机器人相关学术速递[8.19]

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cs.RO机器人相关，共计16篇

【1】 PerceMon: Online Monitoring for Perception Systems 标题：PerceMon：感知系统的在线监测 链接：https://arxiv.org/abs/2108.08289

作者：Anand Balakrishnan,Jyotirmoy Deshmukh,Bardh Hoxha,Tomoya Yamaguchi,Georgios Fainekos 机构： University of Southern California, TRINA, Toyota Motor NA R&D, Arizona State University 摘要：自动驾驶车辆中的感知算法对于车辆理解其周围环境的语义至关重要，包括对环境中物体的检测和跟踪。这些算法的输出反过来用于安全关键场景（如碰撞避免和自动紧急制动）中的决策。因此，在运行时监控这些感知系统是至关重要的。然而，由于感知系统输出的高级、复杂表示，测试和验证这些系统是一个挑战，尤其是在运行时。在本文中，我们提出了一个运行时监控工具PerceMon，它可以监控时间质量时态逻辑（TQTL）中的任意规范及其带有空间运算符的扩展。我们将该工具与卡拉自主车辆仿真环境和ROS中间件平台集成，同时监控最先进的目标检测和跟踪算法的性能。 摘要：Perception algorithms in autonomous vehicles are vital for the vehicle to understand the semantics of its surroundings, including detection and tracking of objects in the environment. The outputs of these algorithms are in turn used for decision-making in safety-critical scenarios like collision avoidance, and automated emergency braking. Thus, it is crucial to monitor such perception systems at runtime. However, due to the high-level, complex representations of the outputs of perception systems, it is a challenge to test and verify these systems, especially at runtime. In this paper, we present a runtime monitoring tool, PerceMon that can monitor arbitrary specifications in Timed Quality Temporal Logic (TQTL) and its extensions with spatial operators. We integrate the tool with the CARLA autonomous vehicle simulation environment and the ROS middleware platform while monitoring properties on state-of-the-art object detection and tracking algorithms.

【2】 End-to-End Urban Driving by Imitating a Reinforcement Learning Coach 标题：模仿强化学习教练的城市端到端驾驶 链接：https://arxiv.org/abs/2108.08265

作者：Zhejun Zhang,Alexander Liniger,Dengxin Dai,Fisher Yu,Luc Van Gool 机构：Computer Vision Lab, ETH Z¨urich,MPI for Informatics,PSI, KU Leuven 备注：ICCV 2021 摘要：自动驾驶的端到端方法通常依靠专家演示。尽管人类是很好的驱动者，但他们不是端到端算法的好教练，而端到端算法需要密集的政策监督。相反，利用特权信息的自动化专家可以高效地生成大规模的策略内和策略外演示。然而，现有的城市驾驶自动化专家大量使用手工制定的规则，即使在驾驶模拟器上也无法达到最佳效果，因为在驾驶模拟器上可以获得地面真实信息。为了解决这些问题，我们训练了一名强化学习专家，将鸟瞰图像映射到连续的低级动作。在为卡拉设定新的绩效上限的同时，我们的专家也是一名更好的教练，为模仿学习代理提供信息丰富的监督信号，供其学习。在强化学习教练的监督下，具有单目摄像机输入的基线端到端代理实现了专家级性能。我们的端到端代理实现了78%的成功率，同时在NoCrash Density benchmark和更具挑战性的CARLA排行榜上推广到新市镇和新天气。 摘要：End-to-end approaches to autonomous driving commonly rely on expert demonstrations. Although humans are good drivers, they are not good coaches for end-to-end algorithms that demand dense on-policy supervision. On the contrary, automated experts that leverage privileged information can efficiently generate large scale on-policy and off-policy demonstrations. However, existing automated experts for urban driving make heavy use of hand-crafted rules and perform suboptimally even on driving simulators, where ground-truth information is available. To address these issues, we train a reinforcement learning expert that maps bird's-eye view images to continuous low-level actions. While setting a new performance upper-bound on CARLA, our expert is also a better coach that provides informative supervision signals for imitation learning agents to learn from. Supervised by our reinforcement learning coach, a baseline end-to-end agent with monocular camera-input achieves expert-level performance. Our end-to-end agent achieves a 78% success rate while generalizing to a new town and new weather on the NoCrash-dense benchmark and state-of-the-art performance on the more challenging CARLA LeaderBoard.

【3】 Towards Robust Human Trajectory Prediction in Raw Videos 标题：面向原始视频的鲁棒人体轨迹预测 链接：https://arxiv.org/abs/2108.08259

作者：Rui Yu,Zihan Zhou 机构： Pennsylvania State University, University Park 备注：8 pages, 6 figures. Accepted by the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021) 摘要：由于人体轨迹预测在自主车辆和室内机器人等应用中的重要性，近年来受到越来越多的关注。然而，现有的大多数方法都是基于人体标记轨迹进行预测，而忽略了检测和跟踪中的误差和噪声。在本文中，我们研究了原始视频中的人体轨迹预测问题，发现各种类型的跟踪误差都会严重影响预测精度。因此，我们提出了一种简单而有效的策略，通过加强预测一致性来纠正跟踪失败。所提出的“再跟踪”算法可以应用于任何现有的跟踪和预测管道。在公共基准数据集上的实验表明，该方法可以在具有挑战性的现实场景中提高跟踪和预测性能。有关代码和数据，请访问https://git.io/retracking-prediction. 摘要：Human trajectory prediction has received increased attention lately due to its importance in applications such as autonomous vehicles and indoor robots. However, most existing methods make predictions based on human-labeled trajectories and ignore the errors and noises in detection and tracking. In this paper, we study the problem of human trajectory forecasting in raw videos, and show that the prediction accuracy can be severely affected by various types of tracking errors. Accordingly, we propose a simple yet effective strategy to correct the tracking failures by enforcing prediction consistency over time. The proposed "re-tracking" algorithm can be applied to any existing tracking and prediction pipelines. Experiments on public benchmark datasets demonstrate that the proposed method can improve both tracking and prediction performance in challenging real-world scenarios. The code and data are available at https://git.io/retracking-prediction.

【4】 Design and Analysis of Modular Pipe Climber-III with a Multi-Output Differential Mechanism 标题：具有多输出差动机构的模块式管材登山机-III型的设计与分析 链接：https://arxiv.org/abs/2108.08243

作者：Vishnu Kumar,Saharsh Agarwal,Rama Vadapalli,Nagamanikandan Govindan,Madhava Krishna 备注：6 pages and 10 figures 摘要：本文介绍了一种管内爬壁机器人的设计，该机器人使用一种新型的“三输出开放式差分”（3-OOD）机构来穿越复杂的管道网络。传统的轮式/履带式管道攀爬机器人在管道弯曲处穿越时容易滑动和拖拽。3-OOD机构有助于实现在运动过程中消除机器人轨迹中滑动和阻力的新结果。该差分实现了传统的两输出差分的功能，这是三输出差分首次实现。3-OOD机构通过消除任何主动控制的需要，根据施加在管网内每条轨道上的力，以机械方式调节机器人的轨道速度。通过对机器人在不同方向的管网中以及在弯管中无滑移穿越的仿真，表明了该设计的有效性。 摘要：This paper presents the design of an in-pipe climbing robot that operates using a novel `Three-output open differential'(3-OOD) mechanism to traverse complex networks of pipes. Conventional wheeled/tracked in-pipe climbing robots are prone to slip and drag while traversing in pipe bends. The 3-OOD mechanism helps in achieving the novel result of eliminating slip and drag in the robot tracks during motion. The proposed differential realizes the functional abilities of the traditional two-output differential, which is achieved the first time for a differential with three outputs. The 3-OOD mechanism mechanically modulates the track speeds of the robot based on the forces exerted on each track inside the pipe network, by eliminating the need for any active control. The simulation of the robot traversing in the pipe network in different orientations and in pipe-bends without slip shows the proposed design's effectiveness.

【5】 LOKI: Long Term and Key Intentions for Trajectory Prediction 标题：LOKI：轨迹预测的长期和关键意图 链接：https://arxiv.org/abs/2108.08236

作者：Harshayu Girase,Haiming Gang,Srikanth Malla,Jiachen Li,Akira Kanehara,Karttikeya Mangalam,Chiho Choi 机构：Honda Research Institute USA, University of California, Berkeley, Honda R&D Co., Ltd. 备注：ICCV 2021 (The dataset is available at this https URL) 摘要：轨迹预测方面的最新进展表明，关于主体意图的明确推理对于准确预测其运动非常重要。然而，目前的研究活动并不直接适用于智能和安全关键系统。这主要是因为很少有公共数据集是可用的，他们只考虑行人特定意图短暂的时间跨度从限制自我中心的观点。为此，我们提出了LOKI（长期和关键意图），这是一种新型的大规模数据集，旨在解决自主驾驶环境中异构交通代理（行人和车辆）的联合轨迹和意图预测问题。创建LOKI数据集是为了发现可能影响意图的几个因素，包括i）代理人自身意愿，ii）社会互动，iii）环境约束，以及iv）上下文信息。我们还提出了一个联合执行轨迹和意图预测的模型，表明关于意图的循环推理可以辅助轨迹预测。我们展示了我们的方法比最先进的轨迹预测方法高出27\%$，并且还为基于帧的意图估计提供了基线。 摘要：Recent advances in trajectory prediction have shown that explicit reasoning about agents' intent is important to accurately forecast their motion. However, the current research activities are not directly applicable to intelligent and safety critical systems. This is mainly because very few public datasets are available, and they only consider pedestrian-specific intents for a short temporal horizon from a restricted egocentric view. To this end, we propose LOKI (LOng term and Key Intentions), a novel large-scale dataset that is designed to tackle joint trajectory and intention prediction for heterogeneous traffic agents (pedestrians and vehicles) in an autonomous driving setting. The LOKI dataset is created to discover several factors that may affect intention, including i) agent's own will, ii) social interactions, iii) environmental constraints, and iv) contextual information. We also propose a model that jointly performs trajectory and intention prediction, showing that recurrently reasoning about intention can assist with trajectory prediction. We show our method outperforms state-of-the-art trajectory prediction methods by upto $27\%$ and also provide a baseline for frame-wise intention estimation.

【6】 Deployment of Deep Neural Networks for Object Detection on Edge AI Devices with Runtime Optimization 标题：运行时优化的深度神经网络在边缘AI设备上的目标检测部署 链接：https://arxiv.org/abs/2108.08166

作者：Lukas Stäcker,Juncong Fei,Philipp Heidenreich,Frank Bonarens,Jason Rambach,Didier Stricker,Christoph Stiller 机构：Stellantis, Opel Automobile GmbH, Germany, German Research Center for Artificial Intelligence, Germany, Institute of Measurement and Control Systems, Karlsruhe Institute of Technology, Germany, equal contribution 备注：To present in ICCV 2021 (ERCVAD Workshop) 摘要：通过不断提高检测性能的新算法，深度神经网络已被证明对汽车场景理解越来越重要。但是，很少强调在嵌入式环境中部署的经验和需求。因此，我们在edge AI平台上对两个具有代表性的目标检测网络的部署进行了案例研究。特别地，我们考虑基于图像的2D对象检测和基于激光雷达的3D物体检测的点柱。考虑到可用的工具，我们描述了将算法从PyTorch训练环境转换为部署环境所需的修改。我们使用两个不同的库TensorRT和TorchScript评估部署的DNN的运行时。在我们的实验中，我们观察到TensorRT对于卷积层和TorchScript对于完全连接层的轻微优势。我们还研究了在为部署选择优化设置时运行时和性能之间的权衡，并观察到量化显著降低了运行时，而对检测性能的影响很小。 摘要：Deep neural networks have proven increasingly important for automotive scene understanding with new algorithms offering constant improvements of the detection performance. However, there is little emphasis on experiences and needs for deployment in embedded environments. We therefore perform a case study of the deployment of two representative object detection networks on an edge AI platform. In particular, we consider RetinaNet for image-based 2D object detection and PointPillars for LiDAR-based 3D object detection. We describe the modifications necessary to convert the algorithms from a PyTorch training environment to the deployment environment taking into account the available tools. We evaluate the runtime of the deployed DNN using two different libraries, TensorRT and TorchScript. In our experiments, we observe slight advantages of TensorRT for convolutional layers and TorchScript for fully connected layers. We also study the trade-off between runtime and performance, when selecting an optimized setup for deployment, and observe that quantization significantly reduces the runtime while having only little impact on the detection performance.

【7】 Rendering and Tracking the Directional TSDF: Modeling Surface Orientation for Coherent Maps 标题：定向TSDF的渲染和跟踪：相干贴图的表面方向建模 链接：https://arxiv.org/abs/2108.08115

作者：Malte Splietker,Sven Behnke 机构：In: Proceedings of the ,th European Conference on Mobile Robots (ECMR) 备注：to be published in 10th European Conference on Mobile Robots (ECMR 2021) 摘要：RGB-D图像的密集实时跟踪和映射是许多机器人应用的重要工具，如导航或抓取。最近提出的方向截断有符号距离函数（DTSDF）是常规TSDF的一个扩展，显示了更相干映射和改进跟踪性能的潜力。在这项工作中，我们提出了从DTSDF渲染深度和颜色贴图的方法，使其成为现有跟踪器中常规TSDF的真正替代品。我们评估并表明，我们的方法提高了映射场景的可重用性。此外，我们添加了颜色集成，显著提高了相邻曲面的颜色正确性。 摘要：Dense real-time tracking and mapping from RGB-D images is an important tool for many robotic applications, such as navigation or grasping. The recently presented Directional Truncated Signed Distance Function (DTSDF) is an augmentation of the regular TSDF and shows potential for more coherent maps and improved tracking performance. In this work, we present methods for rendering depth- and color maps from the DTSDF, making it a true drop-in replacement for the regular TSDF in established trackers. We evaluate and show, that our method increases re-usability of mapped scenes. Furthermore, we add color integration which notably improves color-correctness at adjacent surfaces.

【8】 Combining Local and Global Viewpoint Planning for Fruit Coverage 标题：果品覆盖的局部与全局相结合的视点规划 链接：https://arxiv.org/abs/2108.08114

作者：Tobias Zaenker,Chris Lehnert,Chris McCool,Maren Bennewitz 机构： Bennewitz are with theUniversity of Bonn, Lehnert is with the Queensland Universityof Technology (QUT) 备注：7 pages, 7 figures, accepted at ECMR 2021. arXiv admin note: text overlap with arXiv:2011.00275 摘要：获取完整植物或植物部分（例如作物或水果）的3D传感器数据非常困难，因为它们的结构复杂且高度遮挡。然而，特别是对于水果的位置和大小的估计，需要尽可能避免遮挡并获取相关部分的传感器信息。现有的全局视点规划器建议一系列视点在一定程度上覆盖感兴趣的区域，但它们通常优先考虑全局覆盖，而不强调避免局部遮挡。另一方面，有一些方法旨在避免局部遮挡，但它们不能用于更大的环境，因为它们只能达到局部最大覆盖。因此，在本文中，我们建议将基于梯度的局部方法与全局视点规划相结合，以避免局部遮挡，同时仍然能够覆盖大面积区域。我们的模拟实验使用了一个配备了摄像机阵列和RGB-D摄像机的机械臂，结果表明，与仅应用全局覆盖规划相比，这种组合大大增加了感兴趣区域的覆盖范围。 摘要：Obtaining 3D sensor data of complete plants or plant parts (e.g., the crop or fruit) is difficult due to their complex structure and a high degree of occlusion. However, especially for the estimation of the position and size of fruits, it is necessary to avoid occlusions as much as possible and acquire sensor information of the relevant parts. Global viewpoint planners exist that suggest a series of viewpoints to cover the regions of interest up to a certain degree, but they usually prioritize global coverage and do not emphasize the avoidance of local occlusions. On the other hand, there are approaches that aim at avoiding local occlusions, but they cannot be used in larger environments since they only reach a local maximum of coverage. In this paper, we therefore propose to combine a local, gradient-based method with global viewpoint planning to enable local occlusion avoidance while still being able to cover large areas. Our simulated experiments with a robotic arm equipped with a camera array as well as an RGB-D camera show that this combination leads to a significantly increased coverage of the regions of interest compared to just applying global coverage planning.

【9】 Trust, Acceptance and Social Cues in Human-Robot Interaction 2021 -- SCRITA 标题：2021年人-机器人交互中的信任、接受与社会线索--Scrita 链接：https://arxiv.org/abs/2108.08092

作者：Alessandra Rossi,Patrick Holthaus,Sílvia Moros,Marcus Scheunemann,Gabriella Lakatos 备注：SCRITA workshop proceedings including 5 articles 摘要：本次研讨会旨在从多学科角度深入探讨人-机器人交互（HRI）中的信任和接受，包括机器人感知和感知其他代理、环境和人-机器人动力学的能力。研讨会与IEEE RO-MAN 2021一起在线举行（参见https://ro-man2021.org/). 三位受邀演讲人和六篇立场论文分析/讨论了人机交互的不同方面，这些方面可能影响、增强、破坏或恢复人类对机器人的信任，如社交线索的使用或行为透明度。不同背景的与会者就有效支持社会可接受和可信任机器人的设计和开发的相关挑战展开了动态对话。网站：https://scrita.herts.ac.uk/2021/ 摘要：This workshop aimed for a deeper exploration of trust and acceptance in human-robot interaction (HRI) from a multidisciplinary perspective including robots' capabilities of sensing and perceiving other agents, the environment, and human-robot dynamics. The workshop was held online in conjunction with IEEE RO-MAN 2021 (see https://ro-man2021.org/). Three invited speakers and six position papers analysed/discussed different aspects of human-robot interaction that can affect, enhance, undermine, or recover humans' trust in robots, such as the use of social cues or behaviour transparency. The attendees of different backgrounds engaged in a dynamic conversation about the relevant challenges of effectively supporting the design and development of socially acceptable and trustable robots. Website: https://scrita.herts.ac.uk/2021/

【10】 Panoramic Depth Estimation via Supervised and Unsupervised Learning in Indoor Scenes 标题：基于有监督和无监督学习的室内场景全景深度估计 链接：https://arxiv.org/abs/2108.08076

作者：Keyang Zhou,Kailun Yang,Kaiwei Wang 机构：State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou , China, Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology, Karlsruhe, Germany 备注：Accepted to Applied Optics. Code will be made publicly available at this https URL 摘要：深度估计作为将二维图像转换为三维空间的必要线索，已在许多机器视觉领域得到应用。然而，传统的立体匹配深度估计算法由于噪声大、精度低、对多摄像机标定要求严格等原因，难以实现全方位360度几何传感。在这项工作中，为了获得统一的周围感知，我们引入全景图像以获得更大的视野。我们将PADENet首次出现在我们之前的会议工作中，用于室外场景理解，以对室内场景进行全景单目深度估计。同时，针对全景图像的特点，改进了神经网络的训练过程。此外，我们将传统的立体匹配算法与深度学习方法相融合，进一步提高了深度预测的准确性。通过各种各样的实验，本研究证明了我们针对室内场景感知的方案的有效性。 摘要：Depth estimation, as a necessary clue to convert 2D images into the 3D space, has been applied in many machine vision areas. However, to achieve an entire surrounding 360-degree geometric sensing, traditional stereo matching algorithms for depth estimation are limited due to large noise, low accuracy, and strict requirements for multi-camera calibration. In this work, for a unified surrounding perception, we introduce panoramic images to obtain larger field of view. We extend PADENet first appeared in our previous conference work for outdoor scene understanding, to perform panoramic monocular depth estimation with a focus for indoor scenes. At the same time, we improve the training process of the neural network adapted to the characteristics of panoramic images. In addition, we fuse traditional stereo matching algorithm with deep learning methods and further improve the accuracy of depth predictions. With a comprehensive variety of experiments, this research demonstrates the effectiveness of our schemes aiming for indoor scene perception.

【11】 Optimised Informed RRTs for Mobile Robot Path Planning 标题：用于移动机器人路径规划的优化信息RRT 链接：https://arxiv.org/abs/2108.08051

作者：Bongani B. Maseko,Corné E. van Daalen,Johann Treurnicht 机构：Electronic Systems Laboratory, Department of Electrical & Electronic, Engineering, Stellenbosch University, South Africa 备注：6 pages, 3 figures, Control Conference Africa 2021, To be published on IFAC-PapersOnline 2021 摘要：基于基本快速探索随机树（RRT）的路径规划器快速有效，因此有利于实时机器人路径规划，但几乎肯定是次优的。相比之下，最优RRT（RRT*）收敛到最优解，但在实践中可能代价高昂。最近的工作重点是加快RRT*的收敛速度。最成功的策略是知情抽样、路径优化以及它们的组合。然而，这些加速方法尚未应用于基本RRT。此外，虽然可以使用一些路径优化方法来加快收敛速度，但缺乏对其有效性的比较。在本文中，我们研究了基于基本RRT和RRT*的知情抽样和路径优化的使用，以加速规划者，从而产生一系列称为优化知情RRT的算法。我们应用不同的路径优化方法并比较它们的有效性。目标是确定应用知情抽样和路径优化是否有助于基于基本RRT的快速（尽管几乎肯定是次优）路径规划人员获得与基于RRT*的规划人员相当或更好的性能。分析表明，无论是在计划时间有限的情况下，还是在计划时间较长的情况下，基于RRT的优化知情RRT都比基于RRT*的RRT获得更好的性能。 摘要：Path planners based on basic rapidly-exploring random trees (RRTs) are quick and efficient, and thus favourable for real-time robot path planning, but are almost-surely suboptimal. In contrast, the optimal RRT (RRT*) converges to the optimal solution, but may be expensive in practice. Recent work has focused on accelerating the RRT*'s convergence rate. The most successful strategies are informed sampling, path optimisation, and a combination thereof. However, these acceleration methods have not been applied to the basic RRT. Moreover, while a number of path optimisers can be used to accelerate the convergence rate, a comparison of their effectiveness is lacking. In this paper, we investigate the use of informed sampling and path optimisation to accelerate planners based on both the basic RRT and the RRT*, resulting in a family of algorithms known as optimised informed RRTs. We apply different path optimisers and compare their effectiveness. The goal is to ascertain if applying informed sampling and path optimisation can help the quick, though almost-surely suboptimal, path planners based on the basic RRT attain comparable or better performance than RRT*-based planners. Analyses show that RRT-based optimised informed RRTs do attain better performance than their RRT*-based counterparts, both when planning time is limited and when there is more planning time.

【12】 Navigating by Touch: Haptic Monte Carlo Localization via Geometric Sensing and Terrain Classification 标题：触觉导航：基于几何感知和地形分类的触觉蒙特卡罗定位 链接：https://arxiv.org/abs/2108.08015

作者：Russell Buchanan,Jakub Bednarek,Marco Camurri,Michał R. Nowicki,Krzysztof Walas,Maurice Fallon 机构：Received: date Accepted: date 备注：Autonomous Robots. arXiv admin note: substantial text overlap with arXiv:2005.01567 摘要：由于黑暗、空气模糊或传感器损坏，腿部机器人在极端环境中的导航可能会妨碍摄像头和激光扫描仪的使用。在这些条件下，本体感觉将继续可靠地工作。在本文中，我们提出了一种纯粹的本体感知定位算法，该算法融合了来自几何和地形类别的信息，用于在先验地图中定位腿部机器人。首先，地形分类器根据感测到的脚力计算脚踩在特定地形类别上的概率。然后，基于蒙特卡罗的估计器将该地形类别概率与脚接触点的几何信息融合。结果显示，该方法在线运行，并在ANYmal B300四足机器人上运行，该机器人在1.2km以上的范围内穿越一系列不同几何形状和地形类型的地形路线。该方法仅利用来自四足动物足部、IMU和关节的信息，将定位误差保持在20cm以下。 摘要：Legged robot navigation in extreme environments can hinder the use of cameras and laser scanners due to darkness, air obfuscation or sensor damage. In these conditions, proprioceptive sensing will continue to work reliably. In this paper, we propose a purely proprioceptive localization algorithm which fuses information from both geometry and terrain class, to localize a legged robot within a prior map. First, a terrain classifier computes the probability that a foot has stepped on a particular terrain class from sensed foot forces. Then, a Monte Carlo-based estimator fuses this terrain class probability with the geometric information of the foot contact points. Results are demonstrated showing this approach operating online and onboard a ANYmal B300 quadruped robot traversing a series of terrain courses with different geometries and terrain types over more than 1.2km. The method keeps the localization error below 20cm using only the information coming from the feet, IMU, and joints of the quadruped.

【13】 EPSILON: An Efficient Planning System for Automated Vehicles in Highly Interactive Environments 标题：Epsilon：一种高效的高度交互环境下的自动车辆规划系统 链接：https://arxiv.org/abs/2108.07993

作者：Wenchao Ding,Lu Zhang,Jing Chen,Shaojie Shen 机构：and the work was done while he was at the Hong Kong University of Scienceand Technology, Lu Zhang andShaojie Shen are with the Department of Electronic and Computer Engineer-ing, Hong Kong University of Science and Technology 备注：Accepted by the IEEE Transactions on Robotics (T-RO) 摘要：在本文中，我们提出了一个高效的高交互环境中自动车辆规划系统（EPSILON）。EPSILON是一种高效的自动驾驶交互感知规划系统，在模拟和现实密集城市交通中得到广泛验证。它遵循一个层次结构，包含一个交互行为规划层和一个基于优化的运动规划层。行为规划是由部分可观测马尔可夫决策过程（POMDP）制定的，但比单纯地将POMDP应用于决策问题要有效得多。效率的关键在于行动空间和观察空间中的引导分支，它将原始问题分解为有限数量的闭环策略评估。此外，我们还引入了一种新的驾驶员模型，该模型具有安全机制，以克服由先验知识的潜在不完善性引起的风险。对于运动规划，我们采用时空语义走廊（SSC）来统一建模复杂驾驶环境带来的约束。基于SSC，根据行为规划者提供的决策，优化安全平滑的轨迹。我们在模拟和现实密集交通中验证了我们的规划系统，实验结果表明，与现有的规划方法相比，我们的EPSILON在高度交互的交通流中平稳、安全地实现了类人驾驶行为。 摘要：In this paper, we present an Efficient Planning System for automated vehicles In highLy interactive envirONments (EPSILON). EPSILON is an efficient interaction-aware planning system for automated driving, and is extensively validated in both simulation and real-world dense city traffic. It follows a hierarchical structure with an interactive behavior planning layer and an optimization-based motion planning layer. The behavior planning is formulated from a partially observable Markov decision process (POMDP), but is much more efficient than naively applying a POMDP to the decision-making problem. The key to efficiency is guided branching in both the action space and observation space, which decomposes the original problem into a limited number of closed-loop policy evaluations. Moreover, we introduce a new driver model with a safety mechanism to overcome the risk induced by the potential imperfectness of prior knowledge. For motion planning, we employ a spatio-temporal semantic corridor (SSC) to model the constraints posed by complex driving environments in a unified way. Based on the SSC, a safe and smooth trajectory is optimized, complying with the decision provided by the behavior planner. We validate our planning system in both simulations and real-world dense traffic, and the experimental results show that our EPSILON achieves human-like driving behaviors in highly interactive traffic flow smoothly and safely without being over-conservative compared to the existing planning methods.

【14】 ARDOP: A Versatile Humanoid Robotic Research Platform 标题：ARDOP：一个通用的类人机器人研究平台 链接：https://arxiv.org/abs/2108.07983

作者：Sudarshan S Harithas,Harish V Mekali 机构：BMS College of Engineering, Bangalore, Karnataka, India 备注：Project page this https URL 摘要：本文描述了一种称为ARDOP的仿人机器人的开发。该项目的目标是提供一种模块化、开源、廉价的仿人机器人，使研究人员能够回答与机器人操作和感知相关的各种问题。ARDOP主要由两个功能单元组成，即感知和操纵系统。在这里，我们讨论这些系统的概念化和设计方法，并在仿真和各种定制设计的实验上展示它们的性能结果。 摘要：This paper describes the development of a humanoid robot called ARDOP. The goal of the project is to provide a modular, open-source, and inexpensive humanoid robot that would enable researchers to answer various problems related to robotic manipulation and perception. ARDOP primarily comprises of two functional units namely the perception and manipulation system, here we discuss the conceptualization and design methodology of these systems and proceed to present their performance results on simulation and various custom-designed experiments.

【15】 An Empirical Testing of Autonomous Vehicle Simulator System for Urban Driving 标题：城市自动驾驶车辆模拟器系统的实证测试 链接：https://arxiv.org/abs/2108.07910

作者：John Seymour,Dac-Thanh-Chuong Ho,Quang-Hung Luu 机构：Department of Computer Science and, Software Engineering, Swinburne University of Technology, Hawthorn, VIC , Australia 备注：7 pages, 7 figures, 4 tables 摘要：安全是禁止自动驾驶车辆（AV）的主要挑战之一，要求在允许上路之前对其进行良好测试。与道路试验相比，模拟器使我们能够方便且经济地验证AV。然而，目前尚不清楚如何最好地使用基于AV的模拟器系统进行有效测试。本文介绍了一个结合SVL模拟器和阿波罗平台的AV模拟器系统的实证测试。我们提出了576个测试案例，这些案例的灵感来自于行人和周围车辆的四种自然驾驶情况。我们发现SVL可以模拟真实的安全和碰撞情况；同时，阿波罗可以非常安全地驾驶汽车。另一方面，我们注意到该系统在四个等级中有三个未能检测到道路上的行人或车辆，占测试场景总数的10.0%。我们进一步应用变形测试来识别系统中的不一致性，增加了486个测试用例。然后，我们讨论了在现实生活中可能导致危险情况的情景的一些见解。总之，本文提供了一个新的实证证据，以加强基于模拟器的系统可以成为AV综合测试不可或缺的工具的主张。 摘要：Safety is one of the main challenges that prohibit autonomous vehicles (AV), requiring them to be well tested ahead of being allowed on the road. In comparison with road tests, simulators allow us to validate the AV conveniently and affordably. However, it remains unclear how to best use the AV-based simulator system for testing effectively. Our paper presents an empirical testing of AV simulator system that combines the SVL simulator and the Apollo platform. We propose 576 test cases which are inspired by four naturalistic driving situations with pedestrians and surrounding cars. We found that the SVL can imitate realistic safe and collision situations; and at the same time, Apollo can drive the car quite safely. On the other hand, we noted that the system failed to detect pedestrians or vehicles on the road in three out of four classes, accounting for 10.0% total number of scenarios tested. We further applied metamorphic testing to identify inconsistencies in the system with additional 486 test cases. We then discussed some insights into the scenarios that may cause hazardous situations in real life. In summary, this paper provides a new empirical evidence to strengthen the assertion that the simulator-based system can be an indispensable tool for a comprehensive testing of the AV.

【16】 Diversity-based Trajectory and Goal Selection with Hindsight Experience Replay 标题：基于多样性的轨迹和目标选择及其后见之明经验回放 链接：https://arxiv.org/abs/2108.07887

作者：Tianhong Dai,Hengyan Liu,Kai Arulkumaran,Guangyu Ren,Anil Anthony Bharath 机构： Imperial College London, London SW,AZ, UK, Araya Inc., Tokyo ,-, Japan 摘要：事后经验重播（HER）是一种目标重新标记技术，通常与非策略深度强化学习算法一起用于解决面向目标的任务；它非常适合只提供少量奖励的机器人操作任务。在HER中，轨迹和过渡都是统一采样进行训练的。然而，并不是所有代理的经验对训练都有同样的贡献，因此天真的统一采样可能会导致学习效率低下。在本文中，我们提出了基于多样性的轨迹和目标选择（DTGSH）。首先，根据行列式点过程（DPP）模拟的目标状态的多样性对轨迹进行采样。其次，使用k-dpp从轨迹中选择具有不同目标状态的过渡。我们在模拟机器人环境中评估了五个具有挑战性的机器人操作任务中的DTGSH，结果表明，与其他最先进的方法相比，我们的方法在所有任务中都能更快地学习并达到更高的性能。 摘要：Hindsight experience replay (HER) is a goal relabelling technique typically used with off-policy deep reinforcement learning algorithms to solve goal-oriented tasks; it is well suited to robotic manipulation tasks that deliver only sparse rewards. In HER, both trajectories and transitions are sampled uniformly for training. However, not all of the agent's experiences contribute equally to training, and so naive uniform sampling may lead to inefficient learning. In this paper, we propose diversity-based trajectory and goal selection with HER (DTGSH). Firstly, trajectories are sampled according to the diversity of the goal states as modelled by determinantal point processes (DPPs). Secondly, transitions with diverse goal states are selected from the trajectories by using k-DPPs. We evaluate DTGSH on five challenging robotic manipulation tasks in simulated robot environments, where we show that our method can learn more quickly and reach higher performance than other state-of-the-art approaches on all tasks.