机器人相关学术速递[6.23]

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cs.RO机器人相关，共计14篇

【1】 Analysis of Executional and Procedural Errors in Dry-lab Robotic Surgery Experiments 标题：干实验室机器人手术实验中的执行错误和程序错误分析

作者：Kay Hutchinson,Zongyu Li,Leigh A. Cantrell,Noah S. Schenkman,Homa Alemzadeh 机构： 1 Department of Electrical and Computer Engineering, 2 Department of Obstetrics and Gynecology, 3 Department of Urology, University of Virginia 备注：18 pages, 14 figures, 6 tables. Submitted to The International Journal of Medical Robotics and Computer Assisted Surgery (IJMRCAS). Supplementary video files are available at this https URL 链接：https://arxiv.org/abs/2106.11962 摘要：背景：我们的目标是开发一种自动检测潜在错误动作的方法，这些错误动作会导致机器人辅助手术中的次优外科医生表现和安全关键事件。方法我们开发了一个用于识别特定于任务和手势的执行和程序错误的评估标准，并从JIGSAWS数据集中评估缝合和穿针任务的干实验室演示。我们通过标记视频数据来描述演示的错误部分，并使用分布相似性分析和运动数据的轨迹平均来识别区分错误手势的参数。结果执行错误频率随任务和动作的不同而变化，并与技能水平相关。通过分析特定于误差的运动学参数，可以区分每个姿态中的主要误差模式。程序错误可能导致较低的表现分数和增加的演示时间，但也取决于手术方式。结论本研究提供了初步的证据，自动错误检测可以提供上下文相关的定量反馈给外科受训人员的性能改进。 摘要：Background We aim to develop a method for automated detection of potentially erroneous motions that lead to sub-optimal surgeon performance and safety-critical events in robot-assisted surgery. Methods We develop a rubric for identifying task and gesture-specific Executional and Procedural errors and evaluate dry-lab demonstrations of Suturing and Needle Passing tasks from the JIGSAWS dataset. We characterize erroneous parts of demonstrations by labeling video data, and use distribution similarity analysis and trajectory averaging on kinematic data to identify parameters that distinguish erroneous gestures. Results Executional error frequency varies by task and gesture and correlates with skill level. Some predominant error modes in each gesture are distinguishable by analyzing error-specific kinematic parameters. Procedural errors could lead to lower performance scores and increased demonstration times but also depend on surgical style. Conclusions This study provides preliminary evidence that automated error detection can provide context-dependent and quantitative feedback to surgical trainees for performance improvement.

【2】 Failing with Grace: Learning Neural Network Controllers that are Boundedly Unsafe 标题：GRACE失败：学习有界不安全的神经网络控制器

作者：Panagiotis Vlantis,Michael M. Zavlanos 机构：DukeUniversity 链接：https://arxiv.org/abs/2106.11881 摘要：在这项工作中，我们考虑学习一个前馈神经网络（NN）控制器的问题，以安全地引导一个任意形状的平面机器人在一个紧凑和障碍闭塞的工作空间。与现有方法强烈依赖于接近安全状态空间边界的数据点密度来训练具有闭环安全保证的神经网络控制器不同，我们提出了一种方法，该方法取消了在实际中难以满足的数据上的假设，并允许优美的安全违规，即。，在空间上可以控制的有界大小的。为此，我们采用可达性分析方法来封装训练过程中的安全约束。具体地说，为了获得闭环系统前向可达集的一个计算效率高的过逼近，我们将机器人的状态空间划分为若干个单元，并在训练好的控制律下自适应地细分包含可能脱离安全集的状态的单元。为了做到这一点，我们首先设计适当的欠近似和过近似的机器人的足迹，自适应地细分配置空间成细胞。然后，利用每个单元的前向可达集和不可行机器人配置集之间的重叠作为安全违规的度量，我们在损失函数中引入惩罚项来惩罚训练过程中的这种重叠。因此，我们的方法可以为闭环系统学习一个安全向量场，同时，通过闭环系统的前向可达集的过逼近与不安全状态集的重叠，给出整个配置空间上安全违规的数值最坏情况界。此外，它还可以控制计算复杂度和这些边界的紧密性之间的折衷。最后，我们进行了仿真研究，验证了该方案的有效性。 摘要：In this work, we consider the problem of learning a feed-forward neural network (NN) controller to safely steer an arbitrarily shaped planar robot in a compact and obstacle-occluded workspace. Unlike existing methods that depend strongly on the density of data points close to the boundary of the safe state space to train NN controllers with closed-loop safety guarantees, we propose an approach that lifts such assumptions on the data that are hard to satisfy in practice and instead allows for graceful safety violations, i.e., of a bounded magnitude that can be spatially controlled. To do so, we employ reachability analysis methods to encapsulate safety constraints in the training process. Specifically, to obtain a computationally efficient over-approximation of the forward reachable set of the closed-loop system, we partition the robot's state space into cells and adaptively subdivide the cells that contain states which may escape the safe set under the trained control law. To do so, we first design appropriate under- and over-approximations of the robot's footprint to adaptively subdivide the configuration space into cells. Then, using the overlap between each cell's forward reachable set and the set of infeasible robot configurations as a measure for safety violations, we introduce penalty terms into the loss function that penalize this overlap in the training process. As a result, our method can learn a safe vector field for the closed-loop system and, at the same time, provide numerical worst-case bounds on safety violation over the whole configuration space, defined by the overlap between the over-approximation of the forward reachable set of the closed-loop system and the set of unsafe states. Moreover, it can control the tradeoff between computational complexity and tightness of these bounds. Finally, we provide a simulation study that verifies the efficacy of the proposed scheme.

【3】 Formation Control with Lane Preference for Connected and Automated Vehicles in Multi-lane Scenarios 标题：多车道场景下基于车道偏好的连通自动车辆编队控制

作者：Mengchi Cai,Chaoyi Chen,Jiawei Wang,Qing Xu,Keqiang Li,Jianqiang Wang,Xiangbin Wu 机构：School of Vehicle and Mobility, Tsinghua University, Beijing, China, Intel Lab China, Beijing, China 链接：https://arxiv.org/abs/2106.11763 摘要：多车道道路是现实交通系统中的典型场景。车辆通常根据其路线和目的地优先选择车道。现有的研究很少关注控制车辆在期望车道上行驶的问题。提出了一种考虑不同车道上车辆偏好的编队控制方法。采用双层编队控制框架规划车辆的无碰撞运动，上层进行相对目标分配和路径规划，下层进行轨迹规划和跟踪。将考虑车道偏好的无碰撞多车辆路径规划问题分解为两个子问题：计算不降低费用的分配表和根据给定的分配结果规划无碰撞路径。利用基于冲突的搜索（CBS）方法，根据给定的分配结果规划车辆的无碰撞路径。以三车道道路为例进行了实例分析和仿真。结果表明，与基于规则的方法相比，本文提出的编队控制方法在大流量情况下显著降低了拥塞，提高了交通效率。 摘要：Multi-lane roads are typical scenarios in the real-world traffic system. Vehicles usually have preference on lanes according to their routes and destinations. Few of the existing studies looks into the problem of controlling vehicles to drive on their desired lanes. This paper proposes a formation control method that considers vehicles' preference on different lanes. The bi-level formation control framework is utilized to plan collision-free motion for vehicles, where relative target assignment and path planning are performed in the upper level, and trajectory planning and tracking are performed in the lower level. The collision-free multi-vehicle path planning problem considering lane preference is decoupled into two sub problems: calculating assignment list with non-decreasing cost and planning collision-free paths according to given assignment result. The Conflict-based Searching (CBS) method is utilized to plan collision-free paths for vehicles based on given assignment results. Case study is conducted and simulations are carried out in a three-lane road scenario. The results indicate that the proposed formation control method significantly reduces congestion and improves traffic efficiency at high traffic volumes, compared to the rule-based method.

【4】 Experiments in Artificial Culture: from noisy imitation to storytelling robots 标题：人工文化实验：从嘈杂的模仿到讲故事的机器人

作者：Alan F. T. Winfield,Susan Blackmore 机构：Department of Psychology, University of Plymouth, Plymouth PL,AA 链接：https://arxiv.org/abs/2106.11754 摘要：本文提出了一系列的实验，在集体社会机器人，跨越10多年，以建立长期的目标体现模型（方面）的文化进化。最初的实验表明，一组被编程用来模仿彼此行为的社交机器人（我们称之为模仿机器人）中出现了行为传统。这些实验表明，真实的物理机器人免费提供的噪音（即低于完美逼真度）模仿自然会引起社会学习的变化。最近的实验工作通过基于模拟的内部模型扩展了机器人的认知能力，为其配备了简单的人工思维理论。在我们目前的工作中，通过这种扩展的能力，我们探索了不是通过模仿而是通过机器人讲故事的社会学习，以努力模拟这种非常人性化的文化传播模式。本文介绍了这些实验的方法和启示，实验及其结果，并概述了本研究的可能方向。我们希望这篇论文不仅能激发讨论，而且能为假设提供建议，以便用故事机器人进行测试。 摘要：This paper presents a series of experiments in collective social robotics, spanning more than 10 years, with the long-term aim of building embodied models of (aspects) of cultural evolution. Initial experiments demonstrated the emergence of behavioural traditions in a group of social robots programmed to imitate each other's behaviours (we call these Copybots). These experiments show that the noisy (i.e. less than perfect fidelity) imitation that comes for free with real physical robots gives rise naturally to variation in social learning. More recent experimental work extends the robots' cognitive capabilities with simulation-based internal models, equipping them with a simple artificial theory of mind. With this extended capability we explore, in our current work, social learning not via imitation but robot-robot storytelling, in an effort to model this very human mode of cultural transmission. In this paper we give an account of the methods and inspiration for these experiments, the experiments and their results, and an outline of possible directions for this programme of research. It is our hope that this paper stimulates not only discussion but suggestions for hypotheses to test with the Storybots.

【5】 Robust EMRAN based Neural Aided Learning Controller for Autonomous Vehicles 标题：基于EMRAN的自主车辆鲁棒神经辅助学习控制器

作者：Sauranil Debarshi,Suresh Sundaram,Narasimhan Sundararajan 链接：https://arxiv.org/abs/2106.11716 摘要：提出了一种基于在线进化神经网络的汽车纵向和横向逆动力学学习控制器。车辆的逆动力学近似使用反馈误差学习机制，该机制利用动态径向基函数神经网络，称为扩展最小资源分配网络（EMRAN）。EMRAN使用扩展的Kalman滤波方法进行学习，增长/剪枝条件有助于保持隐藏神经元的数量最少。在线学习算法有助于处理道路上的不确定性和动态变化以及未知干扰。所提出的控制结构采用两个耦合的常规控制器，并辅以EMRAN逆动力学控制器。控制结构有一个常规的PID控制器用于巡航控制和一个Stanley控制器用于路径跟踪。将纵向和横向控制器的性能与现有的控制方法进行了比较，结果表明，所提出的控制方案能较好地处理干扰和参数不确定性，在自主车辆中具有较好的跟踪性能。 摘要：This paper presents an online evolving neural network-based inverse dynamics learning controller for an autonomous vehicles' longitudinal and lateral control under model uncertainties and disturbances. The inverse dynamics of the vehicle is approximated using a feedback error learning mechanism that utilizes a dynamic Radial Basis Function neural network, referred to as the Extended Minimal Resource Allocating Network (EMRAN). EMRAN uses an extended Kalman filter approach for learning and a growing/pruning condition helps in keeping the number of hidden neurons minimum. The online learning algorithm helps in handling the uncertainties and dynamic variations and also the unknown disturbances on the road. The proposed control architecture employs two coupled conventional controllers aided by the EMRAN inverse dynamics controller. The control architecture has a conventional PID controller for cruise control and a Stanley controller for path-tracking. Performances of both the longitudinal and lateral controllers are compared with existing control methods and the results clearly indicate that the proposed control scheme handles the disturbances and parametric uncertainties better, and also provides better tracking performance in autonomous vehicles.

【6】 A Survey on Human-aware Robot Navigation 标题：人类感知机器人导航研究综述

作者：Ronja Möller,Antonino Furnari,Sebastiano Battiato,Aki Härmä,Giovanni Maria Farinella 机构：Department of Mathematics and Informatics, University of Catania, V. Andrea Doria, Catania , Italy, Philips Research, High Tech Campus , Eindhoven, Netherlands, Cognitive Robotics and Social Sensing Laboratory, ICAR-CNR, Palermo, Italy 备注：Robotics and Autonomous Systems, 2021 链接：https://arxiv.org/abs/2106.11650 摘要：智能系统越来越成为我们日常生活的一部分，已经无缝集成到很难想象没有智能系统的世界。另一方面，这些系统的物理表现形式，以具体化的代理或机器人的形式，迄今为止仅用于特定的应用，而且往往局限于功能角色（例如在工业、娱乐和军事领域）。考虑到目前机器人导航、人机交互和人类活动识别等研究领域的发展和创新，这种情况似乎很快就会改变。机器人越来越容易获得和使用，人们对机器人的接受程度也在不断提高。然而，设计一个社会兼容的机器人，可以作为一个同伴需要考虑到各个领域的研究。本文关注的是一个社会顺应机器人的导航方面，并提供了一个现有的解决方案，为相关领域的研究以及对未来可能的方向展望。 摘要：Intelligent systems are increasingly part of our everyday lives and have been integrated seamlessly to the point where it is difficult to imagine a world without them. Physical manifestations of those systems on the other hand, in the form of embodied agents or robots, have so far been used only for specific applications and are often limited to functional roles (e.g. in the industry, entertainment and military fields). Given the current growth and innovation in the research communities concerned with the topics of robot navigation, human-robot-interaction and human activity recognition, it seems like this might soon change. Robots are increasingly easy to obtain and use and the acceptance of them in general is growing. However, the design of a socially compliant robot that can function as a companion needs to take various areas of research into account. This paper is concerned with the navigation aspect of a socially-compliant robot and provides a survey of existing solutions for the relevant areas of research as well as an outlook on possible future directions.

【7】 Total Least Squares for Optimal Pose Estimation 标题：最优姿态估计的总体最小二乘算法

作者：Saeed Maleki,John Crassidis,Yang Cheng,Matthias Schmid 机构：University at Buffalo, State University of New York, Amherst, NY,-, Mississippi State University, Mississippi State, MS, Clemson University, Clemson, SC 链接：https://arxiv.org/abs/2106.11522 摘要：这项工作提供了一个理论框架的姿态估计问题使用全最小二乘矢量观测从地标特征。首先，针对从点云特征中提取观测向量的位姿估计问题，建立了优化框架。然后，推导了误差协方差表达式。在姿态误差的小角度近似下，通过导出的优化框架得到的姿态和位置解被证明达到了Cram′er-Rao下界所定义的界。通过一系列矢量观测扫描提供了用于模拟该问题的测量数据，并假设一个完全填充的观测噪声协方差矩阵作为代价函数中的权重，以覆盖传感器不确定性的最一般情况。这里，先前的推导被扩展用于姿态估计问题，以包括比先前涉及各向同性噪声假设的情况更一般的误差相关情况。在1万个样本的montecarlo框架下对该方法进行了仿真，验证了误差协方差分析的正确性。 摘要：This work provides a theoretical framework for the pose estimation problem using total least squares for vector observations from landmark features. First, the optimization framework is formulated for the pose estimation problem with observation vectors extracted from point cloud features. Then, error-covariance expressions are derived. The attitude and position solutions obtained via the derived optimization framework are proven to reach the bounds defined by the Cram\'er-Rao lower bound under the small angle approximation of attitude errors. The measurement data for the simulation of this problem is provided through a series of vector observation scans, and a fully populated observation noise-covariance matrix is assumed as the weight in the cost function to cover for the most general case of the sensor uncertainty. Here, previous derivations are expanded for the pose estimation problem to include more generic cases of correlations in the errors than previously cases involving an isotropic noise assumption. The proposed solution is simulated in a Monte-Carlo framework with 10,000 samples to validate the error-covariance analysis.

【8】 SA-LOAM: Semantic-aided LiDAR SLAM with Loop Closure 标题：SA-LAAM：带循环闭包的语义辅助LiDAR SLAM

作者：Lin Li,Xin Kong,Xiangrui Zhao,Wanlong Li,Feng Wen,Hongbo Zhang,Yong Liu 机构： Zhejiang University 链接：https://arxiv.org/abs/2106.11516 摘要：基于激光雷达的SLAM系统是公认的比其他系统更精确和稳定，但其环路闭合检测仍然是一个悬而未决的问题。随着点云三维语义分割技术的发展，可以方便、稳定地获取点云的语义信息，是实现高层次智能化的关键，有利于SLAM。在本文中，我们提出了一种新的基于LOAM的语义辅助lidarslam，称为SA-LOAM，它利用了里程计和环路闭合检测中的语义。具体来说，我们提出了一个语义辅助的ICP，包括语义匹配、下采样和平面约束，并在循环闭合检测模块中集成了基于语义图的位置识别方法。借助于语义，我们可以提高定位精度，有效地检测循环闭包，甚至在大规模场景中也可以构造一个全局一致的语义映射。在KITTI和Ford校园数据集上的大量实验表明，该系统显著提高了基线性能，对未知数据具有泛化能力，取得了与现有方法相比较有竞争力的结果。 摘要：LiDAR-based SLAM system is admittedly more accurate and stable than others, while its loop closure detection is still an open issue. With the development of 3D semantic segmentation for point cloud, semantic information can be obtained conveniently and steadily, essential for high-level intelligence and conductive to SLAM. In this paper, we present a novel semantic-aided LiDAR SLAM with loop closure based on LOAM, named SA-LOAM, which leverages semantics in odometry as well as loop closure detection. Specifically, we propose a semantic-assisted ICP, including semantically matching, downsampling and plane constraint, and integrates a semantic graph-based place recognition method in our loop closure detection module. Benefitting from semantics, we can improve the localization accuracy, detect loop closures effectively, and construct a global consistent semantic map even in large-scale scenes. Extensive experiments on KITTI and Ford Campus dataset show that our system significantly improves baseline performance, has generalization ability to unseen data and achieves competitive results compared with state-of-the-art methods.

【9】 SeqNetVLAD vs PointNetVLAD: Image Sequence vs 3D Point Clouds for Day-Night Place Recognition 标题：SeqNetVLAD与PointNetVLAD：图像序列与三维点云的昼夜位置识别

作者：Sourav Garg,Michael Milford 机构：QUT Centre for Robotics, Queensland University of Technology 备注：Accepted to CVPR 2021 Workshop on 3D Vision and Robotics (3DVR). this https URL 链接：https://arxiv.org/abs/2106.11481 摘要：位置识别是移动机器人定位和导航的关键技术。基于图像或视觉位置识别（VPR）是一个具有挑战性的问题，因为场景外观和相机视点在重新访问位置时会发生显著变化。最近基于“序列表示”的VPR方法与传统的序列分数聚合或基于单个图像的技术相比，显示出了良好的结果。与此同时，随着基于深度学习的点云处理技术的发展，基于三维点云的位置识别也在探索之中。然而，一个关键的问题仍然存在：一个显式的基于三维结构的位置表示总是优于一个隐式的基于RGB图像序列的“空间”表示，它可以内在地学习场景结构。在这个扩展的摘要中，我们试图通过考虑类似的“度量跨度”来表示位置，来比较这两种方法。我们将基于三维点云的方法（PointNetVLAD）与基于图像序列的方法（SeqNet等）进行了比较，并展示了基于图像序列的方法在给定度量范围内接近甚至超过基于点云的方法所达到的性能。这些性能变化可归因于输入传感器的数据丰富性以及移动机器人的数据积累策略的差异。虽然对于这两种不同的模式而言，完美的苹果对苹果的比较可能不可行，但所提出的比较朝着回答与空间表示有关的更深层次问题的方向迈出了一步，这些问题与自主驾驶和增强/虚拟现实等数个应用程序有关。公开的源代码https://github.com/oravus/seqNet. 摘要：Place Recognition is a crucial capability for mobile robot localization and navigation. Image-based or Visual Place Recognition (VPR) is a challenging problem as scene appearance and camera viewpoint can change significantly when places are revisited. Recent VPR methods based on ``sequential representations'' have shown promising results as compared to traditional sequence score aggregation or single image based techniques. In parallel to these endeavors, 3D point clouds based place recognition is also being explored following the advances in deep learning based point cloud processing. However, a key question remains: is an explicit 3D structure based place representation always superior to an implicit ``spatial'' representation based on sequence of RGB images which can inherently learn scene structure. In this extended abstract, we attempt to compare these two types of methods by considering a similar ``metric span'' to represent places. We compare a 3D point cloud based method (PointNetVLAD) with image sequence based methods (SeqNet and others) and showcase that image sequence based techniques approach, and can even surpass, the performance achieved by point cloud based methods for a given metric span. These performance variations can be attributed to differences in data richness of input sensors as well as data accumulation strategies for a mobile robot. While a perfect apple-to-apple comparison may not be feasible for these two different modalities, the presented comparison takes a step in the direction of answering deeper questions regarding spatial representations, relevant to several applications like Autonomous Driving and Augmented/Virtual Reality. Source code available publicly https://github.com/oravus/seqNet.

【10】 A Competitive Analysis of Online Multi-Agent Path Finding 标题：在线多智能体寻路的好胜分析

作者：Hang Ma 机构：Simon Fraser University 备注：Published at ICAPS 2021 链接：https://arxiv.org/abs/2106.11454 摘要：我们研究了在线多智能体路径发现（MAPF），其中新的智能体随着时间的推移不断被发现，所有的智能体都必须找到到给定目标位置的无冲突路径。我们将已有的（离线）MAPF的复杂性结果推广到在线MAPF。我们根据（1）可控性（每次规划路径的代理集合）和（2）合理性（规划路径的质量）将在线MAPF算法分为不同的类别，并研究它们之间的关系。我们对每一类在线MAPF算法的常用目标函数进行了竞争性分析。我们证明了一个朴素的算法，路由新发现的代理一次一个序列实现了一个竞争比，从下面和上面渐近有界的数量代理关于流动时间和makespan。然后我们给出了一个反直觉的结果，即如果不允许重新路由先前暴露的代理，任何合理的在线MAPF算法，包括那些为所有新暴露的代理规划最优路径的算法，都具有与naive算法相同的渐近竞争比，即使在2D的4邻域网格上也是如此。我们还导出了允许重路由的任何有理在线MAPF算法的竞争比的常数下界。研究结果首次为在线环境下使用MAPF算法的有效性提供了理论依据。 摘要：We study online Multi-Agent Path Finding (MAPF), where new agents are constantly revealed over time and all agents must find collision-free paths to their given goal locations. We generalize existing complexity results of (offline) MAPF to online MAPF. We classify online MAPF algorithms into different categories based on (1) controllability (the set of agents that they can plan paths for at each time) and (2) rationality (the quality of paths they plan) and study the relationships between them. We perform a competitive analysis for each category of online MAPF algorithms with respect to commonly-used objective functions. We show that a naive algorithm that routes newly-revealed agents one at a time in sequence achieves a competitive ratio that is asymptotically bounded from both below and above by the number of agents with respect to flowtime and makespan. We then show a counter-intuitive result that, if rerouting of previously-revealed agents is not allowed, any rational online MAPF algorithms, including ones that plan optimal paths for all newly-revealed agents, have the same asymptotic competitive ratio as the naive algorithm, even on 2D 4-neighbor grids. We also derive constant lower bounds on the competitive ratio of any rational online MAPF algorithms that allow rerouting. The results thus provide theoretical insights into the effectiveness of using MAPF algorithms in an online setting for the first time.

【11】 BEyond observation: an approach for ObjectNav 标题：超越观测：ObjectNav的一种方法

作者：Daniel V. Ruiz,Eduardo Todt 机构：Department of Informatics, Federal University of Paran´a, Brazil, Paran´a, Curitiba 备注：Presented at the 2th Embodied AI Workshop at CVPR 2021 链接：https://arxiv.org/abs/2106.11379 摘要：随着自动化的兴起，无人驾驶汽车无论是作为商用产品还是作为科研课题，都成为一个热门话题。它包含了机器人学的多学科领域，包括嵌入式系统、控制理论、路径规划、同步定位和映射（SLAM）、场景重建和模式识别。在这项工作中，我们探索性地研究了传感器数据融合和最新的机器学习算法如何执行被称为视觉语义导航的人工智能（E-AI）任务。这个任务，又称目标导航（ObjectNav），是指在不事先了解环境的情况下，通过以自我为中心的视觉观察来到达属于目标语义类的对象的自主导航。我们的方法在生境挑战2021 ObjectNav的Minival阶段和测试标准阶段均获得第四名。 摘要：With the rise of automation, unmanned vehicles became a hot topic both as commercial products and as a scientific research topic. It composes a multi-disciplinary field of robotics that encompasses embedded systems, control theory, path planning, Simultaneous Localization and Mapping (SLAM), scene reconstruction, and pattern recognition. In this work, we present our exploratory research of how sensor data fusion and state-of-the-art machine learning algorithms can perform the Embodied Artificial Intelligence (E-AI) task called Visual Semantic Navigation. This task, a.k.a Object-Goal Navigation (ObjectNav) consists of autonomous navigation using egocentric visual observations to reach an object belonging to the target semantic class without prior knowledge of the environment. Our method reached fourth place on the Habitat Challenge 2021 ObjectNav on the Minival phase and the Test-Standard Phase.

【12】 Distributed Heuristic Multi-Agent Path Finding with Communication 标题：带通信的分布式启发式多Agent路径搜索

作者：Ziyuan Ma,Yudong Luo,Hang Ma 机构： and [ 18] assumes that 1School of Computing Science, Simon Fraser University 备注：Published at ICRA 2021 链接：https://arxiv.org/abs/2106.11365 摘要：多智能体路径发现（MAPF）是大规模机器人系统的关键。最近的方法已经应用强化学习（RL）来学习部分可观测环境中的分散策略。获得无碰撞策略的一个基本挑战是，代理需要学习协作来处理拥塞情况。本文将通信与深度Q学习相结合，提出了一种基于学习的MAPF方法，其中agent通过图卷积实现协作。为了指导RL算法在面向目标的长时间任务中的应用，我们嵌入了来自单个源的最短路径的潜在选择作为启发式指导，而不是像现有的大多数工作那样使用特定的路径。我们的方法独立地处理每个agent，并从单个agent的角度训练模型。最后将训练好的策略应用于每个代理，以实现分散执行。整个系统在训练过程中是分布式的，在课程学习策略下进行训练。在多障碍环境下的实验结果表明，该方法平均步长小，成功率高。 摘要：Multi-Agent Path Finding (MAPF) is essential to large-scale robotic systems. Recent methods have applied reinforcement learning (RL) to learn decentralized polices in partially observable environments. A fundamental challenge of obtaining collision-free policy is that agents need to learn cooperation to handle congested situations. This paper combines communication with deep Q-learning to provide a novel learning based method for MAPF, where agents achieve cooperation via graph convolution. To guide RL algorithm on long-horizon goal-oriented tasks, we embed the potential choices of shortest paths from single source as heuristic guidance instead of using a specific path as in most existing works. Our method treats each agent independently and trains the model from a single agent's perspective. The final trained policy is applied to each agent for decentralized execution. The whole system is distributed during training and is trained under a curriculum learning strategy. Empirical evaluation in obstacle-rich environment indicates the high success rate with low average step of our method.

【13】 Be your own Benchmark: No-Reference Trajectory Metric on Registered Point Clouds 标题：成为您自己的基准：注册点云上的无参考轨迹度量

作者：Anastasiia Kornilova,Gonzalo Ferrer 机构： 1 — mapaggregated from LiDAR point clouds using GT poses muchThe authors are with Skolkovo Institute of Science and Technology 链接：https://arxiv.org/abs/2106.11351 摘要：本文讨论了在没有地面真位姿或其精度不足以完成特定任务的情况下（例如，室外场景中的小比例尺测绘）的轨迹质量评估问题。在我们的工作中，我们提出了一个无参考度量，即相互正交度量（MOM），它通过轨迹姿态从注册点云估计地图的质量。矩量法与全参考轨迹度量相对位姿误差密切相关，使其成为采用三维传感技术的装置上的轨迹基准工具。我们提供了这样的相关性的数学基础，并在合成环境中证实其统计。此外，由于我们的度量使用了相互正交曲面上的一个点子集，因此我们提供了一个提取该子集的算法，并在合成CARLA环境和KITTI数据集上评估了它的性能。 摘要：This paper addresses the problem of assessing trajectory quality in conditions when no ground truth poses are available or when their accuracy is not enough for the specific task - for example, small-scale mapping in outdoor scenes. In our work, we propose a no-reference metric, Mutually Orthogonal Metric (MOM), that estimates the quality of the map from registered point clouds via the trajectory poses. MOM strongly correlates with full-reference trajectory metric Relative Pose Error, making it a trajectory benchmarking tool on setups where 3D sensing technologies are employed. We provide a mathematical foundation for such correlation and confirm it statistically in synthetic environments. Furthermore, since our metric uses a subset of points from mutually orthogonal surfaces, we provide an algorithm for the extraction of such subset and evaluate its performance in synthetic CARLA environment and on KITTI dataset.

【14】 Three-dimensional bipedal model with zero-energy-cost walking 标题：具有零能量成本行走的三维两足动物模型

作者：Sergey Pankov 机构：Harik Shazeer Labs, Palo Alto, CA , ) 备注：None 链接：https://arxiv.org/abs/2106.11765 摘要：我们研究了一个三维关节刚体两足动物模型，该模型具有零成本的运输步行步态。由于模型各部分的协调振荡运动完全消除了脚-地碰撞，避免了能量损失。该模型由两个通过万向节连接的部件组成。它不依赖于任何几何改变机制，无质量零件或弹簧。尽管这个模型很简单，但它的无碰撞步态特征是有限速度行走，脚间隙和地面摩擦。无碰撞谱可以在小运动极限下解析地研究，揭示出无穷多的周期模态。在步行周期的不同阶段，这些模式在矢状面和冠状面振荡的数量上是不同的。我们重点讨论了这种振荡次数最少的振型，给出了它的完整解析解。然后我们用数值方法将其演化为一般的非小运动解。一般的无碰撞模式可以通过调整单个模型参数来调整。其中一些结果显示出惊人的普遍性和普遍性。 摘要：We study a three-dimensional articulated rigid-body biped model that possesses zero cost of transport walking gaits. Energy losses are avoided due to the complete elimination of the foot-ground collisions by the concerted oscillatory motion of the model's parts. The model consists of two parts connected via a universal joint. It does not rely on any geometry altering mechanisms, massless parts or springs. Despite the model's simplicity, its collisionless gaits feature walking with finite speed, foot clearance and ground friction. The collisionless spectrum can be studied analytically in the small movement limit, revealing infinitely many periodic modes. The modes differ in the number of sagittal and coronal plane oscillations at different stages of the walking cycle. We focus on the mode with the minimal number of such oscillations, presenting its complete analytical solution. We then numerically evolve it toward a general non-small movement solution. A general collisionless mode can be tuned by adjusting a single model parameter. Some of the presented results display a surprising degree of generality and universality.