机器人相关学术速递[7.16]

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cs.RO机器人相关，共计21篇

【1】 Optimization-Based Quadrupedal Hybrid Wheeled-Legged Locomotion 标题：基于优化的四足混合轮腿运动

作者：Italo Belli,Matteo Parigi Polverini,Arturo Laurenzi,Enrico Mingo Hoffman,Paolo Rocco,Nikolaos Tsagarakis 备注：Presented at Humanoids 2020 链接：https://arxiv.org/abs/2107.07507 摘要：混合轮式腿部运动是一种导航模式，最近才被新颖的机器人设计所开启，例如半人马型人形半人马座[1]或四足动物ANYmal[2]，其配置以不可操纵的轮子为特征。混合运动一词在下文中用于表示一种特殊类型的运动，通过同时协调使用腿和轮子来实现，见图1。这种选择源于腿运动和简单的轮式导航之间的交叉点，为了从这两种技术中获得最佳效果：第一种是敏捷性和穿越不平坦地形的能力，第二种是速度和稳定性。因此，混合机器人的可行轨迹规划问题与腿部运动问题有许多相似之处：同样，在混合情况下，考虑到带轮的脚只能在地面上推动而不能拉动地面，基础的运动是通过脚与环境的接触来实现的。必须考虑与摩擦锥兼容的力，而接触件可以沿着车轮方向规定的方向滑动。 摘要：Hybrid wheeled-legged locomotion is a navigation paradigm only recently opened up by novel robotic designs,e.g. the centaur-type humanoid CENTAURO [1] or the quadruped ANYmal [2] in its configuration featuring non-steerable wheels. The term Hybrid Locomotion is hereafter used to indicate a particular type of locomotion, achieved with simultaneous and coordinate use of legs and wheels,see Fig. 1. Such choice stems at the intersection between legged locomotion and the simpler wheeled navigation, in order to get the best from both techniques: agility and ability to traverse uneven terrains from the first, speed and stability from the second. As a consequence, the problem of planning feasible trajectories for a hybrid robot shares many similarities with the legged locomotion problem: also in the hybrid case the motion of the base is reached through contact of the feet with the environment, taking into account that the wheeled feet can just push on the ground and not pull it. Forces compatible with friction cones have to be considered, while the contacts can slide just along the direction prescribed by the orientation of the wheels.

【2】 An End-to-End Differentiable Framework for Contact-Aware Robot Design 标题：一种端到端可区分的接触式机器人设计框架

作者：Jie Xu,Tao Chen,Lara Zlokapa,Michael Foshey,Wojciech Matusik,Shinjiro Sueda,Pulkit Agrawal 机构：†Massachusetts Institute of Technology, ‡Texas A&M University 备注：Robotics: Science and Systems 链接：https://arxiv.org/abs/2107.07501 摘要：目前机器人操作的主流模式包括两个独立的阶段：机械手设计和控制。由于机器人的形态和控制方式密切相关，因此设计和控制的联合优化可以显著提高机器人的性能。现有的协同优化方法存在局限性，无法探索丰富的设计空间。主要原因是在复杂的设计之间的权衡，这对于接触丰富的任务是必要的，与实际的限制，制造，优化，接触处理等。我们克服了这些挑战，通过建立一个端到端可微框架接触机器人的设计。这个框架的两个关键组成部分是：一个新的基于变形的参数化，允许设计具有任意复杂几何结构的关节刚性机器人，以及一个可微刚体模拟器，可以处理接触丰富的情况，并计算分析梯度的运动学和动力学参数的全谱。在多个操作任务上，我们的框架优于现有的方法，这些方法要么只针对控制进行优化，要么针对使用替代表示的设计进行优化，要么使用无梯度方法进行协同优化。 摘要：The current dominant paradigm for robotic manipulation involves two separate stages: manipulator design and control. Because the robot's morphology and how it can be controlled are intimately linked, joint optimization of design and control can significantly improve performance. Existing methods for co-optimization are limited and fail to explore a rich space of designs. The primary reason is the trade-off between the complexity of designs that is necessary for contact-rich tasks against the practical constraints of manufacturing, optimization, contact handling, etc. We overcome several of these challenges by building an end-to-end differentiable framework for contact-aware robot design. The two key components of this framework are: a novel deformation-based parameterization that allows for the design of articulated rigid robots with arbitrary, complex geometry, and a differentiable rigid body simulator that can handle contact-rich scenarios and computes analytical gradients for a full spectrum of kinematic and dynamic parameters. On multiple manipulation tasks, our framework outperforms existing methods that either only optimize for control or for design using alternate representations or co-optimize using gradient-free methods.

【3】 Rule-based Evaluation and Optimal Control for Autonomous Driving 标题：基于规则的自动驾驶评价与最优控制

作者：Wei Xiao,Noushin Mehdipour,Anne Collin,Amitai Y. Bin-Nun,Emilio Frazzoli,Radboud Duintjer Tebbens,Calin Belta 备注：under review in TAC, 16 pages. arXiv admin note: substantial text overlap with arXiv:2101.05709 链接：https://arxiv.org/abs/2107.07460 摘要：我们为自动驾驶车辆（AVs）开发了最优控制策略，这些车辆需要满足作为道路规则（ROTR）的复杂规范和当地特定文化对合理驾驶行为的期望。我们将这些规范制定为规则，并通过构造一个优先级结构来指定它们的优先级，称为\underline{T}otal\underline{OR}der over e\underline{Q}uivalence classes（TORQ）。我们提出了一个递归框架，其中优先级结构中规则的满足度按优先级的相反顺序迭代放松。该框架的核心是一个最优控制问题，通过控制Lyapunov函数（CLFs）收敛到期望状态，通过控制屏障函数（CBFs）实现与其他道路使用者的通行。我们提出了解决这个问题的离线和在线方法。在后者中，AV具有有限的感知范围，影响规则的激活，并且控制是使用滚动时域（Model Predictive control，MPC）方法生成的。我们还展示了离线方法如何用于事后（离线）轨迹的通过/失败评估-如果我们能找到一个控制器生成一个导致较少违反规则优先级结构的轨迹，那么给定的轨迹将被拒绝。我们提出了多个驾驶场景的案例研究，以证明算法的有效性，并比较了我们提出的框架的离线和在线版本。 摘要：We develop optimal control strategies for autonomous vehicles (AVs) that are required to meet complex specifications imposed as rules of the road (ROTR) and locally specific cultural expectations of reasonable driving behavior. We formulate these specifications as rules, and specify their priorities by constructing a priority structure, called \underline{T}otal \underline{OR}der over e\underline{Q}uivalence classes (TORQ). We propose a recursive framework, in which the satisfaction of the rules in the priority structure are iteratively relaxed in reverse order of priority. Central to this framework is an optimal control problem, where convergence to desired states is achieved using Control Lyapunov Functions (CLFs) and clearance with other road users is enforced through Control Barrier Functions (CBFs). We present offline and online approaches to this problem. In the latter, the AV has limited sensing range that affects the activation of the rules, and the control is generated using a receding horizon (Model Predictive Control, MPC) approach. We also show how the offline method can be used for after-the-fact (offline) pass/fail evaluation of trajectories - a given trajectory is rejected if we can find a controller producing a trajectory that leads to less violation of the rule priority structure. We present case studies with multiple driving scenarios to demonstrate the effectiveness of the algorithms, and to compare the offline and online versions of our proposed framework.

【4】 GI-NNet \& RGI-NNet: Development of Robotic Grasp Pose Models, Trainable with Large as well as Limited Labelled Training Datasets, under supervised and semi supervised paradigms

作者：Priya Shukla,Nilotpal Pramanik,Deepesh Mehta,G. C. Nandi 机构：IndianInstituteofInformationTechnology 链接：https://arxiv.org/abs/2107.07452 摘要：我们抓取物体的方法对于cobot的高效、智能和最优抓取是一个挑战。为了简化这个过程，这里我们使用深度学习技术来帮助机器人学习快速生成和执行适当的抓取。我们开发了一个生成性初始神经网络（GI-NNet）模型，能够生成机器人对可见和不可见物体的反足抓取。该方法在康奈尔抓取数据集（CGD）上训练，对RGB深度（RGB-D）图像中规则形状和不规则形状的物体的抓取姿态精度达到98.87%，同时只需要现有方法的三分之一的网络可训练参数。然而，为了达到这一性能水平，该模型需要整个CGD的90%可用标记数据只保留10%的标记数据进行测试，这使得它容易受到较差的泛化。此外，获得足够的和高质量的标记数据集越来越难以跟上庞大网络的需求。为了解决这些问题，我们将我们的模型作为一个具有半监督学习结构的解码器，称为矢量量化变分自动编码器（VQVAE），当使用可用的标记数据和未标记数据进行训练时，它可以有效地工作。提出的模型，我们称之为基于表示的GI-NNet（RGI-NNet），在CGD上用不同的标签数据分裂训练，最小为10%的标记数据集，由VQVAE生成的潜在嵌入高达50%的标记数据，由VQVAE获得的潜在嵌入。RGI-NNet的抓取位姿精度在92.13%～95.6%之间，远远优于现有的几种仅用标记数据集训练的模型。为了验证GI-NNet和RGI-NNet模型的性能，我们使用了Anukul（Baxter）硬件cobot。 摘要：Our way of grasping objects is challenging for efficient, intelligent and optimal grasp by COBOTs. To streamline the process, here we use deep learning techniques to help robots learn to generate and execute appropriate grasps quickly. We developed a Generative Inception Neural Network (GI-NNet) model, capable of generating antipodal robotic grasps on seen as well as unseen objects. It is trained on Cornell Grasping Dataset (CGD) and attained 98.87% grasp pose accuracy for detecting both regular and irregular shaped objects from RGB-Depth (RGB-D) images while requiring only one third of the network trainable parameters as compared to the existing approaches. However, to attain this level of performance the model requires the entire 90% of the available labelled data of CGD keeping only 10% labelled data for testing which makes it vulnerable to poor generalization. Furthermore, getting sufficient and quality labelled dataset is becoming increasingly difficult keeping in pace with the requirement of gigantic networks. To address these issues, we attach our model as a decoder with a semi-supervised learning based architecture known as Vector Quantized Variational Auto Encoder (VQVAE), which works efficiently when trained both with the available labelled and unlabelled data. The proposed model, which we name as Representation based GI-NNet (RGI-NNet), has been trained with various splits of label data on CGD with as minimum as 10% labelled dataset together with latent embedding generated from VQVAE up to 50% labelled data with latent embedding obtained from VQVAE. The performance level, in terms of grasp pose accuracy of RGI-NNet, varies between 92.13% to 95.6% which is far better than several existing models trained with only labelled dataset. For the performance verification of both GI-NNet and RGI-NNet models, we use Anukul (Baxter) hardware cobot.

【5】 Personalizing User Engagement Dynamics in a Non-Verbal Communication Game for Cerebral Palsy 标题：脑瘫非语言交流游戏中的个性化用户参与动态

作者：Nathaniel Dennler,Catherine Yunis,Jonathan Realmuto,Terence Sanger,Stefanos Nikolaidis,Maja Matarić 机构： and Maja Matari´c are fromthe Computer Science Department at the University of Southern Cali-fornia in Los Angeles, edu 2CatherineYunisisfromtheBiomedicalEngineeringDepart-ment at the University of Southern California in Los Angeles 备注：7 pages, 6 figures. Accepted to IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN) 2021 链接：https://arxiv.org/abs/2107.07446 摘要：脑性瘫痪（CP）的儿童和成人可能由于其运动障碍的症状而出现不自主的上肢运动，导致与看护者和同伴沟通困难。我们描述了一个社会辅助机器人如何帮助患有脑瘫的个体在一对一的数字猜谜游戏中使用主动矫形器练习非语言交流手势。我们对CP患者进行了用户研究和数据收集；我们发现参与者更喜欢一个具体化的机器人而不是一个基于屏幕的代理，并且我们使用参与者数据来训练参与者参与动力学的个性化模型，这些模型可以用来选择个性化的机器人动作。我们的工作强调了个性化模型在使用社交辅助机器人的CP用户参与中的优势，并为这一领域的未来工作提供了设计见解。 摘要：Children and adults with cerebral palsy (CP) can have involuntary upper limb movements as a consequence of the symptoms that characterize their motor disability, leading to difficulties in communicating with caretakers and peers. We describe how a socially assistive robot may help individuals with CP to practice non-verbal communicative gestures using an active orthosis in a one-on-one number-guessing game. We performed a user study and data collection with participants with CP; we found that participants preferred an embodied robot over a screen-based agent, and we used the participant data to train personalized models of participant engagement dynamics that can be used to select personalized robot actions. Our work highlights the benefit of personalized models in the engagement of users with CP with a socially assistive robot and offers design insights for future work in this area.

【6】 High-level Decisions from a Safe Maneuver Catalog with Reinforcement Learning for Safe and Cooperative Automated Merging 标题：采用强化学习的安全协作自动合并的安全机动目录中的高级决策

作者：Danial Kamran,Yu Ren,Martin Lauer 机构： al in 1Authors are with Institute of Measurement and Control Systems, Karlsruhe Institute of Technology (KIT) 链接：https://arxiv.org/abs/2107.07413 摘要：强化学习（RL）最近被用于解决自动驾驶环境下具有挑战性的决策问题。然而，所提出的基于RL的策略的主要缺点之一是缺乏安全保证，因为它们努力减少预期的碰撞次数，但仍然容忍碰撞。在本文中，我们提出了一个有效的基于RL的决策管道，用于合并场景中的安全和协作自动驾驶。RL代理能够预测当前情况并提供高级决策，指定负责安全的低级计划员的操作模式。为了学习一个更通用的策略，我们提出了一个可扩展的RL架构，用于对环境配置变化不敏感的合并场景。实验结果表明，所提出的RL智能体能够有效地从车辆状态历史中识别出合作驾驶员，并生成交互操作，从而实现更快、更舒适的自动驾驶。同时，由于计划者内部的安全约束，所有的机动都是无碰撞和安全的。 摘要：Reinforcement learning (RL) has recently been used for solving challenging decision-making problems in the context of automated driving. However, one of the main drawbacks of the presented RL-based policies is the lack of safety guarantees, since they strive to reduce the expected number of collisions but still tolerate them. In this paper, we propose an efficient RL-based decision-making pipeline for safe and cooperative automated driving in merging scenarios. The RL agent is able to predict the current situation and provide high-level decisions, specifying the operation mode of the low level planner which is responsible for safety. In order to learn a more generic policy, we propose a scalable RL architecture for the merging scenario that is not sensitive to changes in the environment configurations. According to our experiments, the proposed RL agent can efficiently identify cooperative drivers from their vehicle state history and generate interactive maneuvers, resulting in faster and more comfortable automated driving. At the same time, thanks to the safety constraints inside the planner, all of the maneuvers are collision free and safe.

【7】 Minimizing Safety Interference for Safe and Comfortable Automated Driving with Distributional Reinforcement Learning 标题：分布式强化学习最小化安全舒适自动驾驶的安全干扰

作者：Danial Kamran,Tizian Engelgeh,Marvin Busch,Johannes Fischer,Christoph Stiller 机构： Although the learned policies are prevented to 1AuthorsarewithInstituteofMeasurementandControlSystems, Karlsruhe Institute of Technology (KIT) 链接：https://arxiv.org/abs/2107.07316 摘要：尽管近年来强化学习（RL）取得了一些进展，但其在自主车辆等安全关键领域的应用仍然具有挑战性。尽管在危险情况下惩罚RL代理有助于学习安全策略，但也可能导致高度保守的行为。在本文中，我们提出了一个分布式RL框架来学习自适应策略，该策略可以根据期望的舒适度和效用在运行时调整其保守性水平。通过使用主动安全验证方法，该框架可以保证由RL生成的动作在最坏情况下是故障安全的。同时，鼓励该政策将安全干扰降至最低，并产生更舒适的行为。我们使用一个高级模拟器对所提出的方法和基线策略进行了训练和评估，该模拟器具有多种随机场景，包括一些在现实中很少发生但非常关键的角落案例。根据我们的实验，使用分布式RL学习的策略行为在运行时是自适应的，并且对环境的不确定性是鲁棒的。从数量上讲，学习的分布式RL代理比普通的DQN策略平均快8秒，需要的安全干扰比基于规则的策略少83\%，平均交叉时间略有增加。我们还研究了学习策略在高感知噪声环境下的敏感性，结果表明，在感知噪声比训练配置高两倍的情况下，我们的算法学习到的策略仍然可以可靠地驾驶，用于闭塞交叉口的自动合并和交叉。 摘要：Despite recent advances in reinforcement learning (RL), its application in safety critical domains like autonomous vehicles is still challenging. Although punishing RL agents for risky situations can help to learn safe policies, it may also lead to highly conservative behavior. In this paper, we propose a distributional RL framework in order to learn adaptive policies that can tune their level of conservativity at run-time based on the desired comfort and utility. Using a proactive safety verification approach, the proposed framework can guarantee that actions generated from RL are fail-safe according to the worst-case assumptions. Concurrently, the policy is encouraged to minimize safety interference and generate more comfortable behavior. We trained and evaluated the proposed approach and baseline policies using a high level simulator with a variety of randomized scenarios including several corner cases which rarely happen in reality but are very crucial. In light of our experiments, the behavior of policies learned using distributional RL can be adaptive at run-time and robust to the environment uncertainty. Quantitatively, the learned distributional RL agent drives in average 8 seconds faster than the normal DQN policy and requires 83\% less safety interference compared to the rule-based policy with slightly increasing the average crossing time. We also study sensitivity of the learned policy in environments with higher perception noise and show that our algorithm learns policies that can still drive reliable when the perception noise is two times higher than the training configuration for automated merging and crossing at occluded intersections.

【8】 A Low-Complexity Radar Detector Outperforming OS-CFAR for Indoor Drone Obstacle Avoidance 标题：一种性能优于OS-CFAR的低复杂度雷达室内避障检测器

作者：Ali Safa,Tim Verbelen,Lars Keuninckx,Ilja Ocket,Matthias Hartmann,André Bourdoux,Franky Catthoor,Georges Gielen 机构：Francky Catthoor, Fellow, IEEE, Georges G.E. Gielen, Fellow, IEEE 链接：https://arxiv.org/abs/2107.07250 摘要：随着雷达传感器的小型化，将其用于室内无人机避障等室内传感应用越来越受到关注。在这些新的场景中，雷达必须在密集场景中有大量的相邻散射体。雷达性能的核心是用于从背景噪声和杂波中分离目标的检测算法。传统上，大多数雷达系统都使用传统的恒虚警检测器，但是在有许多反射器的室内场景中，它们的性能会下降。受非线性目标检测进展的启发，我们提出了一种新的高性能、低复杂度的目标检测算法，并在无人机雷达数据集上进行了实验验证。我们的实验表明，我们提出的算法大大优于OS-CFAR（标准检测器用于汽车系统）为我们的具体任务室内无人机导航与超过19%的检测概率高的概率为一个给定的假警报。我们还将我们提出的检测器与最近提出的多目标恒虚警检测器进行了比较，结果表明，与CHA-CFAR相比，检测概率提高了16%，在我们特定的室内场景中，与OR-CFAR和TS-LNCFAR相比，检测概率甚至有更大的提高。对于我们目前最先进的低复杂度探测工作来说，这是提高雷达探测性能的关键。 摘要：As radar sensors are being miniaturized, there is a growing interest for using them in indoor sensing applications such as indoor drone obstacle avoidance. In those novel scenarios, radars must perform well in dense scenes with a large number of neighboring scatterers. Central to radar performance is the detection algorithm used to separate targets from the background noise and clutter. Traditionally, most radar systems use conventional CFAR detectors but their performance degrades in indoor scenarios with many reflectors. Inspired by the advances in non-linear target detection, we propose a novel high-performance, yet low-complexity target detector and we experimentally validate our algorithm on a dataset acquired using a radar mounted on a drone. We experimentally show that our proposed algorithm drastically outperforms OS-CFAR (standard detector used in automotive systems) for our specific task of indoor drone navigation with more than 19% higher probability of detection for a given probability of false alarm. We also benchmark our proposed detector against a number of recently proposed multi-target CFAR detectors and show an improvement of 16% in probability of detection compared to CHA-CFAR, with even larger improvements compared to both OR-CFAR and TS-LNCFAR in our particular indoor scenario. To the best of our knowledge, this work improves the state of the art for high-performance yet low-complexity radar detection in critical indoor sensing applications.

【9】 VILENS: Visual, Inertial, Lidar, and Leg Odometry for All-Terrain Legged Robots 标题：VILENS：用于全地形腿式机器人的视觉、惯性、激光雷达和腿部里程计

作者：David Wisth,Marco Camurri,Maurice Fallon 机构：Oxford Robotics Institute, University of Oxford, Oxford, UK 备注：Video: this https URL 链接：https://arxiv.org/abs/2107.07243 摘要：提出了一种基于因子图的腿式机器人视觉惯性激光雷达（VILENS）里程计系统。关键的创新点是四种不同传感器模式的紧密融合，以实现可靠的操作，否则单个传感器将产生退化估计。为了减小腿部里程漂移，我们用在线估计的线速度偏差项来扩展机器人的状态。这种偏差只有在预积分速度因子与视觉、激光雷达和IMU因子紧密融合的情况下才能观察到。在ANYmal四足机器人上进行了广泛的实验验证，总持续时间为2小时，行程为1.8公里。实验包括在松散的岩石、斜坡和泥土上的动态运动；这些挑战包括感知挑战，如黑暗和灰尘地下洞穴或开放，缺乏特征的地区，以及流动性挑战，如滑动和地形变形。与最先进的松耦合方法相比，我们平均提高了62%的平移误差和51%的旋转误差。为了证明其鲁棒性，VILENS还集成了感知控制器和局部路径规划器。 摘要：We present VILENS (Visual Inertial Lidar Legged Navigation System), an odometry system for legged robots based on factor graphs. The key novelty is the tight fusion of four different sensor modalities to achieve reliable operation when the individual sensors would otherwise produce degenerate estimation. To minimize leg odometry drift, we extend the robot's state with a linear velocity bias term which is estimated online. This bias is only observable because of the tight fusion of this preintegrated velocity factor with vision, lidar, and IMU factors. Extensive experimental validation on the ANYmal quadruped robots is presented, for a total duration of 2 h and 1.8 km traveled. The experiments involved dynamic locomotion over loose rocks, slopes, and mud; these included perceptual challenges, such as dark and dusty underground caverns or open, feature-deprived areas, as well as mobility challenges such as slipping and terrain deformation. We show an average improvement of 62% translational and 51% rotational errors compared to a state-of-the-art loosely coupled approach. To demonstrate its robustness, VILENS was also integrated with a perceptive controller and a local path planner.

【10】 Design of Distributed Reconfigurable Robotics Systems with ReconROS 标题：基于RECOROS的分布式可重构机器人系统设计

作者：Christian Lienen,Marco Platzner 机构： Paderborn University 备注：Paper is under review 链接：https://arxiv.org/abs/2107.07208 摘要：机器人应用程序实时处理大量数据，需要提供高性能和能效的计算平台。FPGA非常适合于许多这样的应用，但是由于设计复杂性的增加和缺乏跨软件/硬件边界的一致编程模型，机器人界不愿意使用硬件加速。本文提出了一个将广泛应用的机器人操作系统（ROS）与可重构计算机的软硬件线程进行多线程编程的框架ReconROS。这种独特的组合使ROS2开发者能够灵活地透明地加速他们的机器人应用程序的硬件部分。我们详细阐述了ReconROS的体系结构和设计流程，并报告了一组实验，强调了我们方法的可行性和灵活性。 摘要：Robotics applications process large amounts of data in real-time and require compute platforms that provide high performance and energy-efficiency. FPGAs are well-suited for many of these applications, but there is a reluctance in the robotics community to use hardware acceleration due to increased design complexity and a lack of consistent programming models across the software/hardware boundary. In this paper we present ReconROS, a framework that integrates the widely-used robot operating system (ROS) with ReconOS, which features multithreaded programming of hardware and software threads for reconfigurable computers. This unique combination gives ROS2 developers the flexibility to transparently accelerate parts of their robotics applications in hardware. We elaborate on the architecture and the design flow for ReconROS and report on a set of experiments that underline the feasibility and flexibility of our approach.

【11】 Real-Time Grasping Strategies Using Event Camera 标题：使用事件摄像机的实时抓取策略

作者：Xiaoqian Huang,Mohamad Halwani,Rajkumar Muthusamy,Abdulla Ayyad,Dewald Swart,Lakmal Seneviratne,Dongming Gan,Yahya Zweiri 机构：· Mohamad, Halwani, · Dongming, Gan, Received: date Accepted: date 备注：37 pages 链接：https://arxiv.org/abs/2107.07200 摘要：机器人视觉在抓取应用中起着感知环境的关键作用。然而，传统的基于帧的机器人视觉存在着运动模糊和采样率低等问题，难以满足不断发展的工业自动化需求。该文首次提出了一种基于事件的机器人抓取框架，用于处理杂乱场景中的多个已知和未知物体。与标准的基于帧的视觉相比，神经形态视觉具有微秒级采样率和无运动模糊的优点。在此基础上，分别提出了基于模型和无模型的已知和未知物体抓取方法。在基于模型的方法中，采用基于事件的多视图方法对场景中的目标进行定位，然后通过点云处理对目标进行聚类和配准。与此不同，本文提出的无模型方法利用基于事件的目标分割、视觉伺服和抓取规划来定位、对齐和抓取目标对象。所提出的方法在不同尺寸的物体上进行了实验验证，使用了一个UR10机器人，该机器人带有一个手持式神经摄像机和一个巴雷特手爪。此外，在弱光环境下验证了两种基于事件的抓取方法的鲁棒性。这种低光操作能力显示了一个很大的优势，抓取使用标准框架为基础的视觉。此外，与所提出的基于模型的方法相比，所提出的无模型方法在处理未知对象时不需要先验知识。 摘要：Robotic vision plays a key role for perceiving the environment in grasping applications. However, the conventional framed-based robotic vision, suffering from motion blur and low sampling rate, may not meet the automation needs of evolving industrial requirements. This paper, for the first time, proposes an event-based robotic grasping framework for multiple known and unknown objects in a cluttered scene. Compared with standard frame-based vision, neuromorphic vision has advantages of microsecond-level sampling rate and no motion blur. Building on that, the model-based and model-free approaches are developed for known and unknown objects' grasping respectively. For the model-based approach, event-based multi-view approach is used to localize the objects in the scene, and then point cloud processing allows for the clustering and registering of objects. Differently, the proposed model-free approach utilizes the developed event-based object segmentation, visual servoing and grasp planning to localize, align to, and grasp the targeting object. The proposed approaches are experimentally validated with objects of different sizes, using a UR10 robot with an eye-in-hand neuromorphic camera and a Barrett hand gripper. Moreover, the robustness of the two proposed event-based grasping approaches are validated in a low-light environment. This low-light operating ability shows a great advantage over the grasping using the standard frame-based vision. Furthermore, the developed model-free approach demonstrates the advantage of dealing with unknown object without prior knowledge compared to the proposed model-based approach.

【12】 MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning 标题：壁画：结果驱动强化学习的元学习不确定性感知奖励

作者：Kevin Li,Abhishek Gupta,Ashwin Reddy,Vitchyr Pong,Aurick Zhou,Justin Yu,Sergey Levine 机构：Equal contribution 1Department of Electrical Engineering andComputer Sciences 备注：Accepted to ICML 2021. First two authors contributed equally 链接：https://arxiv.org/abs/2107.07184 摘要：强化学习中的探索是一个具有挑战性的问题：在最坏的情况下，代理必须搜索可能隐藏在状态空间任何地方的奖励状态。我们是否可以定义一类更容易处理的RL问题，在这种情况下，向代理提供成功结果的示例？在这个问题设置中，通过训练分类器将状态分类为成功与否，可以自动获得奖励函数。如果训练得当，这样的分类器不仅可以提供奖励功能，而且实际上可以提供一个良好的客观景观，既可以促进向良好状态的进展，又可以提供校准的探索奖励。在这项工作中，我们证明了一个不确定性感知分类器可以解决具有挑战性的强化学习问题，既鼓励探索，又为积极的结果提供指导。我们提出了一种新的机制来获得这些校准的，不确定性感知的分类器，基于一种用于计算归一化最大似然（NML）分布的摊销技术，还展示了如何利用元学习工具使这些技术在计算上易于处理。我们证明了所得到的算法与基于计数的探索方法和用于学习奖励函数的先验算法有许多有趣的联系，同时也为目标提供了更有效的指导。我们证明了我们的算法解决了许多具有挑战性的导航和机器人操作任务，这些任务对于以前的方法来说是困难或不可能的。 摘要：Exploration in reinforcement learning is a challenging problem: in the worst case, the agent must search for reward states that could be hidden anywhere in the state space. Can we define a more tractable class of RL problems, where the agent is provided with examples of successful outcomes? In this problem setting, the reward function can be obtained automatically by training a classifier to categorize states as successful or not. If trained properly, such a classifier can not only afford a reward function, but actually provide a well-shaped objective landscape that both promotes progress toward good states and provides a calibrated exploration bonus. In this work, we we show that an uncertainty aware classifier can solve challenging reinforcement learning problems by both encouraging exploration and provided directed guidance towards positive outcomes. We propose a novel mechanism for obtaining these calibrated, uncertainty-aware classifiers based on an amortized technique for computing the normalized maximum likelihood (NML) distribution, also showing how these techniques can be made computationally tractable by leveraging tools from meta-learning. We show that the resulting algorithm has a number of intriguing connections to both count-based exploration methods and prior algorithms for learning reward functions, while also providing more effective guidance towards the goal. We demonstrate that our algorithm solves a number of challenging navigation and robotic manipulation tasks which prove difficult or impossible for prior methods.

【13】 Conflict-free Cooperation Method for Connected and Automated Vehicles at Unsignalized Intersections: Graph-based Modeling and Optimality Analysis 标题：无信号交叉口连通自动车辆无冲突协作方法：基于图的建模与优化分析

作者：Chaoyi Chen,Qing Xu,Mengchi Cai,Jiawei Wang,Jianqiang Wang,Biao Xu,Keqiang Li 机构：School of Vehicle and Mobility, Tsinghua University, Beijing , China, Tsinghua University-Didi Joint Research Center for Future Mobility, Beijing , China, College of Mechanical and Vehicle Engineering, Hunan University, Changsha , China. 链接：https://arxiv.org/abs/2107.07179 摘要：联网和自动化车辆在改善交通流动性和减少排放方面显示出巨大的潜力，特别是在无信号交叉口。已有研究表明，车辆通过顺序是影响交叉口交通流动性的关键因素。本文提出了一种基于图的协作方法来形式化无信号交叉口无冲突调度问题。基于图形分析，将车辆轨迹冲突关系建模为冲突有向图和共存无向图。在此基础上，提出了两种基于图的车辆通过顺序求解方法。第一种是一种改进的深度优先生成树算法，其目标是逐辆寻找局部最优通过顺序。另一种新方法是最小团覆盖算法，它识别全局最优解。最后，提出了一种分布式控制框架和通信拓扑结构，实现了车辆之间的无冲突协作。对不同的车辆数和交通量进行了大量的数值仿真，仿真结果证明了算法的有效性。 摘要：Connected and automated vehicles have shown great potential in improving traffic mobility and reducing emissions, especially at unsignalized intersections. Previous research has shown that vehicle passing order is the key influencing factor in improving intersection traffic mobility. In this paper, we propose a graph-based cooperation method to formalize the conflict-free scheduling problem at an unsignalized intersection. Based on graphical analysis, a vehicle's trajectory conflict relationship is modeled as a conflict directed graph and a coexisting undirected graph. Then, two graph-based methods are proposed to find the vehicle passing order. The first is an improved depth-first spanning tree algorithm, which aims to find the local optimal passing order vehicle by vehicle. The other novel method is a minimum clique cover algorithm, which identifies the global optimal solution. Finally, a distributed control framework and communication topology are presented to realize the conflict-free cooperation of vehicles. Extensive numerical simulations are conducted for various numbers of vehicles and traffic volumes, and the simulation results prove the effectiveness of the proposed algorithms.

【14】 A life-long SLAM approach using adaptable local maps based on rasterized LIDAR images 标题：基于光栅化LIDAR图像的自适应局部地图终身SLAM方法

作者：Waqas Ali,Peilin Liu,Rendong Ying,Zheng Gong 机构： BOWhas proven to be an efficient method for content-based imageThe authors are with The School of Electronic Information andElectrical Engineering, Shanghai Jiaotong University 链接：https://arxiv.org/abs/2107.07133 摘要：大多数实时自主机器人应用都需要机器人在动态空间中长时间地穿行。在某些情况下，机器人需要在相同的环境中工作。这样的应用产生了一个终身的大满贯系统的问题。终身大满贯提出了两个主要的挑战，即跟踪不应在动态环境中失败和需要一个强大而有效的映射策略。系统应使用新信息更新地图；同时也记录了以前的观察结果。但是，长时间的映射可能需要更高的计算要求。本文提出了一种解决终身大满贯问题的方法。我们将全局地图表示为一组本地地图的光栅化图像，以及负责更新本地地图和跟踪旧值的地图管理系统。我们还提出了一种有效的方法，使用袋视觉文字的方法，循环关闭检测和再定位。我们在KITTI数据集和室内数据集上评估了系统的性能。我们的闭环系统报告召回率和准确率超过90%。与最先进的方法相比，我们系统的计算成本要低得多。我们的方法报告更低的计算要求，即使是长期运行。 摘要：Most real-time autonomous robot applications require a robot to traverse through a dynamic space for a long time. In some cases, a robot needs to work in the same environment. Such applications give rise to the problem of a life-long SLAM system. Life-long SLAM presents two main challenges i.e. the tracking should not fail in a dynamic environment and the need for a robust and efficient mapping strategy. The system should update maps with new information; while also keeping track of older observations. But, mapping for a long time can require higher computational requirements. In this paper, we propose a solution to the problem of life-long SLAM. We represent the global map as a set of rasterized images of local maps along with a map management system responsible for updating local maps and keeping track of older values. We also present an efficient approach of using the bag of visual words method for loop closure detection and relocalization. We evaluate the performance of our system on the KITTI dataset and an indoor dataset. Our loop closure system reported recall and precision of above 90 percent. The computational cost of our system is much lower as compared to state-of-the-art methods. Our method reports lower computational requirements even for long-term operation.

【15】 Collision Avoidance Using Spherical Harmonics 标题：利用球面谐波进行避碰

作者：Steven Patrick,Efstathios Bakolas 机构：∗ The University of Texas at Austin, Austin, Texas ,- 备注：6 pages, MECC 2021 链接：https://arxiv.org/abs/2107.07117 摘要：本文提出了一种新的基于优化的轨迹规划方法，利用球谐函数来估计agent周围的无碰撞解空间。在给定的时间步长下，利用约束超定最小二乘估计来确定定义球谐近似的参数。由于球面谐波产生星形凸形，规划者可以考虑在给定半径内的代理的所有视距路径。这与其他最先进的规划师形成对比，规划师通过粗略近似估计障碍物边界，并使用启发式规则将解空间修剪为易于探索的解空间，从而生成轨迹。这些方法使得轨迹规划器在代理必须接近障碍物才能完成目标的环境中过于保守。我们的方法与其他路径规划器的性能相当，并且在某些环境下优于这些规划器。它在实时运行的同时生成可行的轨迹，在有效解存在的情况下保证安全。 摘要：In this paper, we propose a novel optimization-based trajectory planner that utilizes spherical harmonics to estimate the collision-free solution space around an agent. The space is estimated using a constrained over-determined least-squares estimator to determine the parameters that define a spherical harmonic approximation at a given time step. Since spherical harmonics produce star-convex shapes, the planner can consider all paths that are in line-of-sight for the agent within a given radius. This contrasts with other state-of-the-art planners that generate trajectories by estimating obstacle boundaries with rough approximations and using heuristic rules to prune a solution space into one that can be easily explored. Those methods cause the trajectory planner to be overly conservative in environments where an agent must get close to obstacles to accomplish a goal. Our method is shown to perform on-par with other path planners and surpass these planners in certain environments. It generates feasible trajectories while still running in real-time and guaranteeing safety when a valid solution exists.

【16】 On nondeterminism in combinatorial filters 标题：论组合滤波器中的不确定性

作者：Yulin Zhang,Dylan A. Shell 机构： Shell are with Department of ComputerScience & Engineering, Texas A&M University 备注：5 figures 链接：https://arxiv.org/abs/2107.07111 摘要：组合滤波问题源于机器人的资源优化问题；这是一个具体的方式，自动化可以帮助实现最低限度，建立更好，更简单的机器人。本文给出了滤波器极小化的一个新定义，它比以前的定义更广泛，允许滤波器（输入、输出或两者）是不确定的。这大大改变了问题。非确定性过滤器能够重用状态，从本质上说，每个顶点获得更多的“行为”。我们表明，差距的大小可以是显着的（大于多项式），这表明这种情况下通常会比确定性问题更具挑战性。事实上，本文建立的核心计算复杂性结果支持了这一点：产生不确定极小值是PSPACE困难的。因此，存在于确定性滤波器和确定性自动机之间的最小化硬度分离不适用于不确定性情形。 摘要：The problem of combinatorial filter reduction arises from questions of resource optimization in robots; it is one specific way in which automation can help to achieve minimalism, to build better, simpler robots. This paper contributes a new definition of filter minimization that is broader than its antecedents, allowing filters (input, output, or both) to be nondeterministic. This changes the problem considerably. Nondeterministic filters are able to re-use states to obtain, essentially, more 'behavior' per vertex. We show that the gap in size can be significant (larger than polynomial), suggesting such cases will generally be more challenging than deterministic problems. Indeed, this is supported by the core computational complexity result established in this paper: producing nondeterministic minimizers is PSPACE-hard. The hardness separation for minimization which exists between deterministic filter and deterministic automata, thus, does not hold for the nondeterministic case.

【17】 Vision-Based Target Localization for a Flapping-Wing Aerial Vehicle 标题：基于视觉的扑翼飞行器目标定位

作者：Xinghao Dong,Qiang Fu,Chunhua Zhang,Wei He 机构：School of Automation and Electrical Engineering, and Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing , China, Automation Research Institute Co., Ltd. of China South Industries Group Corporation, Mianyang , China 备注：Submitted to the 36th Youth Academic Annual Conference of Chinese Association of Automation (YAC2021) 链接：https://arxiv.org/abs/2107.07084 摘要：扑翼飞行器是一种模仿鸟类和昆虫飞行方式的新型飞行机器人。然而，由于FWAVs具有承载能力小、续航时间短的特点，现有的大多数地面目标定位系统都不适用于FWAVs。提出了一种基于普通摄像机模型的基于视觉的FWAVs目标定位算法。由于传感器存在测量误差，摄像机在飞行过程中存在抖动和运动模糊，在仿真实验中引入高斯噪声，然后采用一阶低通滤波器来稳定定位值。此外，为了验证目标定位算法的可行性和准确性，我们设计了一套加入各种噪声的仿真实验。从仿真中发现，该算法具有良好的定位性能。 摘要：The flapping-wing aerial vehicle (FWAV) is a new type of flying robot that mimics the flight mode of birds and insects. However, FWAVs have their special characteristics of less load capacity and short endurance time, so that most existing systems of ground target localization are not suitable for them. In this paper, a vision-based target localization algorithm is proposed for FWAVs based on a generic camera model. Since sensors exist measurement error and the camera exists jitter and motion blur during flight, Gaussian noises are introduced in the simulation experiment, and then a first-order low-pass filter is used to stabilize the localization values. Moreover, in order to verify the feasibility and accuracy of the target localization algorithm, we design a set of simulation experiments where various noises are added. From the simulation results, it is found that the target localization algorithm has a good performance.

【18】 Learning Sparse Interaction Graphs of Partially Observed Pedestrians for Trajectory Prediction 标题：用于轨迹预测的部分观察行人稀疏交互图学习

作者：Zhe Huang,Ruohua Li,Kazuki Shin,Katherine Driggs-Campbell 机构：University of Illinois at Urbana-Champaign, University of Michigan 备注：10 pages, 3 figures 链接：https://arxiv.org/abs/2107.07056 摘要：多人路径预测是非结构化环境下与人群交互的自治系统中不可缺少的安全因素。许多最近的工作已经发展了轨迹预测算法，重点是理解行人运动背后的社会规范。然而，我们观察到这些工作通常有两个假设，阻止他们顺利地应用于机器人应用：所有行人的位置始终跟踪；目标代理注意场景中的所有行人。第一种假设导致了不完全行人数据下的有偏交互建模，第二种假设引入了不必要的干扰，导致了冻结机器人问题。因此，我们提出了Gumbel社会变换，其中边Gumbel选择器在每个时间步对部分观察到的行人的稀疏交互图进行采样。一个节点变换编码器和一个掩蔽的LSTM用采样的稀疏图对行人特征进行编码以预测其运动轨迹。我们证明，我们的模型克服了由假设引起的潜在问题，并且我们的方法在基准评估方面优于相关工作。 摘要：Multi-pedestrian trajectory prediction is an indispensable safety element of autonomous systems that interact with crowds in unstructured environments. Many recent efforts have developed trajectory prediction algorithms with focus on understanding social norms behind pedestrian motions. Yet we observe these works usually hold two assumptions that prevent them from being smoothly applied to robot applications: positions of all pedestrians are consistently tracked; the target agent pays attention to all pedestrians in the scene. The first assumption leads to biased interaction modeling with incomplete pedestrian data, and the second assumption introduces unnecessary disturbances and leads to the freezing robot problem. Thus, we propose Gumbel Social Transformer, in which an Edge Gumbel Selector samples a sparse interaction graph of partially observed pedestrians at each time step. A Node Transformer Encoder and a Masked LSTM encode the pedestrian features with the sampled sparse graphs to predict trajectories. We demonstrate that our model overcomes the potential problems caused by the assumptions, and our approach outperforms the related works in benchmark evaluation.

【19】 Diff-Net: Image Feature Difference based High-Definition Map Change Detection 标题：Diff-Net：基于图像特征差异的高清地图变化检测

作者：Lei He,Shengjie Jiang,Xiaoqing Liang,Ning Wang,Shiyu Song 机构： Baidu Autonomous Driving Technology, Department (ADT), China University of Geosciences, Beijing, China, National Laboratory of Pattern, Recognition, Institute of Automation Chinese, Academy of Sciences, Nanjing University of Information, Science & Technology 备注：13 pages, 4 figures 链接：https://arxiv.org/abs/2107.07030 摘要：最新的高清（HD）地图对于自动驾驶汽车是必不可少的。为了获得不断更新的高清地图，我们提出了一种深度神经网络（DNN），Diff-Net，来检测地图的变化。与传统的基于目标检测器的方法相比，本文的核心设计是一种并行的特征差分计算结构，通过比较从摄像机和光栅化图像中提取的特征来推断地图的变化。为了生成这些光栅化的图像，我们将地图元素投影到相机视图中的图像上，从而产生有意义的地图表示，DNN可以相应地使用这些表示。当我们将变化检测任务描述为一个对象检测问题时，我们利用基于锚的结构来预测具有不同变化状态类别的边界框。此外，我们引入了一个时空融合模块，将历史帧中的特征融合到当前帧中，从而提高了系统的整体性能。最后，我们使用新收集的数据集全面验证了该方法的有效性。结果表明，我们的Diff-Net实现了比基线方法更好的性能，并准备好集成到地图生产流水线中，以保持最新的HD地图。 摘要：Up-to-date High-Definition (HD) maps are essential for self-driving cars. To achieve constantly updated HD maps, we present a deep neural network (DNN), Diff-Net, to detect changes in them. Compared to traditional methods based on object detectors, the essential design in our work is a parallel feature difference calculation structure that infers map changes by comparing features extracted from the camera and rasterized images. To generate these rasterized images, we project map elements onto images in the camera view, yielding meaningful map representations that can be consumed by a DNN accordingly. As we formulate the change detection task as an object detection problem, we leverage the anchor-based structure that predicts bounding boxes with different change status categories. Furthermore, rather than relying on single frame input, we introduce a spatio-temporal fusion module that fuses features from history frames into the current, thus improving the overall performance. Finally, we comprehensively validate our method's effectiveness using freshly collected datasets. Results demonstrate that our Diff-Net achieves better performance than the baseline methods and is ready to be integrated into a map production pipeline maintaining an up-to-date HD map.

【20】 Sensorimotor-inspired Tactile Feedback and Control Improve Consistency of Prosthesis Manipulation in the Absence of Direct Vision 标题：感觉运动激发的触觉反馈和控制在没有直接视觉的情况下提高假肢操作的一致性

作者：Neha Thomas,Farimah Fazlollahi,Jeremy D. Brown,Katherine J. Kuchenbecker 机构： Max Planck Institute for Intelligent Systems 备注：Accepted to IROS 2021 链接：https://arxiv.org/abs/2107.07000 摘要：缺乏触觉感知的上肢假肢迫使截肢者在很大程度上依赖视觉线索来完成日常生活活动。相比之下，健全的个体天生依赖于有意识的触觉感知和自动触觉反射来控制不允许持续视觉注意的情况下的意志行为。因此，我们提出了一个肌电假肢系统，反映了这些概念，以帮助操作性能无直视。为了实现这一设计，我们构建了两个基于织物的触觉传感器，它们沿着假肢手指的掌侧和背侧测量接触位置，并抓住假肢拇指尖端的压力。受自然感觉运动系统的启发，我们利用这些传感器的测量值来提供接触位置的振动反馈，并实现一个触觉抓取控制器，该控制器使用自动反射来防止过度抓取和物体滑动。我们将这个系统与标准的肌电假肢进行比较，在没有直视的情况下进行具有挑战性的取放任务；在这项研究中，有17名受试者是在成人之间进行的。与标准组的参与者相比，触觉组的参与者取得了更一致的高绩效。这些结果表明，接触位置反馈和反射控制的加入提高了上肢假肢在无直视情况下抓取和移动物体的一致性。 摘要：The lack of haptically aware upper-limb prostheses forces amputees to rely largely on visual cues to complete activities of daily living. In contrast, able-bodied individuals inherently rely on conscious haptic perception and automatic tactile reflexes to govern volitional actions in situations that do not allow for constant visual attention. We therefore propose a myoelectric prosthesis system that reflects these concepts to aid manipulation performance without direct vision. To implement this design, we built two fabric-based tactile sensors that measure contact location along the palmar and dorsal sides of the prosthetic fingers and grasp pressure at the tip of the prosthetic thumb. Inspired by the natural sensorimotor system, we use the measurements from these sensors to provide vibrotactile feedback of contact location and implement a tactile grasp controller that uses automatic reflexes to prevent over-grasping and object slip. We compare this system to a standard myoelectric prosthesis in a challenging reach-to-pick-and-place task conducted without direct vision; 17 able-bodied adults took part in this single-session between-subjects study. Participants in the tactile group achieved more consistent high performance compared to participants in the standard group. These results indicate that the addition of contact-location feedback and reflex control increases the consistency with which objects can be grasped and moved without direct vision in upper-limb prosthetics.

【21】 Deformable Elasto-Plastic Object Shaping using an Elastic Hand and Model-Based Reinforcement Learning 标题：基于弹性手和基于模型强化学习的可变形弹塑性物体成形

作者：Carolyn Matl,Ruzena Bajcsy 机构：All authors are affiliated with the Department of Electrical Engineeringand Computer Science, University of California 备注：9 pages, 6 figures, To be published in Proc. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 链接：https://arxiv.org/abs/2107.06924 摘要：可变形的固体物体，如粘土或面团，在工业和家庭环境中非常普遍。然而，机器人对这些物体的操纵在很大程度上还没有被文献所探索，因为在表示和建模这些物体的变形时涉及到高度的复杂性。本文提出在强化学习框架下，利用一种新型的弹性末端执行器来滚动面团，以解决弹塑性面团的成型问题。通过一小时的机器人探索学习末端效应器与面团相互作用的过渡模型，将不同水合程度的面团推出不同的长度。实验结果令人鼓舞，与启发式方法相比，所提出的框架以60%的动作完成了将面团擀成指定长度的任务。此外，我们还表明，使用软端效应器估计刚度可以有效地初始化模型，与不正确的模型初始化相比，机器人性能提高了约40%。 摘要：Deformable solid objects such as clay or dough are prevalent in industrial and home environments. However, robotic manipulation of such objects has largely remained unexplored in literature due to the high complexity involved in representing and modeling their deformation. This work addresses the problem of shaping elasto-plastic dough by proposing to use a novel elastic end-effector to roll dough in a reinforcement learning framework. The transition model for the end-effector-to-dough interactions is learned from one hour of robot exploration, and doughs of different hydration levels are rolled out into varying lengths. Experimental results are encouraging, with the proposed framework accomplishing the task of rolling out dough into a specified length with 60% fewer actions than a heuristic method. Furthermore, we show that estimating stiffness using the soft end-effector can be used to effectively initialize models, improving robot performance by approximately 40% over incorrect model initialization.