机器人相关学术速递[7.27]

访问www.arxivdaily.com获取含摘要速递，涵盖CS|物理|数学|经济|统计|金融|生物|电气领域，更有搜索、收藏、发帖等功能！点击阅读原文即可访问

cs.RO机器人相关，共计16篇

【1】 The Holy Grail of Multi-Robot Planning: Learning to Generate Online-Scalable Solutions from Offline-Optimal Experts 标题：多机器人规划的圣杯：学习从离线最优专家那里生成在线可扩展的解决方案

作者：Amanda Prorok,Jan Blumenkamp,Qingbiao Li,Ryan Kortvelesy,Zhe Liu,Ethan Stump 机构：Department of Computer Science and Technology, University of Cambridge, UK, DEVCOM Army Research Laboratory (ARL), Maryland, USA. 链接：https://arxiv.org/abs/2107.12254 摘要：许多多机器人规划问题都受到维数灾难的影响，这使得求解大规模问题的难度加大。基于学习的方法在多机器人规划中的应用前景广阔，因为它使我们能够将昂贵但最优的求解器的在线计算负担转移到离线学习过程中。简单地说，其思想是训练一个策略来复制一个小规模系统生成的最优模式，然后将该策略转移到更大的系统中，希望学习到的策略能够扩展，同时保持接近最优的性能。然而，许多问题阻碍我们充分发挥这一想法的潜力。这份蓝天报告阐述了一些仍然存在的关键挑战。 摘要：Many multi-robot planning problems are burdened by the curse of dimensionality, which compounds the difficulty of applying solutions to large-scale problem instances. The use of learning-based methods in multi-robot planning holds great promise as it enables us to offload the online computational burden of expensive, yet optimal solvers, to an offline learning procedure. Simply put, the idea is to train a policy to copy an optimal pattern generated by a small-scale system, and then transfer that policy to much larger systems, in the hope that the learned strategy scales, while maintaining near-optimal performance. Yet, a number of issues impede us from leveraging this idea to its full potential. This blue-sky paper elaborates some of the key challenges that remain.

【2】 Integer-Programming-Based Narrow-Passage Multi-Robot Path Planning with Effective Heuristics 标题：基于整数规划的有效启发式窄通道多机器人路径规划

作者：Jiaxi Huo,Ronghao Zheng,Meiqin Liu,Senlin Zhang 机构： Zhe-jiang University 链接：https://arxiv.org/abs/2107.12219 摘要：为了提高仓库环境下多机器人系统（MRS）的效率，研究了图上多机器人最优路径规划问题。提出了一种基于整数规划（IP）方法的单向多机器人路径规划（OMRPP）算法。我们致力于降低由一组机器人在类似仓库的环境中从初始配置移动到目标配置所带来的成本。本文的创新之处在于：（1）提出了一种基于类仓库环境特性的拓扑图提取方法，减少了构建的IP模型的规模(2） 提出单向通道约束，防止机器人在通道中发生不可解的碰撞(3） 提出了一种启发式体系结构，使IP模型始终具有可行的初始解，以保证其可解性。大量的仿真实验证明了该算法的有效性和性能。 摘要：We study optimal Multi-robot Path Planning (MPP) on graphs, in order to improve the efficiency of multi-robot system (MRS) in the warehouse-like environment. We propose a novel algorithm, OMRPP (One-way Multi-robot Path Planning) based on Integer programming (IP) method. We focus on reducing the cost caused by a set of robots moving from their initial configuration to goal configuration in the warehouse-like environment. The novelty of this work includes: (1) proposing a topological map extraction based on the property of warehouse-like environment to reduce the scale of constructed IP model; (2) proposing one-way passage constraint to prevent the robots from having unsolvable collisions in the passage. (3) developing a heuristic architecture that IP model can always have feasible initial solution to ensure its solvability. Numerous simulations demonstrate the efficiency and performance of the proposed algorithm.

【3】 Robotic Occlusion Reasoning for Efficient Object Existence Prediction 标题：高效预测物体存在的机器人遮挡推理

作者：Mengdi Li,Cornelius Weber,Matthias Kerzel,Jae Hee Lee,Zheni Zeng,Zhiyuan Liu,Stefan Wermter 机构： Department of Informatics, University of Ham-burg 备注：Accepted at IROS 2021 链接：https://arxiv.org/abs/2107.12095 摘要：对潜在遮挡的推理是机器人有效预测环境中物体是否存在的关键。虽然已有的研究表明，具有主动感知的机器人可以完成各种任务，但遮挡推理能否实现还不清楚。为了回答这个问题，我们引入了机器人对象存在性预测的任务：当被问及一个对象时，机器人需要围绕一个随机放置对象的桌子移动尽可能少的步来预测被查询对象是否存在。针对这一问题，我们提出了一种新的递归神经网络模型，该模型可以通过课程训练策略与监督学习和强化学习相结合进行训练。实验结果表明：（1）主动感知和遮挡推理是成功完成任务的必要条件；2） 该模型具有很好的遮挡推理能力，其预测精度与穷尽探测基线相当，平均只需要10\%$左右的基线运动步数；3）该模型推广到新的目标组合，精度损失不大。 摘要：Reasoning about potential occlusions is essential for robots to efficiently predict whether an object exists in an environment. Though existing work shows that a robot with active perception can achieve various tasks, it is still unclear if occlusion reasoning can be achieved. To answer this question, we introduce the task of robotic object existence prediction: when being asked about an object, a robot needs to move as few steps as possible around a table with randomly placed objects to predict whether the queried object exists. To address this problem, we propose a novel recurrent neural network model that can be jointly trained with supervised and reinforcement learning methods using a curriculum training strategy. Experimental results show that 1) both active perception and occlusion reasoning are necessary to successfully achieve the task; 2) the proposed model demonstrates a good occlusion reasoning ability by achieving a similar prediction accuracy to an exhaustive exploration baseline while requiring only about $10\%$ of the baseline's number of movement steps on average; and 3) the model generalizes to novel object combinations with a moderate loss of accuracy.

【4】 Pluto: Motion Detection for Navigation in a VR Headset 标题：冥王星：VR头戴式耳机中用于导航的运动检测

作者：Dmitri Kovalenko,Artem Migukin,Svetlana Ryabkova,Vitaly Chernov 机构：Samsung R&D Institute Russia 备注：to appear in 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 29 Nov. - 2 Dec. 2021, Lloret de Mar, Spain 链接：https://arxiv.org/abs/2107.12030

【5】 Autonomous Coordinated Control of the Light Guide for Positioning in Vitreoretinal Surgery 标题：玻璃体视网膜手术中光导定位的自主协调控制

作者：Yuki Koyama,Murilo M. Marinho,Mamoru Mitsuishi,Kanako Harada 机构： Marinho)The authors are with the Department of Mechanical Engineering, theUniversity of Tokyo 备注：12 pages, 14 figures, Under Review for the IEEE Transactions on Medical Robotics and Bionics (T-MRB) 链接：https://arxiv.org/abs/2107.11985

【6】 CP-loss: Connectivity-preserving Loss for Road Curb Detection in Autonomous Driving with Aerial Images 标题：CP-Loss：航空影像自动驾驶中道路路缘检测的连通性保持损失

作者：Zhenhua Xu,Yuxiang Sun,Lujia Wang,Ming Liu 机构： TheHong Kong Polytechnic University 备注：Accepted by The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2021 链接：https://arxiv.org/abs/2107.11920 摘要：道路边缘检测对于自动驾驶非常重要。它可以用来确定道路边界，限制车辆在道路上行驶，从而避免潜在的事故。目前的大多数方法都是使用车载传感器（如摄像头或三维激光雷达）在线检测路缘。然而，这些方法通常会遇到严重的遮挡问题。特别是在高度动态的交通环境中，大部分视场被动态对象占据。为了缓解这一问题，本文利用高分辨率航空图像离线检测路缘石。此外，检测到的路缘可用于为自动驾驶车辆创建高清（HD）地图。具体来说，我们首先预测路缘石的像素分割图，然后进行一系列的后处理步骤来提取路缘石的图形结构。为了解决分割图中的不连通性问题，我们提出了一种新的保持连通性丢失（CP-loss）方法来提高分割性能。在公共数据集上的实验结果证明了本文提出的损失函数的有效性。本文附有演示视频和补充文件，可在\texttt{\url上找到{https://sites.google.com/view/cp-loss}}. 摘要：Road curb detection is important for autonomous driving. It can be used to determine road boundaries to constrain vehicles on roads, so that potential accidents could be avoided. Most of the current methods detect road curbs online using vehicle-mounted sensors, such as cameras or 3-D Lidars. However, these methods usually suffer from severe occlusion issues. Especially in highly-dynamic traffic environments, most of the field of view is occupied by dynamic objects. To alleviate this issue, we detect road curbs offline using high-resolution aerial images in this paper. Moreover, the detected road curbs can be used to create high-definition (HD) maps for autonomous vehicles. Specifically, we first predict the pixel-wise segmentation map of road curbs, and then conduct a series of post-processing steps to extract the graph structure of road curbs. To tackle the disconnectivity issue in the segmentation maps, we propose an innovative connectivity-preserving loss (CP-loss) to improve the segmentation performance. The experimental results on a public dataset demonstrate the effectiveness of our proposed loss function. This paper is accompanied with a demonstration video and a supplementary document, which are available at \texttt{\url{https://sites.google.com/view/cp-loss}}.

【7】 Learning from Successful and Failed Demonstrations via Optimization 标题：通过优化从成功和失败的演示中学习

作者：Brendan Hertel,S. Reza Ahmadzadeh 机构：UniversityofMassachusettsLowell 备注：6 pages, 7 figures. Accepted to IROS 2021. Code available at this https URL Accompanying video at: this https URL 链接：https://arxiv.org/abs/2107.11918

【8】 Improving Robot Localisation by Ignoring Visual Distraction 标题：忽略视觉干扰改善机器人定位

作者：Oscar Mendez,Matthew Vowels,Richard Bowden 机构：All authors with the University of Surrey {o 备注：2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 链接：https://arxiv.org/abs/2107.11857 摘要：注意力是现代深度学习的重要组成部分。然而，人们对它的反面却不太重视：忽略分心。我们的日常生活要求我们明确避免关注那些让我们试图完成的任务感到困惑的突出的视觉特征。这种视觉优先顺序使我们能够专注于重要的任务，而忽略视觉干扰。在这项工作中，我们引入了神经盲，它使代理能够完全忽略被认为是分心的对象或类。更明确地说，我们的目标是使一个神经网络完全不能代表特定的选择类在其潜在的空间。在非常真实的意义上，这使得网络对某些类“视而不见”，允许和代理专注于对给定任务重要的内容，并演示了如何使用这些内容来改进本地化。 摘要：Attention is an important component of modern deep learning. However, less emphasis has been put on its inverse: ignoring distraction. Our daily lives require us to explicitly avoid giving attention to salient visual features that confound the task we are trying to accomplish. This visual prioritisation allows us to concentrate on important tasks while ignoring visual distractors. In this work, we introduce Neural Blindness, which gives an agent the ability to completely ignore objects or classes that are deemed distractors. More explicitly, we aim to render a neural network completely incapable of representing specific chosen classes in its latent space. In a very real sense, this makes the network "blind" to certain classes, allowing and agent to focus on what is important for a given task, and demonstrates how this can be used to improve localisation.

【9】 Adaptive Identification of Legged Robotic Kinematic Structure 标题：腿式机器人运动学结构的自适应辨识

作者：Bolun Dai 机构：Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 链接：https://arxiv.org/abs/2107.11836

【10】 Reinforcement Learning Compensated Extended Kalman Filter for Attitude Estimation 标题：基于强化学习补偿的扩展卡尔曼过滤姿态估计

作者：Yujie Tang,Liang Hu,Qingrui Zhang,Wei Pan 机构： Pan are with the Department of Cognitive Robotics, Delft University of Technology, Hu is with the School of Computer Science and Electronic Engineering, University of Essex, Zhang is with the School of Aeronautics and Astronautics, SunYat-Sen University 链接：https://arxiv.org/abs/2107.11777

【11】 DR2L: Surfacing Corner Cases to Robustify Autonomous Driving via Domain Randomization Reinforcement Learning 标题：DR2L：基于领域随机化强化学习的机动车自动驾驶表面化

作者：Haoyi Niu,Jianming Hu,Zheyu Cui,Yi Zhang 机构：Department of, AutomationTsinghua, University, Beijing, China, , edu.cn 备注：8 pages, 7 figures 链接：https://arxiv.org/abs/2107.11762 摘要：在深度强化学习（DeepRL）自主驾驶的背景下，如何尽可能有效和彻底地探索弯道案例一直是人们关注的焦点之一。用模拟数据进行训练比用真实数据进行训练成本低、危险性小，但由于参数分布的不一致性和模拟器中系统建模的不正确性，往往导致不可避免的Sim2real缺口，这可能是NEW性能不佳的原因，模拟器难以产生的异常和危险案例。领域随机化（DR）是一种可以在很少或没有真实数据的情况下弥补这一差距的方法。因此，本研究提出了一个对抗性模型，以使在模拟中训练的基于DeepRL的自主车辆能够逐渐地在较难的事件中进行表面处理，从而使模型能够很容易地转移到现实世界中。 摘要：How to explore corner cases as efficiently and thoroughly as possible has long been one of the top concerns in the context of deep reinforcement learning (DeepRL) autonomous driving. Training with simulated data is less costly and dangerous than utilizing real-world data, but the inconsistency of parameter distribution and the incorrect system modeling in simulators always lead to an inevitable Sim2real gap, which probably accounts for the underperformance in novel, anomalous and risky cases that simulators can hardly generate. Domain Randomization(DR) is a methodology that can bridge this gap with little or no real-world data. Consequently, in this research, an adversarial model is put forward to robustify DeepRL-based autonomous vehicles trained in simulation to gradually surfacing harder events, so that the models could readily transfer to the real world.

【12】 Learning Risk-aware Costmaps for Traversability in Challenging Environments 标题：学习具有风险意识的成本图，以便在具有挑战性的环境中实现可旅行性

作者：David D. Fan,Ali-akbar Agha-mohammadi,Evangelos A. Theodorou 机构： often computing traversabilitymeans calculating worst-case bounds on the uncertainty of 1Institute for Robotics and Intelligent Machines, Georgia Institute ofTechnology, California Institute of Technology 链接：https://arxiv.org/abs/2107.11722 摘要：在未知和非结构化环境中，机器人自主探索和导航的主要挑战之一是确定机器人可以或不能安全移动的位置。这种确定的一个重要困难来源是随机性和不确定性，来自定位误差、传感器稀疏性和噪声、难以建模的机器人-地面相互作用以及对车辆运动的干扰。解决这个问题的经典方法依赖于对周围地形的几何分析，这很容易产生建模错误，并且计算成本很高。此外，对不确定可遍历性代价的分布进行建模是一项困难的任务，由于上述各种误差源的存在，使得建模变得更加复杂。在这项工作中，我们采取原则性的学习方法来解决这个问题。我们介绍了一个神经网络结构的鲁棒学习分布的遍历性成本。由于我们的动机是保护机器人的生命，因此我们从学习尾部风险的角度来解决这个学习问题，即条件风险值（CVaR）。我们证明，这种方法可靠地学习期望的尾部风险给定一个期望的概率风险阈值在0和1之间，产生了一个遍历性成本图，它对异常值更稳健，更准确地捕捉尾部风险，并且与基线相比计算效率更高。我们通过一个步行机器人在充满挑战的非结构化环境中（包括废弃的地铁、石灰岩洞穴和熔岩管洞穴）进行数据采集，验证了我们的方法。 摘要：One of the main challenges in autonomous robotic exploration and navigation in unknown and unstructured environments is determining where the robot can or cannot safely move. A significant source of difficulty in this determination arises from stochasticity and uncertainty, coming from localization error, sensor sparsity and noise, difficult-to-model robot-ground interactions, and disturbances to the motion of the vehicle. Classical approaches to this problem rely on geometric analysis of the surrounding terrain, which can be prone to modeling errors and can be computationally expensive. Moreover, modeling the distribution of uncertain traversability costs is a difficult task, compounded by the various error sources mentioned above. In this work, we take a principled learning approach to this problem. We introduce a neural network architecture for robustly learning the distribution of traversability costs. Because we are motivated by preserving the life of the robot, we tackle this learning problem from the perspective of learning tail-risks, i.e. the Conditional Value-at-Risk (CVaR). We show that this approach reliably learns the expected tail risk given a desired probability risk threshold between 0 and 1, producing a traversability costmap which is more robust to outliers, more accurately captures tail risks, and is more computationally efficient, when compared against baselines. We validate our method on data collected a legged robot navigating challenging, unstructured environments including an abandoned subway, limestone caves, and lava tube caves.

【13】 An Internal Arc Fixation Channel and Automatic Planning Algorithm for Pelvic Fracture 标题：骨盆骨折弧形内固定通道及自动规划算法

作者：Qing Yang,Jian Song,Chang Cheng,Chao Shi,Chendi Liang,Yu Wang 机构：School of Biological Science and Medical Engineering, Beihang University, Beijing, China, Department of Mathematics and Computer Science, Colorado College, Colorado, USA 备注：8 pages,6 figures, submitted to RCAE 2021 链接：https://arxiv.org/abs/2107.11710

【14】 One-Leg Stance of Humanoid Robot using Active Balance Control 标题：基于主动平衡控制的仿人机器人单腿站姿

作者：Tri Duc Tran,Anh Khoa Lanh Luu,Van Tu Duong,Huy Hung Nguyen,Tan Tien Nguyen 机构：National Key Laboratory of Digital Control and System Engineering, Ho Chi Minh City University of, Technology, VNU-HCM, Hochiminh city, Vietnam., The Department of Mechatronics, Ho Chi Minh City University of Technology, VNU-HCM, Hochiminh city 链接：https://arxiv.org/abs/2107.11703

【15】 Group-based Motion Prediction for Navigation in Crowded Environments 标题：拥挤环境下基于群体的导航运动预测

作者：Allan Wang,Christoforos Mavrogiannis,Aaron Steinfeld 机构：Robotics Institute, Carnegie Mellon University, Paul G. Allen School of Computer Science & Engineering, University of Washington 链接：https://arxiv.org/abs/2107.11637

【16】 Multi-Start n-Dimensional Lattice Planning with Optimal Motion Primitives 标题：基于最优运动基元的多起点n维格点规划

作者：Alexander Botros,Stephen L. Smith 机构： smoothing algorithmsThis work is supported in part by the Natural Sciences and EngineeringResearch Council of Canada (NSERC)The authors are with the Department of Electrical and Computer Engineer-ing, University of Waterloo, 200 University Ave W 备注：12 pages, 8 figures, 2 tables, to be submitted to IEEE Transactions on Intelligent Vehicles (IV) 链接：https://arxiv.org/abs/2107.11467