机器人相关学术速递[9.8]

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cs.RO机器人相关，共计16篇

【1】 Optimal Stroke Learning with Policy Gradient Approach for Robotic Table Tennis 标题：基于策略梯度法的机器人乒乓球最优击球学习 链接：https://arxiv.org/abs/2109.03100

作者：Yapeng Gao,Jonas Tebbe,Andreas Zell 机构： University of Tue-bingen 摘要：学习打乒乓球对机器人来说是一项具有挑战性的任务，因为所需的击球方式多种多样。深度强化学习（RL）的最新进展显示了学习最佳笔划的潜力。然而，大量的探索仍然限制了在实际场景中使用RL的适用性。在本文中，我们首先提出了一个真实的仿真环境，其中为球的动力学和机器人的运动学建立了多个模型。我们没有训练一个端到端的RL模型，而是将其分解为两个阶段：球的击球状态预测和从中学习球拍击球。第二阶段提出了一种基于TD3骨干网的策略梯度方法。实验表明，该方法在仿真上明显优于现有的RL方法。为了从模拟过渡到现实，我们开发了一种有效的再训练方法，并在三个真实场景中进行了测试，成功率为98%。 摘要：Learning to play table tennis is a challenging task for robots, due to the variety of the strokes required. Current advances in deep Reinforcement Learning (RL) have shown potential in learning the optimal strokes. However, the large amount of exploration still limits the applicability when utilizing RL in real scenarios. In this paper, we first propose a realistic simulation environment where several models are built for the ball's dynamics and the robot's kinematics. Instead of training an end-to-end RL model, we decompose it into two stages: the ball's hitting state prediction and consequently learning the racket strokes from it. A novel policy gradient approach with TD3 backbone is proposed for the second stage. In the experiments, we show that the proposed approach significantly outperforms the existing RL methods in simulation. To cross the domain from simulation to reality, we develop an efficient retraining method and test in three real scenarios with a success rate of 98%.

【2】 OdoNet: Untethered Speed Aiding for Vehicle Navigation Without Hardware Wheeled Odometer 标题：OdoNet：无硬件轮式里程表的车辆导航无绳速度辅助 链接：https://arxiv.org/abs/2109.03091

作者：Hailiang Tang,Xiaoji Niu,Tisheng Zhang,You Li,Jingnan Liu 机构：Wuhan , China., Center, Wuhan University, Wuhan , China, and also with the, Collaborative Innovation Center of Geospatial Technology, Wuhan University, Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 备注：13 pages, 15 figures 摘要：里程表已被证明能显著提高全球导航卫星系统/惯性导航系统（GNSS/INS）组合车辆导航在受到GNSS挑战的环境中的精度。然而，里程表在许多应用中无法使用，尤其是对于售后设备。为了在不使用硬件轮式里程表的情况下应用前进速度辅助，我们提出了一种无约束的基于一维卷积神经网络（CNN）的伪里程表模型ODNET，该模型可以作为轮式里程表的替代方案。已经进行了专门的实验，以验证Ordonet的可行性和鲁棒性。结果表明，IMU个性、车辆载荷和道路条件对齿形网的鲁棒性和精度影响不大，而IMU偏差和安装角度可能会显著损坏齿形网。因此，增加了数据清理程序，以有效缓解IMU偏置和安装角度的影响。与仅使用非完整约束（NHC）的过程相比，采用伪里程表后，定位误差降低了68%左右，而硬件轮式里程表的定位误差降低了74%左右。综上所述，提出的ODNET可以作为一种无约束的车辆导航伪里程表，可以有效地提高GNSS环境下定位的精度和可靠性。 摘要：Odometer has been proven to significantly improve the accuracy of the Global Navigation Satellite System / Inertial Navigation System (GNSS/INS) integrated vehicle navigation in GNSS-challenged environments. However, the odometer is inaccessible in many applications, especially for aftermarket devices. To apply forward speed aiding without hardware wheeled odometer, we propose OdoNet, an untethered one-dimensional Convolution Neural Network (CNN)-based pseudo-odometer model learning from a single Inertial Measurement Unit (IMU), which can act as an alternative to the wheeled odometer. Dedicated experiments have been conducted to verify the feasibility and robustness of the OdoNet. The results indicate that the IMU individuality, the vehicle loads, and the road conditions have little impact on the robustness and precision of the OdoNet, while the IMU biases and the mounting angles may notably ruin the OdoNet. Thus, a data-cleaning procedure is added to effectively mitigate the impacts of the IMU biases and the mounting angles. Compared to the process using only non-holonomic constraint (NHC), after employing the pseudo-odometer, the positioning error is reduced by around 68%, while the percentage is around 74% for the hardware wheeled odometer. In conclusion, the proposed OdoNet can be employed as an untethered pseudo-odometer for vehicle navigation, which can efficiently improve the accuracy and reliability of the positioning in GNSS-denied environments.

【3】 Distributed Allocation and Scheduling of Tasks with Cross-Schedule Dependencies for Heterogeneous Multi-Robot Teams 标题：具有交叉调度依赖的异构多机器人团队任务分布式分配与调度 链接：https://arxiv.org/abs/2109.03089

作者：Barbara Arbanas Ferreira,Tamara Petrović,Matko Orsag,J. Ramiro Martínez-de-Dios,Stjepan Bogdan 机构：University of Zagreb, Unska , Zagreb, Croatia, Universidad de Sevilla, GRVC Robotics Lab Sevilla, Camino de los Descubrimientos sn, Sevilla, Spain 摘要：为了在日常生活中安全高效地使用多机器人系统，必须开发一种鲁棒快速的方法来协调它们的动作。在本文中，我们提出了一种分布式任务分配和调度算法，用于不同机器人的任务与时间和优先级约束紧密耦合的任务。该方法基于将问题表示为车辆路径问题的一个变体，并使用基于进化计算的分布式元启发式算法（CBM pop）找到解决方案。这种方法允许快速且接近最优的分配，因此可用于任务更改时的在线重新规划。仿真结果表明，与现有的分布式方法相比，该方法具有更好的计算速度和可扩展性。给出了规划程序在由多机器人系统维护的温室实际用例中的应用。 摘要：To enable safe and efficient use of multi-robot systems in everyday life, a robust and fast method for coordinating their actions must be developed. In this paper, we present a distributed task allocation and scheduling algorithm for missions where the tasks of different robots are tightly coupled with temporal and precedence constraints. The approach is based on representing the problem as a variant of the vehicle routing problem, and the solution is found using a distributed metaheuristic algorithm based on evolutionary computation (CBM-pop). Such an approach allows a fast and near-optimal allocation and can therefore be used for online replanning in case of task changes. Simulation results show that the approach has better computational speed and scalability without loss of optimality compared to the state-of-the-art distributed methods. An application of the planning procedure to a practical use case of a greenhouse maintained by a multi-robot system is given.

【4】 A drl based distributed formation control scheme with stream based collision avoidance 标题：一种基于DRL的基于流避免冲突的分布式编队控制方案 链接：https://arxiv.org/abs/2109.03037

作者：Xinyou Qiu,Xiaoxiang Li,Jian Wang,Yu Wang,Yuan Shen 机构：Beijing National Research Center for Information Science and Technology, Department of Electronic Engineering, Tsinghua University, Beijing , China 备注：5 pages, 5 figures, been accepted and to be published in IEEE International Conference on Autonomous Systems 2021 摘要：编队和避碰能力对于多智能体系统至关重要。传统的方法通常需要一个中央控制器和全局信息来实现协作，这在未知环境中是不切实际的。本文提出了一种基于深度强化学习（DRL）的自主车辆分布式编队控制方案。采用改进的基于流的避障方法对最优轨迹进行平滑处理，并使用机载传感器（如激光雷达和天线阵列）获取局部相对距离和角度信息。该方案获得了一种可扩展的分布式控制策略，该策略通过局部观测联合优化编队跟踪误差和平均碰撞率。仿真结果表明，该方法在保持队形和避免碰撞方面优于其他两种先进算法。 摘要：Formation and collision avoidance abilities are essential for multi-agent systems. Conventional methods usually require a central controller and global information to achieve collaboration, which is impractical in an unknown environment. In this paper, we propose a deep reinforcement learning (DRL) based distributed formation control scheme for autonomous vehicles. A modified stream-based obstacle avoidance method is applied to smoothen the optimal trajectory, and onboard sensors such as Lidar and antenna arrays are used to obtain local relative distance and angle information. The proposed scheme obtains a scalable distributed control policy which jointly optimizes formation tracking error and average collision rate with local observations. Simulation results demonstrate that our method outperforms two other state-of-the-art algorithms on maintaining formation and collision avoidance.

【5】 Exploring the Accuracy Potential of IMU Preintegration in Factor Graph Optimization 标题：IMU预积分在因子图优化中的精度潜力探讨 链接：https://arxiv.org/abs/2109.03010

作者：Hailiang Tang,Xiaoji Niu,Tisheng Zhang,Jing Fan,Jingnan Liu 机构：with the GNSS Research Center, Wuhan University, Wuhan , China, unscented Kalman filter [,], or other alternatives. In particular, EKF-based GNSSINS integration has become a mature, technology that constitutes a benchmark for the potential 备注：8 pages, 3 figures 摘要：惯性测量单元（IMU）预积分广泛应用于因子图优化（FGO）；e、 例如，在视觉惯性导航系统和全球导航卫星系统/惯性导航系统（GNSS/INS）集成中。然而，大多数现有的IMU预积分模型忽略了地球自转，缺乏精细的积分过程，这些限制严重降低了INS的精度。在本研究中，我们构建了一个包含地球自转的改进IMU预积分模型，并解析计算协方差和雅可比矩阵。为了减轻评估系统中IMU以外的传感器造成的影响，采用基于FGO的GNSS/INS集成方法对改进的预集成精度进行定量评估。与经典的基于滤波的GNSS/INS集成基线相比，采用的基于FGO的集成使用改进的预集成产生相同的精度。相比之下，现有的粗糙预积分会导致精度显著降低。对于工业级MEMS模块，精细和粗糙预集成模型之间的性能差异可以超过200%，对于消费级MEMS芯片，性能差异可以超过10%。显然，地球自转是IMU预集成中要考虑的主要因素，以保持IMU精度，即使对于消费级IMU也是如此。 摘要：Inertial measurement unit (IMU) preintegration is widely used in factor graph optimization (FGO); e.g., in visual-inertial navigation system and global navigation satellite system/inertial navigation system (GNSS/INS) integration. However, most existing IMU preintegration models ignore the Earth's rotation and lack delicate integration processes, and these limitations severely degrade the INS accuracy. In this study, we construct a refined IMU preintegration model that incorporates the Earth's rotation, and analytically compute the covariance and Jacobian matrix. To mitigate the impact caused by sensors other than IMU in the evaluation system, FGO-based GNSS/INS integration is adopted to quantitatively evaluate the accuracy of the refined preintegration. Compared to a classic filtering-based GNSS/INS integration baseline, the employed FGO-based integration using the refined preintegration yields the same accuracy. In contrast, the existing rough preintegration yields significant accuracy degradation. The performance difference between the refined and rough preintegration models can exceed 200% for an industrial-grade MEMS module and 10% for a consumer-grade MEMS chip. Clearly, the Earth's rotation is the major factor to be considered in IMU preintegration in order to maintain the IMU precision, even for a consumer-grade IMU.

【6】 CovarianceNet: Conditional Generative Model for Correct Covariance Prediction in Human Motion Prediction 标题：协方差网：人体运动预测中正确协方差预测的条件生成模型 链接：https://arxiv.org/abs/2109.02965

作者：Aleksey Postnikov,Aleksander Gamayunov,Gonzalo Ferrer 机构： 2Skolkovo Institute of Science and Technology 摘要：预测人体运动时，不确定性的正确表征与预测的准确性同等重要。我们提出了一种新的方法来正确预测与预测的未来轨迹分布相关的不确定性。我们的方法，协变量网，是基于一个具有高斯潜变量的条件生成模型来预测双变量高斯分布的参数。协方差网与运动预测模型的结合产生了一种输出单峰分布的混合方法。我们将展示一些最先进的运动预测方法在预测不确定性时是如何变得过度自信的，根据我们提出的度量，并在ETH数据集{pellegrini2009you}中验证。协方差网能够正确预测不确定性，这使得我们的方法适用于使用预测分布的应用，例如规划或决策。 摘要：The correct characterization of uncertainty when predicting human motion is equally important as the accuracy of this prediction. We present a new method to correctly predict the uncertainty associated with the predicted distribution of future trajectories. Our approach, CovariaceNet, is based on a Conditional Generative Model with Gaussian latent variables in order to predict the parameters of a bi-variate Gaussian distribution. The combination of CovarianceNet with a motion prediction model results in a hybrid approach that outputs a uni-modal distribution. We will show how some state of the art methods in motion prediction become overconfident when predicting uncertainty, according to our proposed metric and validated in the ETH data-set \cite{pellegrini2009you}. CovarianceNet correctly predicts uncertainty, which makes our method suitable for applications that use predicted distributions, e.g., planning or decision making.

【7】 Defending a Perimeter from a Ground Intruder Using an Aerial Defender: Theory and Practice 标题：使用空中防御者防御地面入侵者的边界：理论和实践 链接：https://arxiv.org/abs/2109.02852

作者：Elijah S. Lee,Daigo Shishika,Giuseppe Loianno,Vijay Kumar 备注：6 pages, 10 figures, In the Proceedings of 2021 IEEE International Conference on Safety, Security, and Rescue Robotics (SSRR) 摘要：近年来，周界防御游戏作为追逃游戏的一种变体受到了人们的关注。以前的许多工作已经解决了这一博弈，以获得防御者和入侵者的最优策略，但导出的理论将玩家视为具有一阶假设的点粒子。在这项工作中，我们的目标是将从周界防御问题导出的理论应用于具有真实驱动和传感模型的机器人，并在放松一阶假设的情况下观察性能差异。特别是，我们关注半球周界防御问题，其中地面入侵者试图到达半球底部，而空中防御者被限制在半球上移动以捕获入侵者。详细介绍了从理论到实践的过渡过程，并在露台上对设计的系统进行了仿真。提出了两个参数分析和比较研究的指标来评估性能差异。 摘要：The perimeter defense game has received interest in recent years as a variant of the pursuit-evasion game. A number of previous works have solved this game to obtain the optimal strategies for defender and intruder, but the derived theory considers the players as point particles with first-order assumptions. In this work, we aim to apply the theory derived from the perimeter defense problem to robots with realistic models of actuation and sensing and observe performance discrepancy in relaxing the first-order assumptions. In particular, we focus on the hemisphere perimeter defense problem where a ground intruder tries to reach the base of a hemisphere while an aerial defender constrained to move on the hemisphere aims to capture the intruder. The transition from theory to practice is detailed, and the designed system is simulated in Gazebo. Two metrics for parametric analysis and comparative study are proposed to evaluate the performance discrepancy.

【8】 Robot Sound Interpretation: Learning Visual-Audio Representations for Voice-Controlled Robots 标题：机器人声音解释：声控机器人的视听表示学习 链接：https://arxiv.org/abs/2109.02823

作者：Peixin Chang,Shuijing Liu,Katherine Driggs-Campbell 机构： Driggs-Campbell are with the Department ofElectrical and Computer Engineering at the University of Illinois at Urbana-Champaign 摘要：受感觉运动理论的启发，我们提出了一种新的语音控制机器人管道。以前的工作依赖于声音和图像的明确标签以及外在的奖励功能。这样的方法不仅与人类感觉运动发育几乎没有相似之处，而且还需要手调节奖励和大量的人力劳动。为了解决这些问题，我们学习了一种表示法，它将图像和声音命令与最少的监督相关联。利用这种表示，我们生成了一个内在的奖励函数，用强化学习来学习机器人任务。我们在三个机器人平台上演示了我们的方法，一个TurtleBot3、一个Kuka IIWA手臂和一个Kinova Gen3机器人，它们可以听到命令词，识别相关目标物体，并执行精确控制以接近目标。我们表明，我们的方法在各种声音类型和机器人任务中的经验表现优于以前的工作。我们成功地将在模拟器中学习到的策略部署到真实世界的Kinova Gen3。 摘要：Inspired by sensorimotor theory, we propose a novel pipeline for voice-controlled robots. Previous work relies on explicit labels of sounds and images as well as extrinsic reward functions. Not only do such approaches have little resemblance to human sensorimotor development, but also require hand-tuning rewards and extensive human labor. To address these problems, we learn a representation that associates images and sound commands with minimal supervision. Using this representation, we generate an intrinsic reward function to learn robotic tasks with reinforcement learning. We demonstrate our approach on three robot platforms, a TurtleBot3, a Kuka-IIWA arm, and a Kinova Gen3 robot, which hear a command word, identify the associated target object, and perform precise control to approach the target. We show that our method outperforms previous work across various sound types and robotic tasks empirically. We successfully deploy the policy learned in simulator to a real-world Kinova Gen3.

【9】 IDS 3D City: A Digital Scaled Smart City 标题：IDS 3D城市：数字尺度的智慧城市 链接：https://arxiv.org/abs/2109.02811

作者：Raymond M. Zayas,Logan E. Beaver,Behdad Chalaki,Heeseung Bang,Andreas A. Malikopoulos 机构：The authors are with the Department of Mechanical Engineering, UniversityofDelaware 备注：6 pages, 8 figures 摘要：随着互联和自动化车辆需求的出现，对支持其开发的质量测试环境的需求也随之增加。在本文中，我们为新兴的移动系统介绍了一个基于Unity的虚拟仿真环境，称为信息与决策科学实验室的Scaled Smart Digital City（IDS 3D City），旨在与其物理对等系统及其现有控制框架一起运行。通过利用机器人操作系统、AirSim和Unity，我们构建了一个能够以比物理试验台更快的速度反复设计实验的仿真环境。这为我们提供了一个中间步骤，以便在物理测试台上测试控制框架之前验证其有效性。IDS 3D City提供的另一个好处是证明我们的控制算法独立于物理车辆动力学工作，因为AirSim引入的车辆动力学与我们的智能城市规模不同。最后，我们通过在虚拟和物理环境中进行实验来证明数字环境的有效性。 摘要：As the demand for connected and automated vehicles emerges, so to does the need for quality testing environments to support their development. In this paper, we introduce a Unity-based virtual simulation environment for emerging mobility systems, called Information and Decision Science Lab's Scaled Smart Digital City (IDS 3D City), intended to operate alongside its physical peer and its existing control framework. By utilizing the Robot Operation System, AirSim, and Unity, we have constructed a simulation environment capable of iteratively designing experiments significantly faster than is possible in a physical testbed. This provides us with an intermediate step to validate the effectiveness of our control framework prior to testing them in the physical testbed. Another benefit provided by the IDS 3D City is demonstrating that our control algorithms work independent of the physical vehicle dynamics, since the vehicle dynamics introduced by AirSim operate at a different scale than our scaled smart city. We finally demonstrate the effectiveness of our digital environment by performing an experiment in both virtual and physical environments.

【10】 Safe-Critical Modular Deep Reinforcement Learning with Temporal Logic through Gaussian Processes and Control Barrier Functions 标题：基于高斯过程和控制屏障函数的安全临界模块时态深度强化学习 链接：https://arxiv.org/abs/2109.02791

作者：Mingyu Cai,Cristian-Ioan Vasile 机构： especially while the learning 1Department of Mechanical Engineering, Lehigh University 备注：Under Review 摘要：强化学习（RL）是一种很有前途的方法，在实际应用中取得的成功有限，因为确保安全探索或促进充分利用是控制具有未知模型和测量不确定性的机器人系统的一项挑战。对于连续空间（状态空间和动作空间）上的复杂任务，这种学习问题变得更加棘手。在本文中，我们提出了一个基于学习的控制框架，包括以下几个方面：（1）利用线性时态逻辑（LTL）在无限的视界上简化复杂的任务，并将其转化为一种新的自动机结构(2） 在形式保证下，我们提出了一个创新的RL代理奖励方案，使得全局最优策略使满足LTL规范的概率最大化(3） 基于奖励成形技术，我们开发了一个模块化的策略梯度结构，利用自动机结构的优点来分解总体任务，提高学习控制器的性能(4） 通过结合高斯过程（GPs）对不确定动态系统进行估计，我们利用指数控制屏障函数（ECBF）合成了一种基于模型的防护措施，以解决高阶相对度问题。此外，我们还利用LTL自动机和ECBF的特性构建了一个指导过程，以进一步提高勘探效率。最后，我们通过几个机器人环境验证了该框架的有效性。我们证明了这种基于ECBF的模块化深度RL算法在训练过程中获得了近乎完美的成功率和高概率置信度的安全防护。 摘要：Reinforcement learning (RL) is a promising approach and has limited success towards real-world applications, because ensuring safe exploration or facilitating adequate exploitation is a challenges for controlling robotic systems with unknown models and measurement uncertainties. Such a learning problem becomes even more intractable for complex tasks over continuous space (state-space and action-space). In this paper, we propose a learning-based control framework consisting of several aspects: (1) linear temporal logic (LTL) is leveraged to facilitate complex tasks over an infinite horizons which can be translated to a novel automaton structure; (2) we propose an innovative reward scheme for RL-agent with the formal guarantee such that global optimal policies maximize the probability of satisfying the LTL specifications; (3) based on a reward shaping technique, we develop a modular policy-gradient architecture utilizing the benefits of automaton structures to decompose overall tasks and facilitate the performance of learned controllers; (4) by incorporating Gaussian Processes (GPs) to estimate the uncertain dynamic systems, we synthesize a model-based safeguard using Exponential Control Barrier Functions (ECBFs) to address problems with high-order relative degrees. In addition, we utilize the properties of LTL automatons and ECBFs to construct a guiding process to further improve the efficiency of exploration. Finally, we demonstrate the effectiveness of the framework via several robotic environments. And we show such an ECBF-based modular deep RL algorithm achieves near-perfect success rates and guard safety with a high probability confidence during training.

【11】 Deep SIMBAD: Active Landmark-based Self-localization Using Ranking -based Scene Descriptor 标题：Deep SIMBAD：基于排名的场景描述符的主动地标自定位 链接：https://arxiv.org/abs/2109.02786

作者：Tanaka Kanji 机构：The authors are with Graduate School of Engineering, University ofFukui 备注：6 pages, 7 figures, a preprint 摘要：基于Landmark的机器人自定位作为一种高度压缩的域不变方法，最近引起了人们的兴趣，用于跨域（例如，时间、天气和季节）执行视觉位置识别（VPR）。然而，对于被动观察者（例如，手动机器人控制），基于地标的自定位可能是一个不适定问题，因为许多视点可能无法提供有效的地标视图。在这项研究中，我们认为主动的自我定位任务的积极观察员，并提出了一种新的强化学习（RL）为基础的下一个最佳视图（NBV）规划师。我们的贡献如下(1） 基于SIMBAD的VPR：我们将基于landmark的紧凑场景描述问题表述为SIMBAD（基于相似性的模式识别），并进一步给出其深度学习扩展(2） VPR到NBV知识转移：我们通过将VPR的状态识别能力转移到NBV，解决了不确定性（即主动自我定位）下RL的挑战(3） 基于NNQL的NBV：我们通过采用Q-学习的最近邻近似（NNQL）将可用的VPR视为经验数据库。结果显示了一个非常紧凑的数据结构，它将VPR和NBV压缩为一个增量反向索引。使用公共NCLT数据集的实验验证了该方法的有效性。 摘要：Landmark-based robot self-localization has recently garnered interest as a highly-compressive domain-invariant approach for performing visual place recognition (VPR) across domains (e.g., time of day, weather, and season). However, landmark-based self-localization can be an ill-posed problem for a passive observer (e.g., manual robot control), as many viewpoints may not provide an effective landmark view. In this study, we consider an active self-localization task by an active observer and present a novel reinforcement learning (RL)-based next-best-view (NBV) planner. Our contributions are as follows. (1) SIMBAD-based VPR: We formulate the problem of landmark-based compact scene description as SIMBAD (similarity-based pattern recognition) and further present its deep learning extension. (2) VPR-to-NBV knowledge transfer: We address the challenge of RL under uncertainty (i.e., active self-localization) by transferring the state recognition ability of VPR to the NBV. (3) NNQL-based NBV: We regard the available VPR as the experience database by adapting nearest-neighbor approximation of Q-learning (NNQL). The result shows an extremely compact data structure that compresses both the VPR and NBV into a single incremental inverted index. Experiments using the public NCLT dataset validated the effectiveness of the proposed approach.

【12】 Behavioral assessment of a humanoid robot when attracting pedestrians in a mall 标题：人形机器人在商场吸引行人时的行为评估 链接：https://arxiv.org/abs/2109.02771

作者：Yuki Okafuji,Yasunori Ozaki,Jun Baba,Junya Nakanishi,Kohei Ogawa,Yuichiro Yoshikawa,Hiroshi Ishiguro 摘要：目前正在进行机器人作为人力支持技术的研究。特别是，服务业需要分配更多的人力，机器人支持人类将非常重要。本研究以人形机器人作为社会服务机器人，在购物中心传递信息为研究对象，并对机器人的行为概念进行了分析。为了传递信息，必须进行两个过程。行人必须停在机器人前面，机器人必须继续与他们接触。为了本研究的目的，在实验过程中，分析并比较了机器人的三种自主行为概念：主动概念、被动消极概念和被动积极概念。在尝试与65000多名行人进行互动后，本研究发现被动消极概念会让行人停得更多，停留时间更长。为了评估机器人在真实环境中的有效性，三种行为与人类广告商之间的比较结果表明：（1）机器人的主动和被动积极概念的结果与人类的结果相当，被动消极概念的表现高于所有参与者。这些发现表明，在有限的环境中，机器人在提供信息任务方面的性能与人类相当；因此，服务机器人作为一种劳动力支持技术有望在现实世界中发挥良好的作用。 摘要：Research currently being conducted on the use of robots as human labor support technology. In particular, the service industry needs to allocate more manpower, and it will be important for robots to support people. This study focuses on using a humanoid robot as a social service robot to convey information in a shopping mall, and the robot's behavioral concepts were analyzed. In order to convey the information, two processes must occur. Pedestrians must stop in front of the robot, and the robot must continue the engagement with them. For the purpose of this study, three types of autonomous behavioral concepts of the robot for the general use were analyzed and compared in these processes in the experiment: active, passive-negative, and passive-positive concepts. After interactions were attempted with 65,000+ pedestrians, this study revealed that the passive-negative concept can make pedestrians stop more and stay longer. In order to evaluate the effectiveness of the robot in a real environment, the comparative results between three behaviors and human advertisers revealed that (1) the results of the active and passive-positive concepts of the robot are comparable to those of the humans, and (2) the performance of the passive-negative concept is higher than that of all participants. These findings demonstrate that the performance of robots is comparable to that of humans in providing information tasks in a limited environment; therefore, it is expected that service robots as a labor support technology will be able to perform well in the real world.

【13】 Active Perception with Neural Networks 标题：基于神经网络的主动感知 链接：https://arxiv.org/abs/2109.02744

作者：Elijah S. Lee 机构：Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA , arXiv:,.,v, [cs.RO] , Sep 备注：33 pages, 10 figures, WPE-II Written Report, University of Pennsylvania 摘要：主动感知已经应用于许多领域，特别是在机器人领域。主动感知的思想是利用输入数据预测下一个动作，帮助机器人提高性能。主要的挑战在于理解与行动相结合的输入数据，并且以有效的方式收集有意义的环境信息是必要和需要的。随着神经网络的最新发展，在语义层面上解释感知数据已成为可能，基于深度学习的实时解释使感知-动作循环得以有效闭合。本报告重点介绍了在单智能体和多智能体系统中采用基于神经网络的主动感知的最新进展。 摘要：Active perception has been employed in many domains, particularly in the field of robotics. The idea of active perception is to utilize the input data to predict the next action that can help robots to improve their performance. The main challenge lies in understanding the input data to be coupled with the action, and gathering meaningful information of the environment in an efficient way is necessary and desired. With recent developments of neural networks, interpreting the perceived data has become possible at the semantic level, and real-time interpretation based on deep learning has enabled the efficient closing of the perception-action loop. This report highlights recent progress in employing active perception based on neural networks for single and multi-agent systems.

【14】 Graph Attention Layer Evolves Semantic Segmentation for Road Pothole Detection: A Benchmark and Algorithms 标题：基于图注意力层进化语义分割的道路坑洞检测基准与算法 链接：https://arxiv.org/abs/2109.02711

作者：Rui Fan,Hengli Wang,Yuan Wang,Ming Liu,Ioannis Pitas 机构： the Hong Kong University of Science and Technology 备注：accepted as a regular paper to IEEE Transactions on Image Processing 摘要：现有的道路坑洼检测方法可分为基于计算机视觉的方法和基于机器学习的方法。前一种方法通常采用二维图像分析/理解或三维点云建模和分割算法从视觉传感器数据中检测道路凹坑。后一种方法通常以端到端的方式使用卷积神经网络（CNN）处理道路坑洞检测。然而，道路坑洼不一定无处不在，为CNN训练准备一个大型注释良好的数据集是一项挑战。在这方面，基于计算机视觉的方法是过去十年的主流研究趋势，而基于机器学习的方法只是讨论而已。最近，我们发布了第一个基于立体视觉的道路凹坑检测数据集和一种新的视差变换算法，从而可以高度区分受损和未受损的道路区域。然而，目前还没有使用视差图像或变换视差图像训练的最先进（SoTA）CNN的基准。因此，在本文中，我们首先讨论了用于语义分割的SoTA CNN，并通过大量实验评估了它们在道路坑洞检测中的性能。此外，受图神经网络（GNN）的启发，我们提出了一种新的CNN层，称为图注意层（GAL），它可以很容易地部署在任何现有的CNN中，以优化用于语义分割的图像特征表示。我们的实验将性能最好的实现GAL-DeepLabv3+与九个SoTA CNN在三种模式的训练数据上进行了比较：RGB图像、视差图像和变换的视差图像。实验结果表明，我们提出的GAL-DeepLabv3+在所有训练数据模式下都达到了最佳的整体坑洞检测精度。 摘要：Existing road pothole detection approaches can be classified as computer vision-based or machine learning-based. The former approaches typically employ 2-D image analysis/understanding or 3-D point cloud modeling and segmentation algorithms to detect road potholes from vision sensor data. The latter approaches generally address road pothole detection using convolutional neural networks (CNNs) in an end-to-end manner. However, road potholes are not necessarily ubiquitous and it is challenging to prepare a large well-annotated dataset for CNN training. In this regard, while computer vision-based methods were the mainstream research trend in the past decade, machine learning-based methods were merely discussed. Recently, we published the first stereo vision-based road pothole detection dataset and a novel disparity transformation algorithm, whereby the damaged and undamaged road areas can be highly distinguished. However, there are no benchmarks currently available for state-of-the-art (SoTA) CNNs trained using either disparity images or transformed disparity images. Therefore, in this paper, we first discuss the SoTA CNNs designed for semantic segmentation and evaluate their performance for road pothole detection with extensive experiments. Additionally, inspired by graph neural network (GNN), we propose a novel CNN layer, referred to as graph attention layer (GAL), which can be easily deployed in any existing CNN to optimize image feature representations for semantic segmentation. Our experiments compare GAL-DeepLabv3+, our best-performing implementation, with nine SoTA CNNs on three modalities of training data: RGB images, disparity images, and transformed disparity images. The experimental results suggest that our proposed GAL-DeepLabv3+ achieves the best overall pothole detection accuracy on all training data modalities.

【15】 A Virtual Reality-based Training and Assessment System for Bridge Inspectors with an Assistant Drone 标题：基于虚拟现实的无人驾驶桥梁检查员训练评估系统 链接：https://arxiv.org/abs/2109.02705

作者：Yu Li,Muhammad Monjurul Karim,Ruwen Qin 机构： Qin are with the Department of Civil Engineering, Stony Brook University 备注：22 pages, 11 figures. Submitted to IEEE Transactions on Human-Machine Systems on July 29th, 2021. Current state: Under review 摘要：美国超过600000座桥梁必须每两年检查一次，以确定可能需要后续维护的缺陷、缺陷或潜在问题。为提高安全性、效率和成本效益，桥梁检查采用了一种空中机器人技术——无人驾驶飞行器（无人机）。尽管无人机具有自主操作模式，但在桥梁检查等复杂任务中，让检查员保持在回路中仍然是必要的。因此，检查员需要训练在工作中操作无人机的技能和信心。本文介绍了一个基于虚拟现实技术的桥梁检测人员训练系统的设计与开发，该系统由无人机辅助进行桥梁检测。该系统由四个集成模块组成：Unity开发的模拟桥梁检查、允许学员使用遥控器模拟操作无人机的界面、分析数据以向学员提供实时任务反馈以帮助其学习的监控和分析，以及促进学员学习的学习后评估。本文还进行了一项小型实验研究，以说明该系统的功能及其对建立检查员-无人机伙伴关系的帮助。开发的系统已经建立了一个建模和分析基础，探索先进的解决方案，无人驾驶协作检测和基于人类传感器的人机交互。 摘要：Over 600,000 bridges in the U.S. must be inspected every two years to identify flaws, defects, or potential problems that may need follow-up maintenance. An aerial robotic technology, Unmanned Aerial Vehicles (drones), has been adopted for bridge inspection for improving safety, efficiency, and cost-effectiveness. Although drones have an autonomous operation mode, keeping inspectors in the loop is still necessary for complex tasks like bridge inspection. Therefore, inspectors need to develop the skill and confidence in operating drones in their jobs. This paper presents the design and development of a virtual reality-based system for training inspectors who are assisted by a drone in the bridge inspection. The system is composed of four integrated modules: a simulated bridge inspection developed in Unity, an interface that allows a trainee to operate the drone in simulation using a remote controller, monitoring and analysis that analyzes data to provide real-time, in-task feedback to trainees to assist their learning, and a post-study assessment for accelerating the learning of trainees. The paper also conducts a small-size experimental study to illustrate the functionality of this system and its helpfulness for establishing the inspector-drone partnership. The developed system has built a modeling and analysis foundation for exploring advanced solutions to human-drone cooperative inspection and human sensor-based human-drone interaction.

【16】 Intelligent Motion Planning for a Cost-effective Object Follower Mobile Robotic System with Obstacle Avoidance 标题：高性价比目标跟随式避障移动机器人系统的智能运动规划 链接：https://arxiv.org/abs/2109.02700

作者：Sai Nikhil Gona,Prithvi Raj Bandhakavi 机构：Electrical and Electronics Engineering, Mechanical Engineering, Chaitanya Bharathi Institute of Technology, Telangana, India 备注：24 pages, 27 figures 摘要：很少有行业使用手动控制的机器人来搬运材料，而且这种机器人不能在所有地方一直使用。因此，拥有能够跟随特定人类的机器人是非常安静的，它可以跟随该人所持有的独特颜色的物体。因此，我们提出了一个机器人系统，它使用机器人视觉和深度学习来获得所需的线速度和角速度，分别为{\nu}和{\omega}。这反过来使机器人在跟随人类持有的独特颜色物体时能够避开障碍物。我们提出的新方法能够准确地检测任何类型照明中独特颜色物体的位置，并告诉我们机器人所在位置的水平像素值，还可以告诉我们物体是否靠近或远离机器人。此外，我们在这个问题中使用的人工神经网络在线性和角速度预测和用于控制线性和角速度的PI控制器方面给了我们很小的误差，这反过来又控制了机器人的位置，给我们带来了令人印象深刻的结果，这种方法优于所有其他方法。 摘要：There are few industries which use manually controlled robots for carrying material and this cannot be used all the time in all the places. So, it is very tranquil to have robots which can follow a specific human by following the unique coloured object held by that person. So, we propose a robotic system which uses robot vision and deep learning to get the required linear and angular velocities which are {\nu} and {\omega}, respectively. Which in turn makes the robot to avoid obstacles when following the unique coloured object held by the human. The novel methodology that we are proposing is accurate in detecting the position of the unique coloured object in any kind of lighting and tells us the horizontal pixel value where the robot is present and also tells if the object is close to or far from the robot. Moreover, the artificial neural networks that we have used in this problem gave us a meagre error in linear and angular velocity prediction and the PI controller which was used to control the linear and angular velocities, which in turn controls the position of the robot gave us impressive results and this methodology outperforms all other methodologies.

机器翻译，仅供参考