increasing the success rate of GRIPS for car-like robots. The main enhancement is adding the additional step that heuristically samples waypoints along the bottleneck parts of the geometric paths (such as sharp turns). The results of the experimental evaluation provide a clear evidence that the success rate of the suggested algorithm is up to 40% higher compared to the original GRIPS and hits the bar of 90%, while its runtime is lower.

【12】 A Morphing Quadrotor that Can Optimize Morphology for Transportation 标题：一种可优化运输形态的变形四旋翼 链接：https://arxiv.org/abs/2108.06759

作者：Chanyoung Kim,Hyungyu Lee,Myeongwoo Jeong,Hyun Myung 机构： orequipment that should be mounted far from the center of 1Chanyoung Kim and Myeongwoo Jeong are with School of ElectricalEngineering, Korea Advanced Institute of Science and Technology (KAIST), Schoolof Electrical Engineering 备注：7 pages, Accepted at 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021) 摘要：根据有效载荷的类型，多旋翼可以有效地应用于各种任务，如运输、调查、勘探和救生。然而，由于多转子的性质，加载在多转子上的有效载荷在其位置和重量上受到限制，这在多转子用于各种领域时是一个主要缺点。在本文中，我们提出了一种新的方法，大大改善了限制有效载荷的位置和重量使用变形四旋翼系统。我们的方法可以实时估计无人机的重量、重心位置和惯性张量，它们随有效载荷的变化而变化，并确定高效稳定飞行的最佳形态。提出了一种通过有效载荷和变形来反映飞行动力学变化的自适应控制方法。实验证明，与传统四旋翼系统相比，所提出的变形四旋翼系统在各种有效载荷传输情况下提高了稳定性和效率。 摘要：Multirotors can be effectively applied to various tasks, such as transportation, investigation, exploration, and lifesaving, depending on the type of payload. However, due to the nature of multirotors, the payload loaded on the multirotor is limited in its position and weight, which presents a major disadvantage when the multirotor is used in various fields. In this paper, we propose a novel method that greatly improves the restrictions on payload position and weight using a morphing quadrotor system. Our method can estimate the drone's weight, center of gravity position, and inertia tensor in real-time, which change depending on payload, and determine the optimal morphology for efficient and stable flight. An adaptive control method that can reflect the change in flight dynamics by payload and morphing is also presented. Experiments were conducted to confirm that the proposed morphing quadrotor improves the stability and efficiency in various situations of transporting payloads compared with the conventional quadrotor systems.

【13】 Learning Dynamical System for Grasping Motion 标题：抓取动作的学习动力学系统 链接：https://arxiv.org/abs/2108.06728

作者：Xiao Gao,Miao Li,Xiaohui Xiao 机构：WuhanUniversity 备注：IEEE ICRA 2021 Workshop on Bridging the Gap between Data-driven and Analytical Physics-based Grasping and Manipulation II 摘要：动力系统具有对动态变化环境的固有适应性和对扰动的鲁棒性，已被广泛用于人类演示轨迹的编码。在本文中，我们提出了一个学习基于微分同胚的耦合位置和方向的动力系统的框架。与其他方法不同的是，它可以在整个弹道中实现位置和方向的同步。通过在线抓取实验验证了该方法的有效性和在线适应性。 摘要：Dynamical System has been widely used for encoding trajectories from human demonstration, which has the inherent adaptability to dynamically changing environments and robustness to perturbations. In this paper we propose a framework to learn a dynamical system that couples position and orientation based on a diffeomorphism. Different from other methods, it can realise the synchronization between positon and orientation during the whole trajectory. Online grasping experiments are carried out to prove its effectiveness and online adaptability.

【14】 Efficient Anytime CLF Reactive Planning System for a Bipedal Robot on Undulating Terrain 标题：起伏地形上两足机器人高效随时CLF反应规划系统 链接：https://arxiv.org/abs/2108.06699

作者：Jiunn-Kai Huang,Jessy W. Grizzle 机构：arewiththeRoboticsInstitute, UniversityofMichigan 摘要：我们提出并实验证明了一个反应式规划系统的两足机器人在未探索，具有挑战性的地形。该系统由一个低频规划线程（5Hz）和一个高频反应线程（300Hz）组成，前者用于寻找渐近最优路径，后者用于适应机器人偏差。规划线程包括：多层局部地图，用于计算机器人在地形上的可穿越性；与快速探索随机树星（RRT*）一起使用的随时全向控制Lyapunov函数（CLF），该随机树星生成用于指定节点之间运动的向量场；当最终目标在当前地图之外时，子目标查找器；和一个有限状态机来处理高级任务决策。该系统还包括一个反应线程，以避免传统RRT*算法在执行路径跟踪时出现的非平滑运动。反应线程处理机器人偏差，同时通过向量场（由闭环反馈策略定义）消除非平滑运动，该向量场为机器人的步态控制器提供实时控制命令，作为瞬时机器人姿势的函数。在Cassie Blue（一种具有20个自由度的两足机器人）上，该系统在各种具有挑战性的室外地形和杂乱的室内场景中进行了仿真和实验评估。所有实现都用机器人操作系统（ROS）在C++中编码，并可在https://github.com/UMich-BipedLab/CLF_reactive_planning_system. 摘要：We propose and experimentally demonstrate a reactive planning system for bipedal robots on unexplored, challenging terrains. The system consists of a low-frequency planning thread (5 Hz) to find an asymptotically optimal path and a high-frequency reactive thread (300 Hz) to accommodate robot deviation. The planning thread includes: a multi-layer local map to compute traversability for the robot on the terrain; an anytime omnidirectional Control Lyapunov Function (CLF) for use with a Rapidly Exploring Random Tree Star (RRT*) that generates a vector field for specifying motion between nodes; a sub-goal finder when the final goal is outside of the current map; and a finite-state machine to handle high-level mission decisions. The system also includes a reactive thread to obviate the non-smooth motions that arise with traditional RRT* algorithms when performing path following. The reactive thread copes with robot deviation while eliminating non-smooth motions via a vector field (defined by a closed-loop feedback policy) that provides real-time control commands to the robot's gait controller as a function of instantaneous robot pose. The system is evaluated on various challenging outdoor terrains and cluttered indoor scenes in both simulation and experiment on Cassie Blue, a bipedal robot with 20 degrees of freedom. All implementations are coded in C++ with the Robot Operating System (ROS) and are available at https://github.com/UMich-BipedLab/CLF_reactive_planning_system.

【15】 Force-feedback based Whole-body Stabilizer for Position-Controlled Humanoid Robots 标题：基于力反馈的位置控制仿人机器人全身稳定器 链接：https://arxiv.org/abs/2108.06652

作者：Shunpeng Yang,Hua Chen,Zhen Fu,Wei Zhang 机构： mainly due to theThe authors are with the Department of Mechanical and Energy Engi-neering, Southern University of Science and Technology 备注：IROS 2021, 8 pages 摘要：本文研究了位置控制仿人机器人的稳定器设计。稳定器是位置控制类人机器人的重要组成部分，其主要目标是调整发送给机器人的控制输入，以帮助跟踪控制器更好地跟踪计划的参考轨迹。为了实现这一目标，本文开发了一种新型的基于力反馈的全身稳定器，该稳定器充分利用了六维力测量信息和全身动力学来提高跟踪性能。基于对未知接触下位置控制类人机器人的全身动力学的严格分析，所开发的稳定器利用基于二次规划的技术，允许同时考虑质心跟踪和接触力跟踪。该稳定器的有效性在UBTECH Walker机器人的MuJoCo模拟器上得到了验证。仿真验证表明，与基于零力矩点反馈和线性倒立摆模型的常用稳定器相比，该稳定器在各种情况下都有显著的改进。 摘要：This paper studies stabilizer design for position-controlled humanoid robots. Stabilizers are an essential part for position-controlled humanoids, whose primary objective is to adjust the control input sent to the robot to assist the tracking controller to better follow the planned reference trajectory. To achieve this goal, this paper develops a novel force-feedback based whole-body stabilizer that fully exploits the six-dimensional force measurement information and the whole-body dynamics to improve tracking performance. Relying on rigorous analysis of whole-body dynamics of position-controlled humanoids under unknown contact, the developed stabilizer leverages quadratic-programming based technique that allows cooperative consideration of both the center-of-mass tracking and contact force tracking. The effectiveness of the proposed stabilizer is demonstrated on the UBTECH Walker robot in the MuJoCo simulator. Simulation validations show a significant improvement in various scenarios as compared to commonly adopted stabilizers based on the zero-moment-point feedback and the linear inverted pendulum model.

【16】 Adaptive Selection of Informative Path Planning Strategies via Reinforcement Learning 标题：基于强化学习的信息路径规划策略自适应选择 链接：https://arxiv.org/abs/2108.06618

作者：Taeyeong Choi,Grzegorz Cielniak 备注：Published in the proceedings of ECMR 2021 摘要：在我们之前的工作中，我们设计了一个系统策略，通过使用高斯过程回归（GPR）的预测不确定性作为路径规划中部署机器人的“吸引力”，对采样位置进行优先排序，从而显著提高空间插值的精度。尽管与旅行商问题（TSP）解算器的集成也显示出相对较短的旅行距离，但我们在此假设了几个可能降低整体预测精度的因素，因为次优位置最终可能包含在其路径中。为了解决这个问题，在本文中，我们首先探讨了采用不同空间范围的“局部规划”方法，在这些空间范围内，下一个采样位置被优先排序，以调查它们对预测性能以及产生的旅行距离的影响。此外，训练基于强化学习（RL）的高级控制器，从一组特定的本地计划员自适应生成混合计划，以根据最新的预测状态从选择中继承独特的优势。我们在温度监测机器人用例上的实验表明，动态混合的计划者不仅可以生成复杂的，信息型计划，单个计划员无法单独创建，但也可以确保显著缩短行程距离，而无需任何最短路径计算模块的辅助，且不以预测可靠性为代价。 摘要：In our previous work, we designed a systematic policy to prioritize sampling locations to lead significant accuracy improvement in spatial interpolation by using the prediction uncertainty of Gaussian Process Regression (GPR) as "attraction force" to deployed robots in path planning. Although the integration with Traveling Salesman Problem (TSP) solvers was also shown to produce relatively short travel distance, we here hypothesise several factors that could decrease the overall prediction precision as well because sub-optimal locations may eventually be included in their paths. To address this issue, in this paper, we first explore "local planning" approaches adopting various spatial ranges within which next sampling locations are prioritized to investigate their effects on the prediction performance as well as incurred travel distance. Also, Reinforcement Learning (RL)-based high-level controllers are trained to adaptively produce blended plans from a particular set of local planners to inherit unique strengths from that selection depending on latest prediction states. Our experiments on use cases of temperature monitoring robots demonstrate that the dynamic mixtures of planners can not only generate sophisticated, informative plans that a single planner could not create alone but also ensure significantly reduced travel distances at no cost of prediction reliability without any assist of additional modules for shortest path calculation.

【17】 Monocular visual autonomous landing system for quadcopter drones using software in the loop 标题：基于软件在环的四旋翼无人机单目视觉自主着陆系统 链接：https://arxiv.org/abs/2108.06616

作者：Miguel Saavedra-Ruiz,Ana Mario Pinto-Vargas,Victor Romero-Cano 机构：∗Mila - Quebec Institute of Artificial Intelligence, Universit´e de Montr´eal, Canada, † Alternova Tech SAS, Medell´ın, Colombia, ‡Robotics and Autonomous Systems Laboratory, Universidad Aut´onoma de Occidente, Cali, Colombia 备注：IEEE aerospace and electronic systems 摘要：自主着陆是实现多旋翼无人机在许多社会和工业应用中的全部潜力所必需的能力。在物理平台上实施和测试这种能力是有风险的，而且需要大量资源；因此，为了确保良好的设计过程和安全部署，在实现物理原型之前需要进行模拟。本文介绍了一种单目视觉系统的开发，该系统采用软件在环方法，能够自动、高效地将四旋翼无人机降落在预定的着陆平台上，从而降低物理测试阶段的风险。除了使用基于露台的模拟确保自主着陆系统作为一个整体满足设计要求外，我们的方法还提供了在物理实施之前进行安全参数调整和设计测试的工具。最后，所提出的单目视觉着陆台跟踪方法使得在具有Odroid XU4嵌入式处理器标准计算能力的F450四旋翼无人机上有效实现该系统成为可能。 摘要：Autonomous landing is a capability that is essential to achieve the full potential of multi-rotor drones in many social and industrial applications. The implementation and testing of this capability on physical platforms is risky and resource-intensive; hence, in order to ensure both a sound design process and a safe deployment, simulations are required before implementing a physical prototype. This paper presents the development of a monocular visual system, using a software-in-the-loop methodology, that autonomously and efficiently lands a quadcopter drone on a predefined landing pad, thus reducing the risks of the physical testing stage. In addition to ensuring that the autonomous landing system as a whole fulfils the design requirements using a Gazebo-based simulation, our approach provides a tool for safe parameter tuning and design testing prior to physical implementation. Finally, the proposed monocular vision-only approach to landing pad tracking made it possible to effectively implement the system in an F450 quadcopter drone with the standard computational capabilities of an Odroid XU4 embedded processor.

【18】 Real-Time Multi-Modal Semantic Fusion on Unmanned Aerial Vehicles 标题：无人机上的实时多模态语义融合 链接：https://arxiv.org/abs/2108.06608

作者：Simon Bultmann,Jan Quenzel,Sven Behnke 机构：Accepted for: ,th European Conference on Mobile Robots (ECMR), Bonn, Germany 备注：Accepted for: 10th European Conference on Mobile Robots (ECMR), Bonn, Germany, September 2021 摘要：配备多个互补传感器的无人机（UAV）在快速自主或远程控制语义场景分析方面具有巨大潜力，例如用于灾难检测。在这项工作中，我们提出了一个无人机系统的实时语义推理和融合的多传感器模式。激光雷达扫描和RGB图像的语义分割，以及RGB和热图像上的目标检测，使用轻量级CNN架构和嵌入式推理加速器在无人机计算机上在线运行。我们采用了一种后期融合方法，其中来自多种模式的语义信息增强了3D点云和图像分割遮罩，同时还生成了一个异中心语义图。我们的系统提供了增强的语义图像和点云，分别为$\约\、$9$\、$Hz。我们在城市环境的真实实验中评估了集成系统。 摘要：Unmanned aerial vehicles (UAVs) equipped with multiple complementary sensors have tremendous potential for fast autonomous or remote-controlled semantic scene analysis, e.g., for disaster examination. In this work, we propose a UAV system for real-time semantic inference and fusion of multiple sensor modalities. Semantic segmentation of LiDAR scans and RGB images, as well as object detection on RGB and thermal images, run online onboard the UAV computer using lightweight CNN architectures and embedded inference accelerators. We follow a late fusion approach where semantic information from multiple modalities augments 3D point clouds and image segmentation masks while also generating an allocentric semantic map. Our system provides augmented semantic images and point clouds with $\approx\,$9$\,$Hz. We evaluate the integrated system in real-world experiments in an urban environment.

【19】 Distributed Control of Truss Robots Using Consensus Alternating Direction Method of Multipliers 标题：基于乘子一致交替方向法的桁架机器人分布式控制 链接：https://arxiv.org/abs/2108.06577

作者：Nathan S. Usevitch,Trevor Halsted,Zachary M. Hammond,Allison M. Okamura,Mac Schwager 机构： Okamura are withthe Department of Mechanical Engineering, Stanford University 备注：Submitted to IEEE T-RO 摘要：桁架机器人，或由连接在万向节上的可扩展连杆组成的机器人，通常采用模块化物理组件设计，但需要集中控制技术。提出了一种桁架机器人分布式控制技术。桁架机器人被视为一个整体，其中机器人的每个单独节点都能够测量相邻边的长度，与其他节点的子集通信，并计算和执行其连接边的自身控制动作。通过迭代分布式优化，个体成员利用局部信息收敛于机器人状态的全局估计，然后协调其计划运动以实现所需的全局行为。该分布式优化基于乘法器框架的一致交替方向法。然后将该分布式算法应用于等周桁架机器人的控制，并进行了实验验证。演示允许用户向机器人的单个节点广播命令，从而确保所有其他节点的协调运动，以实现所需的全局运动。 摘要：Truss robots, or robots that consist of extensible links connected at universal joints, are often designed with modular physical components but require centralized control techniques. This paper presents a distributed control technique for truss robots. The truss robot is viewed as a collective, where each individual node of the robot is capable of measuring the lengths of the neighboring edges, communicating with a subset of the other nodes, and computing and executing its own control actions with its connected edges. Through an iterative distributed optimization, the individual members utilize local information to converge on a global estimate of the robot's state, and then coordinate their planned motion to achieve desired global behavior. This distributed optimization is based on a consensus alternating direction method of multipliers framework. This distributed algorithm is then adapted to control an isoperimetric truss robot, and the distributed algorithm is used in an experimental demonstration. The demonstration allows a user to broadcast commands to a single node of the robot, which then ensures the coordinated motion of all other nodes to achieve the desired global motion.

【20】 Constrained Iterative LQG for Real-Time Chance-ConstrainedGaussian Belief Space Planning 标题：实时机会约束高斯信念空间规划的约束迭代LQG算法 链接：https://arxiv.org/abs/2108.06533

作者：Jianyu Chen,Yutaka Shimizu,Liting Sun,Masayoshi Tomizuka,Wei Zhan 机构： Shimizu is with Graduate School of Information Science andTechnology, University of Tokyo 备注：IROS 2021 摘要：不确定性条件下的运动规划对于自主车辆等安全关键系统具有重要意义。此类系统必须满足必要的约束条件（例如，避免碰撞），潜在的不确定性来自受干扰的系统动力学或噪声传感器测量。然而，现有的运动规划方法在一般的非线性和非凸环境下不能有效地找到鲁棒最优解。在本文中，我们提出了机会约束高斯信念空间规划问题，并提出了约束迭代线性二次高斯（CILQG）算法作为实时解决方案。在该算法中，我们迭代计算信念的高斯近似，并转换机会约束。我们评估了我们的方法在模拟静态和动态障碍物的自主驾驶规划任务中的有效性。结果表明，与基线方法相比，CILQG能更恰当地处理不确定性，且计算速度更快。 摘要：Motion planning under uncertainty is of significant importance for safety-critical systems such as autonomous vehicles. Such systems have to satisfy necessary constraints (e.g., collision avoidance) with potential uncertainties coming from either disturbed system dynamics or noisy sensor measurements. However, existing motion planning methods cannot efficiently find the robust optimal solutions under general nonlinear and non-convex settings. In this paper, we formulate such problem as chance-constrained Gaussian belief space planning and propose the constrained iterative Linear Quadratic Gaussian (CILQG) algorithm as a real-time solution. In this algorithm, we iteratively calculate a Gaussian approximation of the belief and transform the chance-constraints. We evaluate the effectiveness of our method in simulations of autonomous driving planning tasks with static and dynamic obstacles. Results show that CILQG can handle uncertainties more appropriately and has faster computation time than baseline methods.

【21】 Data Generation for Learning to Grasp in a Bin-picking Scenario 标题：拣箱场景中学习抓取的数据生成 链接：https://arxiv.org/abs/2108.06532

作者：Yiting Chen,Miao Li 机构：School of Power and Mechanical Engineering, Wuhan University, China 备注：3 pages, workshop 摘要：深度学习的兴起极大地改变了机器人抓取的方式，从基于模型的方式转变为数据驱动的方式。沿着这条路线，从模拟或真实世界示例中收集的大量数据变得极其重要。在本文中，我们介绍了我们最近在模拟垃圾箱拾取场景中生成数据的工作。YCB对象数据集中的77个对象用于使用PyBullet生成数据集，其中考虑了不同的环境条件，包括照明、相机姿势、传感器噪声等。总共收集了100K数据样本，包括地面真值分割、RGB、6D姿势和点云。包括源代码在内的所有数据示例都可以在线获取。 摘要：The rise of deep learning has greatly transformed the pipeline of robotic grasping from model-based approach to data-driven stream. Along this line, a large scale of grasping data either collected from simulation or from real world examples become extremely important. In this paper, we present our recent work on data generation in simulation for a bin-picking scene. 77 objects from the YCB object data sets are used to generate the dataset with PyBullet, where different environment conditions are taken into account including lighting, camera pose, sensor noise and so on. In all, 100K data samples are collected in terms of ground truth segmentation, RGB, 6D pose and point cloud. All the data examples including the source code are made available online.

【22】 Sharing Cognition: Human Gesture and Natural Language Grounding Based Planning and Navigation for Indoor Robots 标题：共享认知：基于人体手势和自然语言基础的室内机器人规划与导航 链接：https://arxiv.org/abs/2108.06478

作者：Gourav Kumar,Soumyadip Maity,Ruddra dev Roychoudhury,Brojeshwar Bhowmick 摘要：人与人之间的合作使得即使在未知场景中也能轻松执行任务和无缝导航。凭借我们的个人知识和集体认知技能，我们可以在不可预见的情况和环境中推理并表现出色。为了实现机器人在人类之间导航和与人类交互的类似潜力，它必须具备与人类进行简单、高效和自然的交流和认知共享的能力。在这项工作中，我们的目标是利用人类的手势，这是众所周知的最突出的沟通方式后的讲话。我们演示了如何使用具有视觉和听觉能力的机器人，以一种非常简单但有效的方式将手势与空间理解进行交流。这表明，在需要发展认知和室内导航的任务中，仅使用基于视觉和语言的导航、语言基础或人机交互具有很大的优势。我们采用最先进的语言基础和人机交互模块，在现实环境中演示了一种新的系统管道，该管道位于一个远程临场感机器人上，用于执行一系列具有挑战性的任务。据我们所知，这是第一条在室内环境中将HRI和语言基础领域结合起来演示自主导航的管道。 摘要：Cooperation among humans makes it easy to execute tasks and navigate seamlessly even in unknown scenarios. With our individual knowledge and collective cognition skills, we can reason about and perform well in unforeseen situations and environments. To achieve a similar potential for a robot navigating among humans and interacting with them, it is crucial for it to acquire the ability for easy, efficient and natural ways of communication and cognition sharing with humans. In this work, we aim to exploit human gestures which is known to be the most prominent modality of communication after the speech. We demonstrate how the incorporation of gestures for communicating spatial understanding can be achieved in a very simple yet effective way using a robot having the vision and listening capability. This shows a big advantage over using only Vision and Language-based Navigation, Language Grounding or Human-Robot Interaction in a task requiring the development of cognition and indoor navigation. We adapt the state-of-the-art modules of Language Grounding and Human-Robot Interaction to demonstrate a novel system pipeline in real-world environments on a Telepresence robot for performing a set of challenging tasks. To the best of our knowledge, this is the first pipeline to couple the fields of HRI and language grounding in an indoor environment to demonstrate autonomous navigation.

【23】 The Geometric Structure of Externally Actuated Planar Locomoting Systems in Ambient Media 标题：环境介质中外驱动平面运动系统的几何结构 链接：https://arxiv.org/abs/2108.06442

作者：Blake Buchanan,Tony Dear,Scott Kelly,Matthew Travers,Howie Choset 机构： USA‡Mechanical Engineering and Engineering ScienceUniversity of North Carolina at Charlotte, The Robotics Institute 摘要：机器人经常通过轮子、关节或附属物等附着部件与世界互动。在许多系统中，这些相互作用以及它们导致运动的方式可以使用几何力学的机械来理解，解释机器人形状空间中的输入如何影响其配置空间及其环境配置空间中的运动。在本文中，我们考虑一种相反类型的运动，其中机器人受到与外部受迫环境介质的相互作用的主动影响。我们研究了两个外部驱动系统的例子；一种是由主连接控制运动的，通常被认为不具有漂移动力学，另一种是不存在这种连接的，其运动固有漂移。对于无漂移系统，我们根据先前了解的系统内部驱动版本开发几何工具，并演示它们在外部驱动下的运动规划中的使用。对于具有漂移的系统，我们采用非完整约化来获得系统动力学的约化表示，说明有助于研究运动的几何特征，并导出外部驱动策略。 摘要：Robots often interact with the world via attached parts such as wheels, joints, or appendages. In many systems, these interactions, and the manner in which they lead to locomotion, can be understood using the machinery of geometric mechanics, explaining how inputs in the shape space of a robot affect motion in its configuration space and the configuration space of its environment. In this paper we consider an opposite type of locomotion, wherein robots are influenced actively by interactions with an externally forced ambient medium. We investigate two examples of externally actuated systems; one for which locomotion is governed by a principal connection, and is usually considered to possess no drift dynamics, and another for which no such connection exists, with drift inherent in its locomotion. For the driftless system, we develop geometric tools based on previously understood internally actuated versions of the system and demonstrate their use for motion planning under external actuation. For the system possessing drift, we employ nonholonomic reduction to obtain a reduced representation of the system dynamics, illustrate geometric features conducive to studying locomotion, and derive strategies for external actuation.

【24】 DensePASS: Dense Panoramic Semantic Segmentation via Unsupervised Domain Adaptation with Attention-Augmented Context Exchange 标题：DensePASS：基于注意力增强上下文交换的无监督领域适配的密集全景语义分割 链接：https://arxiv.org/abs/2108.06383

作者：Chaoxiang Ma,Jiaming Zhang,Kailun Yang,Alina Roitberg,Rainer Stiefelhagen 机构： in part by the University of Excellence through the “KITFuture Fields” project, ) 1Authors are with Institute for Anthropomatics and Robotics, KarlsruheInstitute of Technology 备注：Accepted to IEEE ITSC 2021. Dataset and code will be made publicly available at this https URL 摘要：智能车辆显然受益于360度传感器的扩展视野（FoV），但绝大多数可用的语义分割训练图像都是用针孔相机拍摄的。在这项工作中，我们通过领域自适应的视角来看待这个问题，并将全景语义分割引入到一个场景中，其中标记的训练数据来自于传统针孔相机图像的不同分布。首先，我们形式化了全景语义分割的无监督域适配任务，其中，根据针孔相机数据源域中的标记示例训练的网络部署在全景图像的不同目标域中，而没有可用的标记。为了验证这一想法，我们收集并公开发布了DensePASS——一种用于跨域条件下全景分割的新型密集注释数据集，专门用于研究针孔到全景的转换，并附带了从城市景观中获得的针孔相机训练示例。DensePASS涵盖标记和未标记的360度图像，标记数据包括19个类别，这些类别明确符合源域（即针孔）数据中可用的类别。为了应对领域转移的挑战，我们利用当前基于注意机制的进展，基于不同的注意增强域自适应模块，构建了跨领域全景语义分割的通用框架。我们的框架在学习域对应时促进了局部和全局级别的信息交换，并将两个标准分段网络的平均IoU域自适应性能提高了6.05%和11.26%。 摘要：Intelligent vehicles clearly benefit from the expanded Field of View (FoV) of the 360-degree sensors, but the vast majority of available semantic segmentation training images are captured with pinhole cameras. In this work, we look at this problem through the lens of domain adaptation and bring panoramic semantic segmentation to a setting, where labelled training data originates from a different distribution of conventional pinhole camera images. First, we formalize the task of unsupervised domain adaptation for panoramic semantic segmentation, where a network trained on labelled examples from the source domain of pinhole camera data is deployed in a different target domain of panoramic images, for which no labels are available. To validate this idea, we collect and publicly release DensePASS - a novel densely annotated dataset for panoramic segmentation under cross-domain conditions, specifically built to study the Pinhole-to-Panoramic transfer and accompanied with pinhole camera training examples obtained from Cityscapes. DensePASS covers both, labelled- and unlabelled 360-degree images, with the labelled data comprising 19 classes which explicitly fit the categories available in the source domain (i.e. pinhole) data. To meet the challenge of domain shift, we leverage the current progress of attention-based mechanisms and build a generic framework for cross-domain panoramic semantic segmentation based on different variants of attention-augmented domain adaptation modules. Our framework facilitates information exchange at local- and global levels when learning the domain correspondences and improves the domain adaptation performance of two standard segmentation networks by 6.05% and 11.26% in Mean IoU.