机器人相关学术速递[7.23]

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cs.RO机器人相关，共计10篇

【1】 A novel teleoperator testbed to understand the effects of master-slave dynamics on embodiment and kinesthetic perception * 标题：一种新的遥控器试验台，用于了解主从动力学对具身和运动知觉的影响。

作者：Mohit Singhala,Jeremy D. Brown 备注：Accepted as Works-in-Progress paper at Haptics Symposium 2020 链接：https://arxiv.org/abs/2107.10807 摘要：随着遥操作机器人系统的日益普及，人们对触觉感知的透明度也越来越重视。然而，透明度代表了理论上的理想，因为大多数双向力反射遥控机器人在操作者和环境之间引入了动力学（刚度和阻尼）。为了获得真正的灵巧性，必须了解人类如何体现这些遥控机器人的动态，从而将它们与他们正在探索的环境区分开来。在这篇简短的手稿中，我们介绍了一种新颖的单自由度实验台，用于在遥控机器人探索过程中对动觉知觉进行心理物理学和任务绩效评估。该系统可配置为刚性机械遥操作器、动态机械遥操作器和机电遥操作器。我们进行了前驱系统辨识，发现该系统能够模拟遥控机器人的探索，以了解主从动力学对动觉知觉的影响。 摘要：With the rising popularity of telerobotic systems, the focus on transparency with regards to haptic perception is also increasing. Transparency, however, represents a theoretical ideal as most bilateral force-reflecting telerobots introduce dynamics (stiffness and damping) between the operator and the environment. To achieve true dexterity, it will be essential to understand how humans embody the dynamics of these telerobots and thereby distinguish them from the environment they are exploring. In this short manuscript, we introduce a novel single degree-of-freedom testbed designed to perform psychophysical and task performance assessments of kinesthetic perception during telerobotic exploration. The system is capable of being configured as a rigid mechanical teleoperator, a dynamic mechanical teleoperator, and an electromechanicaal teleoperator. We performed prefatory system identification and found that the system is capable of simulating telerobotic exploration necessary to understand the impact of master-slave dynamics on kinesthetic perception.

【2】 Towards an Understanding of the Role Operator Limb Dynamics Plays in Haptic Perception of Stiffness 标题：了解操作者肢体动力学在触觉僵硬知觉中的作用

作者：Mohit Singhala,Jeremy D. Brown 备注：Accepted as Works-in-Progress paper in Haptics Symposium 2018 链接：https://arxiv.org/abs/2107.10788 摘要：创造能够提供灵巧操作所需的丰富感觉的触觉界面对于人在环遥操作系统（HiLTS）的发展至关重要。一个限制因素是缺乏对操作者肢体动力学和触觉探索动力学对触觉感知影响的详细了解。我们建议开始研究这些影响与单一联合触觉探索和反馈的物理和虚拟环境。在这里，我们介绍了我们的实验装置，一个1自由度旋转动觉触觉装置和肌电图（EMG）系统，以及我们研究不同探测速度下刚度辨别阈值变化的初步结果。结果表明，探索速度和辨别阈值之间可能存在某种关系，肌肉激活、探索速度和触觉反馈之间也存在复杂的相互作用。 摘要：Creating haptic interfaces capable of rendering the rich sensation needed for dexterous manipulation is crucial for the advancement of human-in-the-loop telerobotic systems (HiLTS). One limiting factor has been the absence of detailed knowledge of the effect of operator limb dynamics and haptic exploration dynamics on haptic perception. We propose to begin investigations of these effects with single-joint haptic exploration and feedback of physical and virtual environments. Here, we present our experimental apparatus, a 1-DoF rotational kinesthetic haptic device and electromyography (EMG) system, along with preliminary findings from our efforts to investigate the change in stiffness discrimination thresholds for differing exploration velocities. Result trends indicate a possible relationship between exploration velocity and discrimination thresholds, as well as a complex interaction between muscle activation, exploration velocity, and haptic feedback.

【3】 A novel testbed for investigating the impact of teleoperator dynamics on perceived environment dynamics 标题：一种新型遥操作机械手动力学对感知环境动力学影响的试验台

作者：Mohit Singhala,Jeremy D. Brown 备注：Accepted to IROS 2021 链接：https://arxiv.org/abs/2107.10784 摘要：人在回路遥操作机器人系统（HiLTS）是一种机器人工具，旨在扩展并在某些情况下提高虚拟和远程环境中人类操作员的灵巧能力。然而，灵巧的操作取决于遥操作机器人与操作者的感觉运动控制方案的结合程度。经验证据表明，触觉反馈可以提高灵活性。不幸的是，触觉反馈也会在遥操作机器人的引导者和跟随者之间引入动力学，从而影响稳定性和设备性能。虽然一致的研究工作集中在掩盖这些设备的动态或完全绕过它们，但还不清楚人类如何将这些动态纳入他们的控制方案。我们相信，要发展灵巧的遥操作机器人，关键是要了解人类-操作者结合遥操作动力学的过程，并将其与环境动力学区分开来。这方面知识的关键在于了解先进的遥操作机器人体系结构与黄金标准（1950年代首次引入的刚性机械遥操作器）相比有多先进。在这篇手稿中，我们提出了一个遥操作试验台，它有可重构的传输之间的领导者和追随者，以改变其动态行为。该试验台的目的是研究遥操作人员的动态行为对远程/虚拟环境中感知和任务执行的影响。我们描述了试验台的硬件和软件组件，然后演示了不同的遥操作传输如何导致操作员在探索相同环境时所感受到的动态差异，有时是显著的。 摘要：Human-in-the-loop telerobotic systems (HiLTS) are robotic tools designed to extend and in some circumstances improve the dexterous capabilities of the human operator in virtual and remote environments. Dexterous manipulation, however, depends on how well the telerobot is incorporated into the operator's sensorimotor control scheme. Empirical evidence suggests that haptic feedback can lead to improved dexterity. Unfortunately, haptic feedback can also introduce dynamics between the leader and follower of the telerobot that affect both stability and device performance. While concerted research effort has focused on masking these device dynamics or bypassing them altogether, it is not well understood how human operators incorporate these dynamics into their control scheme. We believe that to advance dexterous telerobotic manipulation, it is crucial to understand the process by which humans operators incorporate teleoperator dynamics and distinguish them from the dynamics of the environment. Key to this knowledge is an understanding of how advanced telerobotic architectures compare to the gold standard, the rigid mechanical teleoperators first introduced in the 1950's. In this manuscript, we present a teleoperator testbed that has reconfigurable transmissions between the leader and follower to change its dynamic behavior. The intent of this testbed is to investigate the effect of the teleoperator's dynamics on perception of and task performance in the remote/virtual environment. We describe the hardware and software components of the testbed and then demonstrate how the different teleoperator transmissions can lead to differences, sometimes significant, in the dynamics that would be felt by the operator when exploring the same environment.

【4】 DeltaCharger: Charging Robot with Inverted Delta Mechanism and CNN-driven High Fidelity Tactile Perception for Precise 3D Positioning 标题：DeltaCharger：具有倒置Delta机构和CNN驱动的高保真触觉的充电机器人精确三维定位

作者：Iaroslav Okunevich,Daria Trinitatova,Pavel Kopanev,Dzmitry Tsetserukou 机构： Skolkovo Institute of Science and Technology 备注：Accepted to IEEE Robotics and Automation Letters and 17th International Conference on Automation Science and Engineering (CASE) 2021, IEEE copyright, 7 pages, 9 figures 链接：https://arxiv.org/abs/2107.10710 摘要：DeltaCharger是一种新型的充电机器人，采用倒三角结构对电极进行三维定位，实现了两个移动机器人之间可靠、安全的能量传输。嵌入式高保真触觉传感器允许使用接触面上的压力数据估计充电机构上的电极和目标机器人上的电极之间的角度、垂直和水平偏差。这对于防止短路至关重要。本文介绍了该样机的工作原理，并对不同的机器学习模型进行了评价研究。实验结果表明，该系统利用卷积神经网络（CNN）对压力数据进行角度、垂直和水平偏差测量，准确率分别为95.46%、98.2%和86.9%。DeltaCharger有可能带来一个新的充电系统水平，提高移动自主机器人的普及率。 摘要：DeltaCharger is a novel charging robot with an Inverted Delta structure for 3D positioning of electrodes to achieve robust and safe transferring energy between two mobile robots. The embedded high-fidelity tactile sensors allow to estimate the angular, vertical and horizontal misalignments between electrodes on the charger mechanism and electrodes on the target robot using pressure data on the contact surfaces. This is crucial for preventing a short circuit. In this paper, the mechanism of the developed prototype and evaluation study of different machine learning models for misalignment prediction are presented. The experimental results showed that the proposed system can measure the angle, vertical and horizontal values of misalignment from pressure data with an accuracy of 95.46%, 98.2%, and 86.9%, respectively, using a Convolutional Neural Network (CNN). DeltaCharger can potentially bring a new level of charging systems and improve the prevalence of mobile autonomous robots.

【5】 Dialogue Object Search 标题：对话对象搜索

作者：Monica Roy,Kaiyu Zheng,Jason Liu,Stefanie Tellex 机构：Department of Computer Science, Brown University 备注：3 pages, 1 figure. Robotics: Science and Systems (RSS) 2021 Workshop on Robotics for People (R4P): Perspectives on Interaction, Learning and Safety. Extended Abstract 链接：https://arxiv.org/abs/2107.10653 摘要：我们设想的机器人可以与人类无缝协作和交流。这类机器人在与人类互动时，有必要决定说什么和怎么做。为此，我们引入了一个新的任务，对话对象搜索：机器人的任务是在人类环境（如厨房）中搜索目标对象（如叉子），同时与远程人类进行“视频通话”，后者对目标位置有额外但不准确的知识。也就是说，机器人与人类进行基于语音的对话，同时分享其安装摄像头的图像。这项任务在多个层面上都具有挑战性，从数据收集、算法和系统开发到评估。尽管有这些挑战，我们相信这样的任务阻碍了走向更智能和协作机器人的道路。在这个扩展的摘要中，我们激发和介绍了对话对象搜索任务，并分析了从一个试点研究中收集的例子。然后，我们讨论我们的下一步，并总结了我们希望收到反馈的几个挑战。 摘要：We envision robots that can collaborate and communicate seamlessly with humans. It is necessary for such robots to decide both what to say and how to act, while interacting with humans. To this end, we introduce a new task, dialogue object search: A robot is tasked to search for a target object (e.g. fork) in a human environment (e.g., kitchen), while engaging in a "video call" with a remote human who has additional but inexact knowledge about the target's location. That is, the robot conducts speech-based dialogue with the human, while sharing the image from its mounted camera. This task is challenging at multiple levels, from data collection, algorithm and system development,to evaluation. Despite these challenges, we believe such a task blocks the path towards more intelligent and collaborative robots. In this extended abstract, we motivate and introduce the dialogue object search task and analyze examples collected from a pilot study. We then discuss our next steps and conclude with several challenges on which we hope to receive feedback.

【6】 MobileCharger: an Autonomus Mobile Robot with Inverted Delta Actuator for Robust and Safe Robot Charging 标题：MobileCharger：一种具有倒置Delta执行器的自主移动机器人，可实现强健安全的机器人充电

作者：Iaroslav Okunevich,Daria Trinitatova,Pavel Kopanev,Dzmitry Tsetserukou 备注：Accepted to 26th International Conference on Emerging Technologies and Factory Automation (ETFA) 2021, IEEE copyright, 8 pages, 12 figures 链接：https://arxiv.org/abs/2107.10585 摘要：MobileCharger是一种新型的移动充电机器人，具有倒三角驱动器，可在两个移动机器人之间实现安全可靠的能量传输。基于RGB-D摄像机的计算机视觉系统可以利用卷积神经网络（CNN）检测目标移动机器人的电极。应用嵌入式高保真触觉传感器，基于接触面上的压力数据，利用CNN估计充电机构电极与主机器人电极之间的错位。因此，开发的视觉触觉感知系统允许执行器末端执行器的精确定位，并确保两个机器人电极之间的可靠连接。实验结果表明，CNN检测电极的平均准确率为84.2%。基于CNN的电极搜索算法试验成功率达83%，平均执行时间为60s。MobileCharger可以引入一种新的充电系统，提高自主移动机器人的普及率。 摘要：MobileCharger is a novel mobile charging robot with an Inverted Delta actuator for safe and robust energy transfer between two mobile robots. The RGB-D camera-based computer vision system allows to detect the electrodes on the target mobile robot using a convolutional neural network (CNN). The embedded high-fidelity tactile sensors are applied to estimate the misalignment between the electrodes on the charger mechanism and the electrodes on the main robot using CNN based on pressure data on the contact surfaces. Thus, the developed vision-tactile perception system allows precise positioning of the end effector of the actuator and ensures a reliable connection between the electrodes of the two robots. The experimental results showed high average precision (84.2%) for electrode detection using CNN. The percentage of successful trials of the CNN-based electrode search algorithm reached 83% and the average execution time accounted for 60 s. MobileCharger could introduce a new level of charging systems and increase the prevalence of autonomous mobile robots.

【7】 Online-Learning Deep Neuro-Adaptive Dynamic Inversion Controller for Model Free Control 标题：用于无模型控制的在线学习深度神经自适应动态逆控制器

作者：Nathan Lutes,K. Krishnamurthy,Venkata Sriram Siddhardh Nadendla,S. N. Balakrishnan 机构：∗Mechanical and Aerospace Dept., †Computer Science Dept., Missouri University of Science and Technology, Rolla, MO, USA 备注：8 pages, 4 fugures, manuscript under review for CDC'2021 链接：https://arxiv.org/abs/2107.10383 摘要：自适应方法在控制文献中很流行，因为它们在建模领域提供了灵活性和容错性。神经网络自适应控制特别有利于机器学习算法逼近未知函数的强大特性和传统自适应控制中放松某些约束的能力。深度神经网络是一种大框架网络，其逼近特性比浅层神经网络优越得多。然而，由于特定尺寸的复杂情况，例如训练中的消失/爆炸梯度，实现深度神经网络可能很困难。本文提出了一种新的权值更新算法，利用深度神经网络训练，只需加入梯度的符号，就可以避开消失/爆炸梯度问题，实现了一种神经自适应控制器。所设计的控制器是一种自适应动态逆控制器，在二次估计回路中利用一个改进的状态观测器来训练网络。深度神经网络在线学习整个工厂模型，创建完全无模型的控制器。在一个2连杆平面机器人手臂上进行了仿真验证。该控制器能快速学习非线性对象，在跟踪控制问题中表现出良好的性能。 摘要：Adaptive methods are popular within the control literature due to the flexibility and forgiveness they offer in the area of modelling. Neural network adaptive control is favorable specifically for the powerful nature of the machine learning algorithm to approximate unknown functions and for the ability to relax certain constraints within traditional adaptive control. Deep neural networks are large framework networks with vastly superior approximation characteristics than their shallow counterparts. However, implementing a deep neural network can be difficult due to size specific complications such as vanishing/exploding gradients in training. In this paper, a neuro-adaptive controller is implemented featuring a deep neural network trained on a new weight update law that escapes the vanishing/exploding gradient problem by only incorporating the sign of the gradient. The type of controller designed is an adaptive dynamic inversion controller utilizing a modified state observer in a secondary estimation loop to train the network. The deep neural network learns the entire plant model on-line, creating a controller that is completely model free. The controller design is tested in simulation on a 2 link planar robot arm. The controller is able to learn the nonlinear plant quickly and displays good performance in the tracking control problem.

【8】 Capacitated Vehicle Routing with Target Geometric Constraints 标题：目标几何约束下的容量约束车辆路径

作者：Kai Gao,Jingjin Yu 机构： Yu are with the Department of Computer Science, RutgersUniversity at New Brunswick 备注：To appear in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021) 链接：https://arxiv.org/abs/2107.10382 摘要：我们研究了机器人环境下的有能力车辆路径问题（CVRP），其中有效载荷有限的车辆必须完成交货（或提货）任务，以服务于一组具有不同需求的地理分布的客户。在经典的CVRP中，客户位置被建模为一个点。然而，在许多机器人应用中，更适合将这种“客户位置”建模为二维区域。例如，在航空运输中，无人机可能会将包裹扔到客户所在地的任何地方。这就产生了具有目标几何约束的容量车辆路径问题。计算上，CVRP已经是强NP难的；因此，CVRG更具挑战性。然而，我们为CVRG开发了快速算法，能够计算出数百个区域的高质量解。当客户区域是凸的时，我们的算法解是最优的。数值计算结果表明，本文提出的方法明显优于贪心最优优先方法。综合仿真研究证实了本文方法的有效性。 摘要：We investigate the capacitated vehicle routing problem (CVRP) under a robotics context, where a vehicle with limited payload must complete delivery (or pickup) tasks to serve a set of geographically distributed customers with varying demands. In classical CVRP, a customer location is modeled as a point. In many robotics applications, however, it is more appropriate to model such "customer locations" as 2D regions. For example, in aerial delivery, a drone may drop a package anywhere in a customer's lot. This yields the problem of CVRG (Capacitated Vehicle Routing with Target Geometric Constraints). Computationally, CVRP is already strongly NP-hard; CVRG is therefore more challenging. Nevertheless, we develop fast algorithms for CVRG, capable of computing high quality solutions for hundreds of regions. Our algorithmic solution is guaranteed to be optimal when customer regions are convex. Numerical evaluations show that our proposed methods significantly outperform greedy best-first approaches. Comprehensive simulation studies confirm the effectiveness of our methods.

【9】 Uncertainty-Aware Task Allocation for Distributed Autonomous Robots 标题：分布式自主机器人的不确定性感知任务分配

作者：Liang Sun,Leonardo Escamilla 机构： 1Liang Sun and Leonardo Escamilla are with Department of Mechanicaland Aerospace Engineering, New Mexico State University 链接：https://arxiv.org/abs/2107.10350 摘要：研究了分布式自主机器人（DARs）中具有不确定性的任务分配问题。不确定性在任务分配过程中的传播是通过使用Sigma点抽样机制的Unscented变换来完成的。在不考虑态势感知不确定性的情况下，无需对现有的任务分配方法进行修改，因此，它在通用任务分配方案中具有很大的应用潜力。提出的框架在一个模拟环境中进行了测试，在这个环境中，决策者需要确定分配给多个移动飞行机器人的多个位置的最优分配，这些机器人的位置是已知均值和协方差的随机变量。仿真结果表明，在不考虑不确定性的情况下，所提出的随机任务分配方法生成的任务总成本比随机任务分配方法低30%。 摘要：This paper addresses task-allocation problems with uncertainty in situational awareness for distributed autonomous robots (DARs). The uncertainty propagation over a task-allocation process is done by using the Unscented transform that uses the Sigma-Point sampling mechanism. It has great potential to be employed for generic task-allocation schemes, in the sense that there is no need to modify an existing task-allocation method that has been developed without considering the uncertainty in the situational awareness. The proposed framework was tested in a simulated environment where the decision-maker needs to determine an optimal allocation of multiple locations assigned to multiple mobile flying robots whose locations come as random variables of known mean and covariance. The simulation result shows that the proposed stochastic task allocation approach generates an assignment with 30% less overall cost than the one without considering the uncertainty.

【10】 Rethinking Trajectory Forecasting Evaluation 标题：关于弹道预报评估的再思考

作者：Boris Ivanovic,Marco Pavone 机构：NVIDIA Research 备注：4 pages, 2 figures 链接：https://arxiv.org/abs/2107.10297 摘要：预测其他智能体的行为是现代机器人自主堆栈的一个组成部分，特别是在具有人机交互的安全关键场景中，例如自动驾驶。反过来，人们对轨道预测有了大量的兴趣和研究，产生了各种各样的方法。然而，所有工作的共同点是使用相同的基于精度的评估指标，例如位移误差和对数似然。虽然这些指标是信息性的，但它们是任务不可知的，被评估为相等的预测可能会导致截然不同的结果，例如在下游规划和决策中。在这项工作中，我们后退了一步，并对当前的轨迹预测指标进行了批判性评估，提出了任务感知指标，作为部署预测的系统中性能的更好度量。此外，我们还提供了一个这样一个指标的例子，将规划意识纳入现有的轨迹预测指标中。 摘要：Forecasting the behavior of other agents is an integral part of the modern robotic autonomy stack, especially in safety-critical scenarios with human-robot interaction, such as autonomous driving. In turn, there has been a significant amount of interest and research in trajectory forecasting, resulting in a wide variety of approaches. Common to all works, however, is the use of the same few accuracy-based evaluation metrics, e.g., displacement error and log-likelihood. While these metrics are informative, they are task-agnostic and predictions that are evaluated as equal can lead to vastly different outcomes, e.g., in downstream planning and decision making. In this work, we take a step back and critically evaluate current trajectory forecasting metrics, proposing task-aware metrics as a better measure of performance in systems where prediction is being deployed. We additionally present one example of such a metric, incorporating planning-awareness within existing trajectory forecasting metrics.