机器人相关学术速递[11.11]

cs.RO机器人相关，共计10篇

【1】 Structure from Silence: Learning Scene Structure from Ambient Sound 标题：无声中的结构：从环境声中学习场景结构 链接：https://arxiv.org/abs/2111.05846

作者：Ziyang Chen,Xixi Hu,Andrew Owens 机构：University of Michigan 备注：Accepted to CoRL 2021 (Oral Presentation) 摘要：从旋转的吊扇到滴答作响的时钟，当我们在场景中移动时，我们听到的声音会发生微妙的变化。我们询问这些环境声音是否传达了有关3D场景结构的信息，如果是，它们是否为多模态模型提供了有用的学习信号。为了研究这一点，我们收集了来自各种安静室内场景的成对音频和RGB-D录音数据集。然后，我们训练模型，估计到附近墙壁的距离，只提供音频作为输入。我们还利用这些录音通过自我监督学习多模态表征，通过训练网络将图像与其对应的声音相关联。这些结果表明，环境声音传递了大量有关场景结构的信息，是学习多模态特征的有用信号。 摘要：From whirling ceiling fans to ticking clocks, the sounds that we hear subtly vary as we move through a scene. We ask whether these ambient sounds convey information about 3D scene structure and, if so, whether they provide a useful learning signal for multimodal models. To study this, we collect a dataset of paired audio and RGB-D recordings from a variety of quiet indoor scenes. We then train models that estimate the distance to nearby walls, given only audio as input. We also use these recordings to learn multimodal representations through self-supervision, by training a network to associate images with their corresponding sounds. These results suggest that ambient sound conveys a surprising amount of information about scene structure, and that it is a useful signal for learning multimodal features.

【2】 Object Servoing of Differential-Drive Robots 标题：差动驱动机器人的目标伺服 链接：https://arxiv.org/abs/2111.05710

作者：Weibin Jia,Wenjie Zhao,Zhihuan Song,Zhengguo Li 机构：. School of Aeronautics and Astronautics, Zhejiang University, Zhejiang , China, . School of Control Science and Engineering, Zhejiang University, Zhejiang , China, . SRO Department of Institute for Infocomm Research, Fusionopolis Way, Singapore 摘要：由于可移动物体的姿态可能发生变化以及差动驱动机器人的非完整约束，设计一种可使差动驱动机器人渐近停在预定相对可移动物体姿态的物体伺服方案具有挑战性。本文针对差动驱动机器人设计了一种新的目标伺服方案。每个在线相对姿势首先通过使用可移动对象的特征点进行估计，并作为对象伺服友好型停车控制器的输入。然后由停车控制器确定线速度和角速度。实验结果验证了所提出的目标伺服方案的性能。由于其较低的在线计算成本，该方案可应用于差速驱动机器人向可移动物体的最后一英里输送。 摘要：Due to possibly changing pose of a movable object and nonholonomic constraint of a differential-drive robot, it is challenging to design an object servoing scheme for the differential-drive robot to asymptotically park at a predefined relative pose to the movable object. In this paper, a novel object servoing scheme is designed for the differential-drive robots. Each on-line relative pose is first estimated by using feature points of the moveable object and it serves as the input of an object servoing friendly parking controller. The linear velocity and angular velocity are then determined by the parking controller. Experimental results validate the performance of the proposed object servoing scheme. Due to its low on-line computational cost, the proposed scheme can be applied for last mile delivery of differential-drive robots to movable objects.

【3】 Effects of Design and Hydrodynamic Parameters on Optimized Swimming for Simulated, Fish-inspired Robots 标题：设计和水动力参数对模拟仿鱼机器人优化游泳的影响 链接：https://arxiv.org/abs/2111.05682

作者：Donghao Li,Hankun Deng,Yagiz E. Bayiz,Bo Cheng 摘要：在这项工作中，我们开发了一个以鱼为灵感的机器人模板的数学模型和仿真平台，即磁性、模块化、波动机器人（$\mu$机器人）。通过这个平台，我们通过强化学习系统地探讨了设计和流体参数对游泳成绩的影响。数学模型由两个相互作用的子系统组成，机器人动力学和流体动力学，流体动力学模型由反应组件（附加质量和压力）和阻力组件（阻力和摩擦力）组成，然后对其进行无量纲化，以导出关键的“控制参数”机器人流体相互作用的研究。$\mu$机器人通过谐波电压信号控制的磁致动器驱动，通过基于EM的策略超参数探索（EPHE）优化磁致动器，以最大化游泳速度。通过改变控制参数，通过EPHE对36个不同机器人模板变化（驱动次数（NoA）和刚度）和流体动力学参数的情况进行了模拟和优化。结果表明，优化步态的波长（即沿身体的行波）与模板变化和流体动力学参数无关。NoA越高，速度越高，但每体长的速度越低，但增益越小，每体长的速度越低。身体和尾鳍步态动力学主要由流体附加质量、弹簧和驱动力矩之间的相互作用决定，流体阻力阻力的贡献可以忽略不计。相反，推力的产生主要是由作用在尾鳍上的压力所决定的，因为稳定的游泳是阻力和压力之间平衡的结果，附加的质量和身体阻力的贡献很小。因此，附加质量力仅通过尾鳍动力学间接影响推力产生和游泳速度。 摘要：In this work we developed a mathematical model and a simulation platform for a fish-inspired robotic template, namely Magnetic, Modular, Undulatory Robotics ($\mu$Bots). Through this platform, we systematically explored the effects of design and fluid parameters on the swimming performance via reinforcement learning. The mathematical model was composed of two interacting subsystems, the robot dynamics and the hydrodynamics, and the hydrodynamic model consisted of reactive components (added-mass and pressure forces) and resistive components (drag and friction forces), which were then nondimensionalized for deriving key "control parameters" of robot-fluid interaction. The $\mu$Bot was actuated via magnetic actuators controlled with harmonic voltage signals, which were optimized via EM-based Policy Hyper Parameter Exploration (EPHE) to maximize swimming speed. By varying the control parameters, total 36 cases with different robot template variations (Number of Actuation (NoA) and stiffness) and hydrodynamic parameters were simulated and optimized via EPHE. Results showed that wavelength of optimized gaits (i.e., traveling wave along body) was independent of template variations and hydrodynamic parameters. Higher NoA yielded higher speed but lower speed per body length however with diminishing gain and lower speed per body length. Body and caudal-fin gait dynamics were dominated by the interaction among fluid added-mass, spring, and actuation torque, with negligible contribution from fluid resistive drag. In contrast, thrust generation was dominated by pressure force acting on caudal fin, as steady swimming resulted from a balance between resistive force and pressure force, with minor contributions from added-mass and body drag forces. Therefore, added-mass force only indirectly affected the thrust generation and swimming speed via the caudal fin dynamics.

【4】 FabricFlowNet: Bimanual Cloth Manipulation with a Flow-based Policy 标题：FabricFlowNet：基于流策略的双工布料操作 链接：https://arxiv.org/abs/2111.05623

作者：Thomas Weng,Sujay Bajracharya,Yufei Wang,Khush Agrawal,David Held 机构：Robotics Institute, Carnegie Mellon University, USA 备注：CoRL 2021 摘要：我们解决了目标导向的布料操纵问题，这是一项具有挑战性的任务，因为布料的可变形性。我们的见解是光流，一种通常用于视频中运动估计的技术，也可以为观察和目标图像中相应的布料姿势提供有效的表示。我们将介绍FabricFlowNet（FFN），这是一种布料操作策略，它利用流作为输入和动作表示来提高性能。FabricFlowNet还根据所需目标在双手和单臂动作之间优雅地切换。我们表明，FabricFlowNet的性能显著优于采用图像输入的最先进的无模型和基于模型的布料操作策略。我们还介绍了一个双手系统的真实实验，展示了有效的模拟到真实的传输。最后，我们证明了我们的方法可以推广到其他形状的布料，如T恤衫和长方形布料。视频和其他补充材料可从以下网址获得：https://sites.google.com/view/fabricflownet. 摘要：We address the problem of goal-directed cloth manipulation, a challenging task due to the deformability of cloth. Our insight is that optical flow, a technique normally used for motion estimation in video, can also provide an effective representation for corresponding cloth poses across observation and goal images. We introduce FabricFlowNet (FFN), a cloth manipulation policy that leverages flow as both an input and as an action representation to improve performance. FabricFlowNet also elegantly switches between bimanual and single-arm actions based on the desired goal. We show that FabricFlowNet significantly outperforms state-of-the-art model-free and model-based cloth manipulation policies that take image input. We also present real-world experiments on a bimanual system, demonstrating effective sim-to-real transfer. Finally, we show that our method generalizes when trained on a single square cloth to other cloth shapes, such as T-shirts and rectangular cloths. Video and other supplementary materials are available at: https://sites.google.com/view/fabricflownet.

【5】 TomoSLAM: factor graph optimization for rotation angle refinement in microtomography 标题：TomoSLAM：微层析成像中旋转角度优化的因子图优化 链接：https://arxiv.org/abs/2111.05562

作者：Mark Griguletskii,Mikhail Chekanov,Oleg Shipitko 机构：Kibalov, Institute for Information Transmission Problems – IITP RAS, Bol’shoy Karetnyy Pereulok , Moscow, Russia, ., Evocargo, Moscow, Russia, Smart Engines Service LLC, Moscow, Russia 摘要：在计算机断层扫描（CT）中，样品、探测器和信号源的相对轨迹传统上被认为是已知的，因为它们是由仪器部件的有意预编程运动引起的。然而，由于机械反冲击、旋转传感器测量误差等原因，热变形的真实轨迹与期望轨迹不同。这会对层析重建的结果质量产生负面影响。设备的校准或初步调整都不能完全消除轨迹的不精确性，但会显著增加仪器维护成本。解决此问题的许多方法都基于重建过程中每个投影（在每个时间步）相对于样本的源和传感器位置估计的自动细化。在机器人技术中（特别是在移动机器人和自动驾驶车辆中），从不同角度观察对象的不同图像时，位置优化的类似问题是众所周知的，称为同步定位和映射（SLAM）。这项工作的科学新奇之处在于，将微层析成像中的轨迹细化问题视为SLAM问题。这是通过从X射线投影中提取加速鲁棒特征（SURF）特征，使用随机样本一致性（RANSAC）过滤匹配，计算投影之间的角度，并在因子图中结合步进电机控制信号使用它们来优化旋转角度来实现的。 摘要：In computed tomography (CT), the relative trajectories of a sample, a detector, and a signal source are traditionally considered to be known, since they are caused by the intentional preprogrammed movement of the instrument parts. However, due to the mechanical backlashes, rotation sensor measurement errors, thermal deformations real trajectory differs from desired ones. This negatively affects the resulting quality of tomographic reconstruction. Neither the calibration nor preliminary adjustments of the device completely eliminates the inaccuracy of the trajectory but significantly increase the cost of instrument maintenance. A number of approaches to this problem are based on an automatic refinement of the source and sensor position estimate relative to the sample for each projection (at each time step) during the reconstruction process. A similar problem of position refinement while observing different images of an object from different angles is well known in robotics (particularly, in mobile robots and self-driving vehicles) and is called Simultaneous Localization And Mapping (SLAM). The scientific novelty of this work is to consider the problem of trajectory refinement in microtomography as a SLAM problem. This is achieved by extracting Speeded Up Robust Features (SURF) features from X-ray projections, filtering matches with Random Sample Consensus (RANSAC), calculating angles between projections, and using them in factor graph in combination with stepper motor control signals in order to refine rotation angles.

【6】 Verifying Controllers with Convolutional Neural Network-based Perception: A Case for Intelligible, Safe, and Precise Abstractions 标题：基于卷积神经网络感知的控制器验证：一种易于理解、安全和精确的抽象 链接：https://arxiv.org/abs/2111.05534

作者：Chiao Hsieh,Keyur Joshi,Sasa Misailovic,Sayan Mitra 机构：University of Illinois at Urbana-Champaign, Urbana, IL, USA 备注：12 pages, 9 figures, submitted to HSCC 2022 摘要：用于目标检测、车道检测和分割的卷积神经网络（CNN）目前在大多数自治管道中处于领先地位，但其安全性分析仍然是一个重要挑战。对感知模型进行形式化分析从根本上说是困难的，因为它们的正确性很难确定。我们提出了一种从系统级安全需求、数据和perception下游模块的程序分析中推断perception模型的可理解和安全抽象的技术。在创建抽象和随后的验证时，该技术可以帮助权衡安全性、大小和精度。我们将该方法应用于两个基于高保真仿真的重要案例研究（a）基于视觉的自主车辆车道保持控制器和（b）农业机器人控制器。我们展示了如何将生成的抽象与下游模块组合，然后使用CBMC等程序分析工具验证生成的抽象系统。对尺寸、安全要求和环境参数（如照明、路面、工厂类型）对生成的抽象精度的影响的详细评估表明，该方法有助于指导角落案例和安全操作包络的搜索。 摘要：Convolutional Neural Networks (CNN) for object detection, lane detection, and segmentation now sit at the head of most autonomy pipelines, and yet, their safety analysis remains an important challenge. Formal analysis of perception models is fundamentally difficult because their correctness is hard if not impossible to specify. We present a technique for inferring intelligible and safe abstractions for perception models from system-level safety requirements, data, and program analysis of the modules that are downstream from perception. The technique can help tradeoff safety, size, and precision, in creating abstractions and the subsequent verification. We apply the method to two significant case studies based on high-fidelity simulations (a) a vision-based lane keeping controller for an autonomous vehicle and (b) a controller for an agricultural robot. We show how the generated abstractions can be composed with the downstream modules and then the resulting abstract system can be verified using program analysis tools like CBMC. Detailed evaluations of the impacts of size, safety requirements, and the environmental parameters (e.g., lighting, road surface, plant type) on the precision of the generated abstractions suggest that the approach can help guide the search for corner cases and safe operating envelops.

【7】 Dealing with the Unknown: Pessimistic Offline Reinforcement Learning 标题：应对未知：悲观的离线强化学习 链接：https://arxiv.org/abs/2111.05440

作者：Jinning Li,Chen Tang,Masayoshi Tomizuka,Wei Zhan 机构：Department of Mechanical Engineering, University of California, Berkeley, United States 备注：Published in 5th Annual Conference on Robot Learning (CoRL 2021) 摘要：强化学习（RL）已被证明在智能体可以通过与其操作环境的主动交互来学习策略的领域是有效的。然而，如果我们将RL方案更改为离线设置，其中代理只能通过静态数据集更新其策略，那么离线强化学习中的一个主要问题就会出现，即分布转移。我们提出了一种悲观离线强化学习（PessORL）算法，通过操纵值函数，主动引导agent返回到熟悉的区域。我们关注由分布外（OOD）状态引起的问题，并故意惩罚训练数据集中不存在的状态的高值，以便学习到的悲观值函数在状态空间中的任何位置降低真实值。我们在各种基准任务上评估了PessORL算法，结果表明，与那些只考虑OOD操作的方法相比，我们的方法通过显式处理OOD状态获得了更好的性能。 摘要：Reinforcement Learning (RL) has been shown effective in domains where the agent can learn policies by actively interacting with its operating environment. However, if we change the RL scheme to offline setting where the agent can only update its policy via static datasets, one of the major issues in offline reinforcement learning emerges, i.e. distributional shift. We propose a Pessimistic Offline Reinforcement Learning (PessORL) algorithm to actively lead the agent back to the area where it is familiar by manipulating the value function. We focus on problems caused by out-of-distribution (OOD) states, and deliberately penalize high values at states that are absent in the training dataset, so that the learned pessimistic value function lower bounds the true value anywhere within the state space. We evaluate the PessORL algorithm on various benchmark tasks, where we show that our method gains better performance by explicitly handling OOD states, when compared to those methods merely considering OOD actions.

【8】 AW-Opt: Learning Robotic Skills with Imitation andReinforcement at Scale 标题：AW-OPT：通过模仿和大规模增强来学习机器人技能 链接：https://arxiv.org/abs/2111.05424

作者：Yao Lu,Karol Hausman,Yevgen Chebotar,Mengyuan Yan,Eric Jang,Alexander Herzog,Ted Xiao,Alex Irpan,Mohi Khansari,Dmitry Kalashnikov,Sergey Levine 机构：Robotics at Google, X, The Moonshot Factory, UC Berkeley 摘要：机器人技能可以通过模仿学习（IL）使用用户提供的演示来学习，或者通过强化学习（RL）使用大量自主收集的经验来学习。这两种方法都有互补的优点和缺点：RL可以达到高水平的性能，但需要探索，这可能非常耗时且不安全；IL不需要再探索，只需要学习与提供的演示一样好的技能。一种方法可以结合两种方法的优点吗？许多PROR方法旨在解决这个问题，提出了多种集成IL和RL元素的技术。然而，将这种方法扩展到复杂的机器人技能，集成各种离线数据并将其完全推广到现实世界场景仍然是一个重大挑战。在本文中，我们的目标是测试先前IL+RL算法的可扩展性，并设计一个基于详细经验实验的系统，以最有效和可扩展的方式组合现有组件。为此，我们提供了一系列的实验，旨在了解每个设计决策的含义，从而开发出一种组合方法，该方法可以利用演示和异构的先验数据，在一系列真实世界和现实模拟机器人问题上获得最佳性能。我们称之为AW Opt的完整方法结合了优势加权回归[1,2]和QT Opt[3]的要素，为整合演示和机器人操作离线数据提供了统一的方法。请参见https://awopt.github.io 更多细节。 摘要：Robotic skills can be learned via imitation learning (IL) using user-provided demonstrations, or via reinforcement learning (RL) using large amountsof autonomously collected experience.Both methods have complementarystrengths and weaknesses: RL can reach a high level of performance, but requiresexploration, which can be very time consuming and unsafe; IL does not requireexploration, but only learns skills that are as good as the provided demonstrations.Can a single method combine the strengths of both approaches? A number ofprior methods have aimed to address this question, proposing a variety of tech-niques that integrate elements of IL and RL. However, scaling up such methodsto complex robotic skills that integrate diverse offline data and generalize mean-ingfully to real-world scenarios still presents a major challenge. In this paper, ouraim is to test the scalability of prior IL + RL algorithms and devise a system basedon detailed empirical experimentation that combines existing components in themost effective and scalable way. To that end, we present a series of experimentsaimed at understanding the implications of each design decision, so as to develop acombined approach that can utilize demonstrations and heterogeneous prior datato attain the best performance on a range of real-world and realistic simulatedrobotic problems. Our complete method, which we call AW-Opt, combines ele-ments of advantage-weighted regression [1, 2] and QT-Opt [3], providing a unifiedapproach for integrating demonstrations and offline data for robotic manipulation.Please see https://awopt.github.io for more details.

【9】 A Framework for eVTOL Performance Evaluation in Urban Air Mobility Realm 标题：城市空中机动性领域eVTOL性能评价框架 链接：https://arxiv.org/abs/2111.05413

作者：Mrinmoy Sarkar,Xuyang Yan,Abenezer Girma,Abdollah Homaifar 机构：North Carolina A&T State University, Greensboro, North Carolina 备注：7 pages, 9 figures, Submitted to ICRA 2022 conference 摘要：在本文中，我们开发了一个通用的仿真框架，用于在无人机系统（UAS）交通管理（UTM）和城市航空机动性（UAM）概念下评估电动垂直起降飞行器（EVTOL）。与大多数现有研究不同，所提出的框架结合了UTM和EVTOL的使用，以开发一个现实的UAM测试平台。为此，我们首先增强了现有的UTM模拟器，以模拟真实的UAM环境。然后，不再使用简化的eVOTL模型，而是使用了一个真实的eVTOL设计工具，即SUAVE，并引入了一个扩展子模块，以弥合UTM模拟器和SUAVE eVTOL性能评估工具之间的差距，以详细说明完整的任务概要。基于所开发的仿真框架，进行了实验，并给出了结果，以分析在UAM环境下EVTOL的性能。 摘要：In this paper, we developed a generalized simulation framework for the evaluation of electric vertical takeoff and landing vehicles (eVTOLs) in the context of Unmanned Aircraft Systems (UAS) Traffic Management (UTM) and under the concept of Urban Air Mobility (UAM). Unlike most existing studies, the proposed framework combines the utilization of UTM and eVTOLs to develop a realistic UAM testing platform. For this purpose, we first enhanced an existing UTM simulator to simulate the real-world UAM environment. Then, instead of using a simplified eVOTL model, a realistic eVTOL design tool, namely SUAVE, is employed and an dilation sub-module is introduced to bridge the gap between the UTM simulator and SUAVE eVTOL performance evaluation tool to elaborate the complete mission profile. Based on the developed simulation framework, experiments are conducted and the results are presented to analyze the performance of eVTOLs in the UAM environment.

【10】 Optimizing robot planning domains to reduce search time for long-horizon planning 标题：优化机器人规划域以减少长期规划的搜索时间 链接：https://arxiv.org/abs/2111.05397

作者：Maximilian Diehl,Chris Paxton,Karinne Ramirez-Amaro 机构： Chalmers University of Technology 备注：Accepted and presented as extended abstract, 5th Workshop on Semantic Policy and Action Representations for Autonomous Robots (SPAR), Prague, CzechRepublic, Online, 2021, at IROS 2021 摘要：我们最近引入了一个系统，该系统可以根据人类演示自动生成机器人规划操作员。我们系统的一个功能是操作员计数，它跟踪演示中每个操作员的应用频率。在这个扩展的摘要中，我们展示了我们可以使用计数来缩小域，目的是减少长时间规划目标的搜索时间。我们的方法背后的概念理念是，我们希望将演示中更频繁出现的操作员优先于那些未经常观察到的操作员。因此，我们建议将该领域仅限于最受欢迎的运营商。如果这个算子子集不足以找到一个计划，我们迭代地扩展这个算子子集。我们表明，这大大缩短了长期规划目标的搜索时间。 摘要：We have recently introduced a system that automatically generates robotic planning operators from human demonstrations. One feature of our system is the operator count, which keeps track of the application frequency of every operator within the demonstrations. In this extended abstract, we show that we can use the count to slim down domains with the goal of decreasing the search time for long-horizon planning goals. The conceptual idea behind our approach is that we would like to prioritize operators that have occurred more often in the demonstrations over those that were not observed so frequently. We, therefore, propose to limit the domain only to the most popular operators. If this subset of operators is not sufficient to find a plan, we iteratively expand this subset of operators. We show that this significantly reduces the search time for long-horizon planning goals.