机器人相关学术速递[6.25]

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cs.RO机器人相关，共计11篇

【1】 Model-Based Reinforcement Learning via Latent-Space Collocation 标题：基于模型的潜在空间配置强化学习

作者：Oleh Rybkin,Chuning Zhu,Anusha Nagabandi,Kostas Daniilidis,Igor Mordatch,Sergey Levine 机构： 20 16;Equal contribution 1University of Pennsylvania 2Covariant 3Google AI 4UC Berkeley 备注：International Conference on Machine Learning (ICML), 2021. Videos and code at this https URL 链接：https://arxiv.org/abs/2106.13229 摘要：对未来进行规划的能力，同时只利用原始的高维观测，如图像，可以为自主代理提供广泛的能力。基于视觉模型的强化学习（RL）方法直接规划未来的行为，在只需要短时推理的任务上取得了令人印象深刻的效果，然而，这些方法在时间扩展的任务上却很困难。我们认为，通过规划状态序列而不仅仅是行动来解决长时间的任务更容易，因为行动的效果随着时间的推移而大大复合，并且更难优化。为了实现这一点，我们借鉴了最优控制文献中的搭配思想，并利用学习到的潜在状态空间模型将其应用到基于图像的环境中。由此产生的潜在搭配方法（LatCo）优化了潜在状态的轨迹，这比以前提出的基于视觉模型的RL射击方法在奖励稀疏和长期目标的任务上有了改进。视频和代码https://orybkin.github.io/latco/. 摘要：The ability to plan into the future while utilizing only raw high-dimensional observations, such as images, can provide autonomous agents with broad capabilities. Visual model-based reinforcement learning (RL) methods that plan future actions directly have shown impressive results on tasks that require only short-horizon reasoning, however, these methods struggle on temporally extended tasks. We argue that it is easier to solve long-horizon tasks by planning sequences of states rather than just actions, as the effects of actions greatly compound over time and are harder to optimize. To achieve this, we draw on the idea of collocation, which has shown good results on long-horizon tasks in optimal control literature, and adapt it to the image-based setting by utilizing learned latent state space models. The resulting latent collocation method (LatCo) optimizes trajectories of latent states, which improves over previously proposed shooting methods for visual model-based RL on tasks with sparse rewards and long-term goals. Videos and code at https://orybkin.github.io/latco/.

【2】 Driver-centric Risk Object Identification 标题：以司机为中心的风险对象识别

作者：Chengxi Li,Stanley H. Chan,Yi-Ting Chen 机构：CheniswithNationalYangMingChiaoTungUniver-sity and National Chiao Tung University 备注：Submitted to TPAMI 链接：https://arxiv.org/abs/2106.13201 摘要：大量的交通事故是由于司机失误造成的。为了减少死亡事故，迫切需要开发智能驾驶系统来帮助驾驶员识别潜在的风险。在现有的研究中，危险情况通常是基于碰撞预测来定义的。然而，碰撞只是交通场景中的一种风险。我们认为需要一个更通用的定义。在这项工作中，我们提出了一个新的以驾驶员为中心的风险定义，即风险对象影响驾驶员的行为。在此基础上，提出了一种新的风险目标识别方法。我们将任务描述为因果问题，并从情境感知模型和因果推理模型中得到启发，提出了一种新的两阶段风险对象识别框架。一个以驾驶员为中心的风险对象识别（ROI）数据集被用来评估所提出的系统。与ROI数据集上的强基线相比，我们展示了最先进的风险对象识别性能。此外，我们进行了广泛的烧蚀研究，以证明我们的设计选择。 摘要：A massive number of traffic fatalities are due to driver errors. To reduce fatalities, developing intelligent driving systems assisting drivers to identify potential risks is in urgent need. Risky situations are generally defined based on collision prediction in existing research. However, collisions are only one type of risk in traffic scenarios. We believe a more generic definition is required. In this work, we propose a novel driver-centric definition of risk, i.e., risky objects influence driver behavior. Based on this definition, a new task called risk object identification is introduced. We formulate the task as a cause-effect problem and present a novel two-stage risk object identification framework, taking inspiration from models of situation awareness and causal inference. A driver-centric Risk Object Identification (ROI) dataset is curated to evaluate the proposed system. We demonstrate state-of-the-art risk object identification performance compared with strong baselines on the ROI dataset. In addition, we conduct extensive ablative studies to justify our design choices.

【3】 Comparison between safety methods control barrier function vs. reachability analysis 标题：屏障功能控制安全方法与可达性分析的比较

作者：Zhichao Li 机构：Department of Electrical and Computer Engineering, University of California, San Diego 链接：https://arxiv.org/abs/2106.13176 摘要：本文旨在比较两种安全方法：控制障碍函数和Hamilton-Jacobi可达性分析。我们将从系统动力学的一般性、构造的难度和计算成本等方面来考虑这一差异。一个标准的杜宾斯汽车模型将进行数值评估，使比较更具体。 摘要：This report aims to compare two safety methods: control barrier function and Hamilton-Jacobi reachability analysis. We will consider the difference with a focus on the following aspects: generality of system dynamics, difficulty of construction and computation cost. A standard Dubins car model will be evaluated numerically to make the comparison more concrete.

【4】 Some Problems of Deployment and Navigation of Civilian Aerial Drones 标题：民用无人机部署导航的几个问题

作者：Xiaohui Li 机构：School of Electrical Engineering and Telecommunications, University of New South Wales 链接：https://arxiv.org/abs/2106.13162 摘要：最大的挑战之一是确定无人机的部署和导航，以使其在不同的应用中受益最大。关于这个话题，人们提出了许多研究问题。例如，近几年来，利用无人机进行的野生动物监测备受关注。不幸的是，这种方法对不同种类的野生动物造成了严重的干扰。此外，由于能够在需要时迅速将通信供应转向需求，配备有基站（即无人机小区）的无人机正成为向灾区灾民和救援队提供蜂窝网络的一个有希望的解决方案。然而，很少有研究在有限的回程通信距离下研究多个无人机单元的最优部署。此外，在许多实际应用中，无人驾驶飞机作为飞行互动器的使用还没有得到充分的讨论。无人机自主空中互动系统具有良好的机动性，可用于鲨鱼攻击预防和动物放牧。尽管如此，以前对自主无人机的研究并没有对此类应用进行过太详细的论述。本报告探讨了上述所有研究问题的解决方案，特别侧重于无人机的部署和导航。仿真结果验证了所提方法的有效性。我们相信，我们在这份报告中的发现为自主民用无人机的根本好处提供了新的启示。 摘要：One of the biggest challenges is to determine the deployment and navigation of the drones to benefit the most for different applications. Many research questions have been raised about this topic. For example, drone-enabled wildlife monitoring has received much attention in recent years. Unfortunately, this approach results in significant disturbance to different species of wild animals. Moreover, with the capability of rapidly moving communication supply towards demand when required, the drone equipped with a base station, i.e., drone-cell, is becoming a promising solution for providing cellular networks to victims and rescue teams in disaster-affected areas. However, few studies have investigated the optimal deployments of multiple drone-cells with limited backhaul communication distances. In addition, the use of autonomous drones as flying interactors for many real-life applications has not been sufficiently discussed. With superior maneuverability, drone-enabled autonomous aerial interacting can potentially be used on shark attack prevention and animal herding. Nevertheless, previous studies of autonomous drones have not dealt with such applications in much detail. This report explores the solutions to all the mentioned research questions, with a particular focus on the deployment and navigation of the drones. Simulations have been conducted to verify the effectiveness of the proposed approaches. We believe that our findings in this report shed new light on the fundamental benefits of autonomous civilian drones.

【5】 Autonomous Driving Strategies at Intersections: Scenarios, State-of-the-Art, and Future Outlooks 标题：交叉口自动驾驶策略：情景、现状和未来展望

作者：Lianzhen Wei,Zirui Li,Jianwei Gong,Cheng Gong,Jiachen Li 机构：of Mechanical Engineering, Beijing Institute of Technology, Beijing, China., Civil Engineering and Geosciences, Delft University of Technology, Stevinweg , CN Delft, The Netherlands., perform self-learning to minimize the delay at intersections [,]. 链接：https://arxiv.org/abs/2106.13052 摘要：由于交叉口场景的复杂性和动态性，交叉口自动驾驶策略一直是智能交通系统研究的难点和热点。本文简要总结了目前最先进的交叉口自动驾驶策略。首先，我们列举和分析了常见的交叉口场景类型、相应的仿真平台以及相关的数据集。其次，在回顾前人研究的基础上，总结了现有自主驾驶策略的特点，并对其进行了分类。最后指出了现有自主驾驶策略存在的问题，并提出了几点有价值的研究展望。 摘要：Due to the complex and dynamic character of intersection scenarios, the autonomous driving strategy at intersections has been a difficult problem and a hot point in the research of intelligent transportation systems in recent years. This paper gives a brief summary of state-of-the-art autonomous driving strategies at intersections. Firstly, we enumerate and analyze common types of intersection scenarios, corresponding simulation platforms, as well as related datasets. Secondly, by reviewing previous studies, we have summarized characteristics of existing autonomous driving strategies and classified them into several categories. Finally, we point out problems of the existing autonomous driving strategies and put forward several valuable research outlooks.

【6】 rSoccer: A Framework for Studying Reinforcement Learning in Small and Very Small Size Robot Soccer 标题：rSocball：一种研究小型和超小型机器人足球强化学习的框架

作者：Felipe B. Martins,Mateus G. Machado,Hansenclever F. Bassani,Pedro H. M. Braga,Edna S. Barros 机构：Centro de Inform´atica - Universidade Federal de Pernambuco, Av. Jornalista Anibal, Fernandes, sn - CDU ,.,-, Recife, PE, Brazil. 链接：https://arxiv.org/abs/2106.12895 摘要：强化学习是一个活跃的研究领域，在机器人学中有着广泛的应用，而RoboCup竞赛是研究和评价强化学习方法的一个有趣的环境。将强化学习应用于机器人学的一个已知困难是需要大量的经验样本，即使用模拟环境训练代理，然后将学习转移到现实世界（sim-to-real）是一条可行的路径。本文介绍了一个用于IEEE超小型足球和小型联赛的开放源代码模拟器，该模拟器针对强化学习实验进行了优化。我们还提出了一个框架，用于创建OpenAI健身房环境和一组基准任务，用于评估单agent和多agent机器人足球技能。然后，我们展示了两种最先进的强化学习方法的学习能力，以及它们在该框架中引入的特定场景中的局限性。我们相信，这将使更多的团队更容易在这些类别的竞争中使用端到端强化学习方法，并进一步发展这一研究领域。 摘要：Reinforcement learning is an active research area with a vast number of applications in robotics, and the RoboCup competition is an interesting environment for studying and evaluating reinforcement learning methods. A known difficulty in applying reinforcement learning to robotics is the high number of experience samples required, being the use of simulated environments for training the agents followed by transfer learning to real-world (sim-to-real) a viable path. This article introduces an open-source simulator for the IEEE Very Small Size Soccer and the Small Size League optimized for reinforcement learning experiments. We also propose a framework for creating OpenAI Gym environments with a set of benchmarks tasks for evaluating single-agent and multi-agent robot soccer skills. We then demonstrate the learning capabilities of two state-of-the-art reinforcement learning methods as well as their limitations in certain scenarios introduced in this framework. We believe this will make it easier for more teams to compete in these categories using end-to-end reinforcement learning approaches and further develop this research area.

【7】 Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control 标题：SE(3)流形上基于哈密顿的动力学学习与控制神经常微分方程网络

作者：Thai Duong,Nikolay Atanasov 机构：Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA , USA 备注：Accepted to RSS 2021. Website: this https URL 链接：https://arxiv.org/abs/2106.12782 摘要：精确的机器人动力学模型对于机器人的安全稳定控制和新工况的推广至关重要。然而，手工设计的模型可能不够精确，即使经过仔细的参数调整。这促使机器学习技术的使用，以近似的机器人动力学训练集的状态控制轨迹。以SE（3）位姿和广义速度描述了地面、空中和水下机器人的动力学行为，并满足能量守恒原理。本文在常微分方程（ODE）网络结构的SE（3）流形上提出了一种近似刚体动力学的哈密顿公式。与黑匣子ODE网络不同，我们的公式保证了建筑节能。我们开发了能量成形和阻尼注入控制的学习，潜在欠驱动SE（3）哈密顿动力学，使稳定和轨迹跟踪与各种平台，包括摆，刚体和四转子系统的统一方法。 摘要：Accurate models of robot dynamics are critical for safe and stable control and generalization to novel operational conditions. Hand-designed models, however, may be insufficiently accurate, even after careful parameter tuning. This motivates the use of machine learning techniques to approximate the robot dynamics over a training set of state-control trajectories. The dynamics of many robots, including ground, aerial, and underwater vehicles, are described in terms of their SE(3) pose and generalized velocity, and satisfy conservation of energy principles. This paper proposes a Hamiltonian formulation over the SE(3) manifold of the structure of a neural ordinary differential equation (ODE) network to approximate the dynamics of a rigid body. In contrast to a black-box ODE network, our formulation guarantees total energy conservation by construction. We develop energy shaping and damping injection control for the learned, potentially under-actuated SE(3) Hamiltonian dynamics to enable a unified approach for stabilization and trajectory tracking with various platforms, including pendulum, rigid-body, and quadrotor systems.

【8】 Topological Semantic Mapping by Consolidation of Deep Visual Features 标题：基于深层视觉特征合并的拓扑语义映射

作者：Ygor C. N. Sousa,Hansenclever F. Bassani 机构：UniversidadeFederaldePernambuco 备注：8 pages, 3 figures 链接：https://arxiv.org/abs/2106.12709 摘要：近年来，许多文献提出了利用卷积神经网络（CNNs）来识别图像语义属性的语义映射方法。属性的类型（例如：房间大小、场所类别和对象）及其类（例如：厨房和浴室，用于场所类别）通常是预定义的，并且仅限于特定的任务。因此，在构建地图的过程中获取和处理的所有视觉数据都将丢失，地图上只保留已识别的语义属性。与此相反，本文引入了一种拓扑语义映射方法，该方法使用CNN（GoogLeNet）从机器人操作时在多个环境视图中捕获的2D图像中提取的深度视觉特征，来创建在每个拓扑节点覆盖的区域中获得的视觉特征的统一表示。这些合并表示允许灵活地识别区域的语义属性，并在一系列视觉任务中使用。实验结果表明，该方法能够有效地融合区域的视觉特征，将区域的视觉特征作为语义属性识别对象和位置类别，并指示图像的拓扑位置，取得了很好的效果。利用GoogLeNet的分类层对目标进行分类，无需再训练，利用浅层多层感知器对地点类别进行识别。 摘要：Many works in the recent literature introduce semantic mapping methods that use CNNs (Convolutional Neural Networks) to recognize semantic properties in images. The types of properties (eg.: room size, place category, and objects) and their classes (eg.: kitchen and bathroom, for place category) are usually predefined and restricted to a specific task. Thus, all the visual data acquired and processed during the construction of the maps are lost and only the recognized semantic properties remain on the maps. In contrast, this work introduces a topological semantic mapping method that uses deep visual features extracted by a CNN, the GoogLeNet, from 2D images captured in multiple views of the environment as the robot operates, to create consolidated representations of visual features acquired in the regions covered by each topological node. These consolidated representations allow flexible recognition of semantic properties of the regions and use in a range of visual tasks. The experiments, performed using a real-world indoor dataset, showed that the method is able to consolidate the visual features of regions and use them to recognize objects and place categories as semantic properties, and to indicate the topological location of images, with very promising results. The objects are classified using the classification layer of GoogLeNet, without retraining, and the place categories are recognized using a shallow Multilayer Perceptron.

【9】 Object Detection and Ranging for Autonomous Navigation of Mobile Robots 标题：移动机器人自主导航中的目标检测与测距

作者：Md Ziaul Haque Zim,Nimai Chandra Das 机构：Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh, Department of Automatic Control Systems,Saint Petersburg Electrotechnical University “LETI”, Saint Petersburg, Russia, A R T I C L E I N F O 链接：https://arxiv.org/abs/2106.12701 摘要：近十年来，电子技术日新月异，方法论也应不断更新。为了测距，使用了各种方法，如无线电探测和测距（雷达）、光探测和测距（激光雷达）以及声波导航和测距（声纳）等。后来，通过对早期技术的改造和对导航中探测和测距目的的进一步修改，声波探测和测距技术在现代机器人中得到了应用。SODAR可以定义为声纳的孩子，也是回声探测仪的双胞胎。回声测深仪仅用于测距。但是SODAR使用33千赫的低频波来测量水下深度，同时也探测水下介质下的物体。因此，本文的工作包括设计一个移动机器人自主导航目标检测与测距评估系统。 摘要：In the recent decade, electronic technology gets advanced day by day the methodologies too should update. For the purpose of ranging various methods such Radio Detection and Ranging (RADAR), Light Detection and Ranging (LIDAR) and Sonic Navigation and Ranging (SONAR) etc. are used. Later, by adapting the earlier technologies and further modifying the purposes of detection and ranging in navigation, the technology of Sonic Detection and Ranging (SODAR) is used in modern robotics. The SODAR can be defined as a child of SONAR and also a twin of Echo sounder. The echo-sounder is used only for ranging. But the SODAR use the low-frequency wave of 33 kHz to measure the underwater depth and also to detect the objects below the water medium. So, this work comprises the designing of a system to evaluate the Object Detection and Ranging for Autonomous Navigation of Mobile Robots.

【10】 What makes visual place recognition easy or hard? 标题：是什么让视觉地点识别变得容易还是困难？

作者：Stefan Schubert,Peer Neubert 机构：TU Chemnitz, Germany 链接：https://arxiv.org/abs/2106.12671 摘要：视觉位置识别是移动机器人定位的基本能力。它将图像检索放在物理世界中操作的物理代理的实际上下文中。它是一个活跃的研究领域，在许多不同的实验中提出并评估了许多不同的方法。在下文中，我们认为，由于实际环境和个人设计决策的变化，地点识别实验在不同的论文中几乎没有可比性，并且在不同的实验中，有各种各样的特性可以改变。我们提供了一个这样的属性的广泛列表，并举例说明如何使用它们来设置一个位置识别实验更容易或更难。这对于不同的参与者来说可能是有趣的：（1）只想选择适合他们手头特定任务性质的地点识别方法的人，（2）寻找开放性研究问题并对特别困难的情况感兴趣的研究人员，（3）作者希望在这个主题上创造可复制的论文，以及（4）审稿人的任务，以确定潜在的问题，在审查的论文。 摘要：Visual place recognition is a fundamental capability for the localization of mobile robots. It places image retrieval in the practical context of physical agents operating in a physical world. It is an active field of research and many different approaches have been proposed and evaluated in many different experiments. In the following, we argue that due to variations of this practical context and individual design decisions, place recognition experiments are barely comparable across different papers and that there is a variety of properties that can change from one experiment to another. We provide an extensive list of such properties and give examples how they can be used to setup a place recognition experiment easier or harder. This might be interesting for different involved parties: (1) people who just want to select a place recognition approach that is suitable for the properties of their particular task at hand, (2) researchers that look for open research questions and are interested in particularly difficult instances, (3) authors that want to create reproducible papers on this topic, and (4) also reviewers that have the task to identify potential problems in papers under review.

【11】 Coherent, super resolved radar beamforming using self-supervised learning 标题：基于自监督学习的相干超分辨雷达波束形成

作者：Itai Orr,Moshik Cohen,Harel Damari,Meir Halachmi,Zeev Zalevsky 机构：Bar Ilan University, Ramat-Gan, Israel, Wisense Technologies Ltd., Tel Aviv, Israel 备注：28 pages 10 figures 链接：https://arxiv.org/abs/2106.13085 摘要：高分辨率汽车雷达传感器的要求，以满足高标准的自主汽车的需要和法规。然而，目前的雷达系统在角度分辨率上受到限制，造成了技术上的空白。通过增加物理通道的数量来提高角度分辨率的行业和学术趋势，也增加了系统复杂性，需要敏感的校准过程，降低了对硬件故障的鲁棒性，并导致更高的成本。本文提出了一种新的雷达信号重建方法，称为自监督雷达信号重建（R2-S2），该方法在不增加物理通道数目的情况下，显著提高了给定雷达阵列的角分辨力。R2-S2算法是一类以复杂距离多普勒雷达数据为输入的深度神经网络（DNN）算法，它采用在多个数据表示空间中运行的损失函数进行自监督训练。在晴雨天气条件下，利用在城市和公路环境中收集的真实数据集，证明了角度分辨率提高了4倍。 摘要：High resolution automotive radar sensors are required in order to meet the high bar of autonomous vehicles needs and regulations. However, current radar systems are limited in their angular resolution causing a technological gap. An industry and academic trend to improve angular resolution by increasing the number of physical channels, also increases system complexity, requires sensitive calibration processes, lowers robustness to hardware malfunctions and drives higher costs. We offer an alternative approach, named Radar signal Reconstruction using Self Supervision (R2-S2), which significantly improves the angular resolution of a given radar array without increasing the number of physical channels. R2-S2 is a family of algorithms which use a Deep Neural Network (DNN) with complex range-Doppler radar data as input and trained in a self-supervised method using a loss function which operates in multiple data representation spaces. Improvement of 4x in angular resolution was demonstrated using a real-world dataset collected in urban and highway environments during clear and rainy weather conditions.