机器人相关学术速递[6.30]

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cs.RO机器人相关，共计19篇

【1】 Learning Task Informed Abstraction 标题：学习任务知情摘要

作者：Xiang Fu,Ge Yang,Pulkit Agrawal,Tommi Jaakkola 备注：8 pages, 12 figures 链接：https://arxiv.org/abs/2106.15612 摘要：现有的基于模型的强化学习方法在复杂的视觉场景中进行操作时，由于无法确定任务相关特征的优先级，因而存在一定的困难。为了缓解这个问题，我们提出学习任务通知抽象（TIA），明确区分奖励相关的视觉特征和分心。对于TIA的学习，我们引入了任务通知MDP（TiMDP）的形式化方法，该方法通过训练两个通过合作重建学习视觉特征的模型来实现，但其中一个模型与奖赏信号是敌对分离的。实验结果表明，在许多视觉控制任务中，TIA比最先进的方法有显著的性能提高，而这些任务中自然和无约束的视觉分心是一个巨大的挑战。 摘要：Current model-based reinforcement learning methods struggle when operating from complex visual scenes due to their inability to prioritize task-relevant features. To mitigate this problem, we propose learning Task Informed Abstractions (TIA) that explicitly separates reward-correlated visual features from distractors. For learning TIA, we introduce the formalism of Task Informed MDP (TiMDP) that is realized by training two models that learn visual features via cooperative reconstruction, but one model is adversarially dissociated from the reward signal. Empirical evaluation shows that TIA leads to significant performance gains over state-of-the-art methods on many visual control tasks where natural and unconstrained visual distractions pose a formidable challenge.

【2】 Scalable and Elastic LiDAR Reconstruction in Complex Environments Through Spatial Analysis 标题：基于空间分析的复杂环境下可伸缩弹性激光雷达重建

作者：Yiduo Wang,Milad Ramezani,Matias Mattamala,Maurice Fallon 机构： Fallon is supported by a Royal Society UniversityResearch Fellowship, 1TheseauthorsarewiththeOxfordRoboticsInstitute, UniversityofOxford 备注：8 pages, 9 figures 链接：https://arxiv.org/abs/2106.15446 摘要：本文提出了一种在弹性密集三维重建系统中产生和融合子映射的新策略。该系统利用对扫描环境的空间理解，通过不同方式融合重叠的子映射来控制内存使用量的增长。这允许子映射的数量和内存消耗随着环境的大小而不是探索的持续时间而扩展。通过分析空间重叠，我们的系统分段不同的空间，如房间和楼梯井在探索飞行。此外，我们提出了一个新的数学公式的相对不确定性之间的立场，以提高全球一致性的重建。通过一个多层多室室内实验、一个大型室外实验和一个模拟数据集验证了该方法的性能。相对于我们的基线，所提出的方法展示了改进的可伸缩性和准确性。 摘要：This paper presents novel strategies for spawning and fusing submaps within an elastic dense 3D reconstruction system. The proposed system uses spatial understanding of the scanned environment to control memory usage growth by fusing overlapping submaps in different ways. This allows the number of submaps and memory consumption to scale with the size of the environment rather than the duration of exploration. By analysing spatial overlap, our system segments distinct spaces, such as rooms and stairwells on the fly during exploration. Additionally, we present a new mathematical formulation of relative uncertainty between poses to improve the global consistency of the reconstruction. Performance is demonstrated using a multi-floor multi-room indoor experiment, a large-scale outdoor experiment and a simulated dataset. Relative to our baseline, the presented approach demonstrates improved scalability and accuracy.

【3】 Autonomous Driving Implementation in an Experimental Environment 标题：自主驾驶在实验环境中的实现

作者：Namig Aliyev,Oguzhan Sezer,Mehmet Turan Guzel 机构：ID ,‡, Department of Computer Engineering, Sakarya University 备注：8 pages, 21 figures.This is a bachelor's thesis research report and was supported by the Scientific and Technological Research Council of Turkey 链接：https://arxiv.org/abs/2106.15274 摘要：自主系统需要识别环境，而要将其安全地付诸实践还有很长的路要走。在自动驾驶系统中，障碍物和红绿灯的检测与车道跟踪同样重要。在本研究中，我们开发了一套自主驾驶系统，并在设计的实验环境中进行了测试。在该系统中，采用带摄像头的模型车进行车道跟踪和避障实验，研究自主驾驶行为。训练卷积神经网络模型进行车道跟踪。针对车辆避障，分别建立了拐角检测、光流、焦点扩展、碰撞时间、平衡计算和决策机制。 摘要：Autonomous systems require identifying the environment and it has a long way to go before putting it safely into practice. In autonomous driving systems, the detection of obstacles and traffic lights are of importance as well as lane tracking. In this study, an autonomous driving system is developed and tested in the experimental environment designed for this purpose. In this system, a model vehicle having a camera is used to trace the lanes and avoid obstacles to experimentally study autonomous driving behavior. Convolutional Neural Network models were trained for Lane tracking. For the vehicle to avoid obstacles, corner detection, optical flow, focus of expansion, time to collision, balance calculation, and decision mechanism were created, respectively.

【4】 Learning Control Policies for Imitating Human Gaits 标题：模仿人体步态的学习控制策略

作者：Utkarsh A. Mishra 机构： IjspeertDEPARTMENT OF MECHANICAL AND INDUSTRIAL ENGINEERINGINDIAN INSTITUTE OF TECHNOLOGY ROORKEEROORKEE - 2 47667 (INDIA)APRIL 备注：47 pages, 17 figures, Bachelor of Technology Final Year Project Report 链接：https://arxiv.org/abs/2106.15273 摘要：本报告介绍了一个旨在学习模仿人类步态的框架。人类以最有效的方式表现出像行走、跑步和跳跃这样的动作，这是这个项目的动力来源。骨骼和肌肉骨骼人体模型被认为是在矢状面运动，并从两者的结果进行了详尽的比较。骨骼模型是由运动驱动的，而肌肉骨骼模型是通过肌腱驱动的。无模型强化学习算法用于优化逆动力学控制行为，以满足模仿参考运动的目标，以及最小化电机消耗的功率和肌肉消耗的代谢能量的次要目标。一方面，电机驱动模型的控制行为是通过比例微分控制器将目标关节角转化为关节力矩。另一方面，肌腱驱动模型的控制作用是将肌肉的激励隐式地转化为肌肉的激活，再转化为施加在关节上力矩的肌力。研究发现，肌腱驱动模型比运动驱动模型具有更大的优越性，因为它们由于肌肉激活动力学而具有固有的光滑性，并且不需要任何外部调节器。最后，讨论了一种在框架中获得重要决策变量最优配置的策略。所有的结果和分析都以说明性、定性和定量的方式呈现。附件中提供了支持视频链接。 摘要：The work presented in this report introduces a framework aimed towards learning to imitate human gaits. Humans exhibit movements like walking, running, and jumping in the most efficient manner, which served as the source of motivation for this project. Skeletal and Musculoskeletal human models were considered for motions in the sagittal plane, and results from both were compared exhaustively. While skeletal models are driven with motor actuation, musculoskeletal models perform through muscle-tendon actuation. Model-free reinforcement learning algorithms were used to optimize inverse dynamics control actions to satisfy the objective of imitating a reference motion along with secondary objectives of minimizing effort in terms of power spent by motors and metabolic energy consumed by the muscles. On the one hand, the control actions for the motor actuated model is the target joint angles converted into joint torques through a Proportional-Differential controller. While on the other hand, the control actions for the muscle-tendon actuated model is the muscle excitations converted implicitly to muscle activations and then to muscle forces which apply moments on joints. Muscle-tendon actuated models were found to have superiority over motor actuation as they are inherently smooth due to muscle activation dynamics and don't need any external regularizers. Finally, a strategy that was used to obtain an optimal configuration of the significant decision variables in the framework was discussed. All the results and analysis are presented in an illustrative, qualitative, and quantitative manner. Supporting video links are provided in the Appendix.

【5】 Predicting Depth from Semantic Segmentation using Game Engine Dataset 标题：基于游戏引擎数据集的语义分割深度预测

作者：Mohammad Amin Kashi 机构：Supervisor, Hamid D. Taghirad, Summer , arXiv:,.,v, [cs.CV] , Jun 备注：79 pages, Master's thesis at K. N. Toosi University of Technology, supervised by Professor Hamid D. Taghirad 链接：https://arxiv.org/abs/2106.15257 摘要：深度感知是机器人理解周围环境的基础。根据认知神经科学的观点，视觉深度知觉方法可分为三类，即双目视觉、主动视觉和图像视觉。前两类已经详细研究了几十年。然而，近年来随着深度学习方法的出现，对第三类知识的探索研究仍处于起步阶段，并取得了一定的发展势头。在认知神经科学中，图像深度知觉机制依赖于所见物体的知觉。受此启发，本文研究了卷积神经网络中物体感知与深度估计的关系。为此，我们开发了一种新的网络结构，它基于一个简单的深度估计网络，只使用一幅图像作为输入。我们提出的结构使用图像和图像的语义标签作为输入。我们使用语义标签作为对象感知的输出。与原网络的性能比较结果表明，新结构能使深度估计性能提高52%。大多数实验研究都是在游戏引擎生成的合成数据集上进行的，目的是将性能比较与非合成数据集不准确的深度和语义标签的影响隔离开来。结果表明，在没有合适的数据集的情况下，特定的合成数据集可用于深度网络的训练。此外，我们还发现，在这些情况下，语义标签的使用提高了网络对域从合成训练数据向非合成测试数据转移的鲁棒性。 摘要：Depth perception is fundamental for robots to understand the surrounding environment. As the view of cognitive neuroscience, visual depth perception methods are divided into three categories, namely binocular, active, and pictorial. The first two categories have been studied for decades in detail. However, research for the exploration of the third category is still in its infancy and has got momentum by the advent of deep learning methods in recent years. In cognitive neuroscience, it is known that pictorial depth perception mechanisms are dependent on the perception of seen objects. Inspired by this fact, in this thesis, we investigated the relation of perception of objects and depth estimation convolutional neural networks. For this purpose, we developed new network structures based on a simple depth estimation network that only used a single image at its input. Our proposed structures use both an image and a semantic label of the image as their input. We used semantic labels as the output of object perception. The obtained results of performance comparison between the developed network and original network showed that our novel structures can improve the performance of depth estimation by 52\% of relative error of distance in the examined cases. Most of the experimental studies were carried out on synthetic datasets that were generated by game engines to isolate the performance comparison from the effect of inaccurate depth and semantic labels of non-synthetic datasets. It is shown that particular synthetic datasets may be used for training of depth networks in cases that an appropriate dataset is not available. Furthermore, we showed that in these cases, usage of semantic labels improves the robustness of the network against domain shift from synthetic training data to non-synthetic test data.

【6】 Model Predictive Control for Trajectory Tracking on Differentiable Manifolds 标题：微分流形上轨迹跟踪的模型预测控制

作者：Guozheng Lu,Wei Xu,Fu Zhang 机构： Department of Mechanical Engineering, University of Hong Kong 链接：https://arxiv.org/abs/2106.15233 摘要：研究了机器人系统在流形上的几何跟踪控制理论与模型预测控制（MPC）实现之间的桥梁问题。我们提出了一个通用的流形上的MPC公式的基础上规范表示的系统在流形上发展。然后，我们提出了一种方法，通过线性化系统沿被跟踪轨迹来求解流形上的MPC公式。该方案有两个主要优点。首先，线性化后的系统可以得到一个由一组最小参数表示的等效误差系统，而不存在任何奇异性。其次，在系统建模、误差系统推导、线性化和控制的过程中，流形约束与系统描述完全解耦，从而可以开发一个自然封装流形约束的符号MPC框架。在这个框架中，用户只需要提供特定于系统的描述，而不需要处理多方面的约束。我们实现了这个框架，并在运行在$SO（3）\times\mathbb{R}^n$上的四旋翼无人机（UAV）和在曲面上运行的无人地面车辆（UGV）上进行了测试。实际实验表明，该框架和实现在高攻击性的特技飞行四旋翼机动中也能获得较高的跟踪性能和计算效率。 摘要：We consider the problem of bridging the gap between geometric tracking control theory and implementation of model predictive control (MPC) for robotic systems operating on manifolds. We propose a generic on-manifold MPC formulation based on a canonical representation of the system evolving on manifolds. Then, we present a method that solves the on-manifold MPC formulation by linearizing the system along the trajectory under tracking. There are two main advantages of the proposed scheme. The first is that the linearized system leads to an equivalent error system represented by a set of minimal parameters without any singularity. Secondly, the process of system modeling, error-system derivation, linearization and control has the manifold constraints completely decoupled from the system descriptions, enabling the development of a symbolic MPC framework that naturally encapsulates the manifold constraints. In this framework, users need only to supply system-specific descriptions without dealing with the manifold constraints. We implement this framework and test it on a quadrotor unmanned aerial vehicle (UAV) operating on $SO(3) \times \mathbb{R}^n$ and an unmanned ground vehicle (UGV) moving on a curved surface. Real-world experiments show that the proposed framework and implementation achieve high tracking performance and computational efficiency even in highly aggressive aerobatic quadrotor maneuvers.

【7】 On the Implementation of Behavior Trees in Robotics 标题：关于行为树在机器人学中的实现

作者：Michele Colledanchise,Lorenzo Natale 备注：None 链接：https://arxiv.org/abs/2106.15227 摘要：作为描述和实现机器人行为的工具，行为树（BTs）受到越来越多的关注。BTs是在视频游戏行业设计的，它们在机器人技术中的应用导致了特殊库的开发，以设计和执行适合复杂机器人技术软件体系结构的BTs。虽然人们对BTs的工作方式有着广泛的共识，但有些特性依赖于在所使用的特定软件库中所做的实现选择。在这封信中，我们概述了采用BTs的实践方面，以及机器人界为充分利用BTs在实际机器人中的优势而设计的解决方案。我们还概述了机器人技术中使用的开源库中提出的解决方案，展示了BTs如何适应机器人软件体系结构，并给出了一个用例示例。 摘要：There is a growing interest in Behavior Trees (BTs) as a tool to describe and implement robot behaviors. BTs were devised in the video game industry and their adoption in robotics resulted in the development of ad-hoc libraries to design and execute BTs that fit complex robotics software architectures. While there is broad consensus on how BTs work, some characteristics rely on the implementation choices done in the specific software library used. In this letter, we outline practical aspects in the adoption of BTs and the solutions devised by the robotics community to fully exploit the advantages of BTs in real robots. We also overview the solutions proposed in open-source libraries used in robotics, we show how BTs fit in robotic software architecture, and we present a use case example.

【8】 A Toolchain to Design, Execute, and Monitor Robots Behaviors 标题：设计、执行和监控机器人行为的工具链

作者：Michele Colledanchise,Giuseppe Cicala,Daniele E. Domenichelli,Lorenzo Natale,Armando Tacchella 机构：it 2Universita degli Studi di Genova 备注：None 链接：https://arxiv.org/abs/2106.15211 摘要：在本文中，我们提出了一个设计、执行和验证机器人行为的工具链。该工具链遵循欧盟H2020项目RobMoSys定义的准则，并将机器人审议编码为行为树（BT），一种有向树，其中内部节点模拟行为组合，叶节点模拟动作或测量操作。这种叶节点采用状态图（statechart，SC）的形式，它在不同的线程中运行，这些线程的状态执行基本的算术运算，并向robot发送命令。工具链提供了为给定系统规范定义运行时监视器的功能，该监视器在违反给定规范时向用户发出警告。我们在一个模拟实验中验证了这个工具链，使其在操作系统虚拟化环境中具有可复制性。 摘要：In this paper, we present a toolchain to design, execute, and verify robot behaviors. The toolchain follows the guidelines defined by the EU H2020 project RobMoSys and encodes the robot deliberation as a Behavior Tree (BT), a directed tree where the internal nodes model behavior composition and leaf nodes model action or measurement operations. Such leaf nodes take the form of a statechart (SC), which runs in separate threads, whose states perform basic arithmetic operations and send commands to the robot. The toolchain provides the ability to define a runtime monitor for a given system specification that warns the user whenever a given specification is violated. We validated the toolchain in a simulated experiment that we made reproducible in an OS-virtualization environment.

【9】 Towards Generalisable Deep Inertial Tracking via Geometry-Aware Learning 标题：基于几何意识学习的通用型深惯性跟踪

作者：Mohammed Alloulah,Maximilian Arnold,Anton Isopoussu 机构：†Bell Labs, ‡Invenia Labs 备注：Draft 链接：https://arxiv.org/abs/2106.15178 摘要：无仪器和无准备环境下的自主导航是下一代室内外位置服务的基本需求。为了实现这种雄心壮志，需要一套协作的传感模式，以便在不受挑战的动态条件影响的情况下保持性能。在现有的许多模式中，惯性跟踪由于其独立于周围环境，在暂时不利的操作条件下发挥着关键作用。然而，惯性跟踪传统上（i）遭受过多的误差增长和（ii）需要广泛和繁琐的调整。这两个问题限制了惯性跟踪的吸引力和实用性。本文提出了一种新的深度学习惯性跟踪系统DIT，它克服了以往的局限性；也就是说，通过（i）显著减少跟踪漂移和（ii）无缝地构建鲁棒和可推广的学习模型。DIT描述了两个核心贡献：（i）DIT采用了一个机械滑块子系统增强的机器人平台，该子系统可自动对不同传感器安装几何形状产生的惯性信号变量进行采样。我们利用该平台在内部策划了720万个样本数据集，覆盖21公里的总距离，分为11个索引传感器安装几何体(ii）DIT使用深度学习、最佳传输和域自适应（DA）来创建一个对传感器安装几何结构中的变化具有鲁棒性的模型。整个系统以端到端的机器人学习方式综合高性能和通用的惯性导航模型。在我们的评估中，DIT在性能上优于工业级传感器融合基线10倍（第90百分位），在训练时间上优于最先进的对抗性DA技术2.5倍（第90百分位）和10倍以上。 摘要：Autonomous navigation in uninstrumented and unprepared environments is a fundamental demand for next generation indoor and outdoor location-based services. To bring about such ambition, a suite of collaborative sensing modalities is required in order to sustain performance irrespective of challenging dynamic conditions. Of the many modalities on offer, inertial tracking plays a key role under momentary unfavourable operational conditions owing to its independence of the surrounding environment. However, inertial tracking has traditionally (i) suffered from excessive error growth and (ii) required extensive and cumbersome tuning. Both of these issues have limited the appeal and utility of inertial tracking. In this paper, we present DIT: a novel Deep learning Inertial Tracking system that overcomes prior limitations; namely, by (i) significantly reducing tracking drift and (ii) seamlessly constructing robust and generalisable learned models. DIT describes two core contributions: (i) DIT employs a robotic platform augmented with a mechanical slider subsystem that automatically samples inertial signal variabilities arising from different sensor mounting geometries. We use the platform to curate in-house a 7.2 million sample dataset covering an aggregate distance of 21 kilometres split into 11 indexed sensor mounting geometries. (ii) DIT uses deep learning, optimal transport, and domain adaptation (DA) to create a model which is robust to variabilities in sensor mounting geometry. The overall system synthesises high-performance and generalisable inertial navigation models in an end-to-end, robotic-learning fashion. In our evaluation, DIT outperforms an industrial-grade sensor fusion baseline by 10x (90th percentile) and a state-of-the-art adversarial DA technique by > 2.5x in performance (90th percentile) and >10x in training time.

【10】 O2O-Afford: Annotation-Free Large-Scale Object-Object Affordance Learning 标题：O2O-OFFER：无注释的大规模对象-对象抗声学习

作者：Kaichun Mo,Yuzhe Qin,Fanbo Xiang,Hao Su,Leonidas Guibas 机构：Stanford University, UCSD 链接：https://arxiv.org/abs/2106.15087 摘要：与计算机视觉和机器人学中大量的建模、感知和理解智能体-对象（如人、手、机器人-对象）交互的文献相反，很少有人研究对象-对象交互任务，它在机器人操作和规划任务中也起着重要的作用。在我们的日常生活中，有着丰富的对象-对象交互场景空间，如将对象放在凌乱的桌面上、将对象放在抽屉中、使用工具推送对象等。本文提出了一个统一的启示学习框架来学习各种任务的对象-对象交互。通过使用物理模拟（SAPIEN）和数千个具有丰富几何多样性的ShapeNet模型构建四个对象-对象交互任务环境，我们能够进行大规模的对象-对象启示学习，而不需要人工注释或演示。在技术贡献的核心部分，我们提出了一个对象核点卷积网络来推理两个对象之间的详细交互。在大规模合成数据和真实数据上的实验证明了该方法的有效性。有关代码、数据、视频和更多资料，请参阅项目网页：https://cs.stanford.edu/~kaichun/o2oafford 摘要：Contrary to the vast literature in modeling, perceiving, and understanding agent-object (e.g., human-object, hand-object, robot-object) interaction in computer vision and robotics, very few past works have studied the task of object-object interaction, which also plays an important role in robotic manipulation and planning tasks. There is a rich space of object-object interaction scenarios in our daily life, such as placing an object on a messy tabletop, fitting an object inside a drawer, pushing an object using a tool, etc. In this paper, we propose a unified affordance learning framework to learn object-object interaction for various tasks. By constructing four object-object interaction task environments using physical simulation (SAPIEN) and thousands of ShapeNet models with rich geometric diversity, we are able to conduct large-scale object-object affordance learning without the need for human annotations or demonstrations. At the core of technical contribution, we propose an object-kernel point convolution network to reason about detailed interaction between two objects. Experiments on large-scale synthetic data and real-world data prove the effectiveness of the proposed approach. Please refer to the project webpage for code, data, video, and more materials: https://cs.stanford.edu/~kaichun/o2oafford

【11】 EVPropNet: Detecting Drones By Finding Propellers For Mid-Air Landing And Following 标题：EVPropNet：通过寻找用于半空着陆和跟随的螺旋桨来探测无人机

作者：Nitin J. Sanket,Chahat Deep Singh,Chethan M. Parameshwara,Cornelia Fermüller,Guido C. H. E. de Croon,Yiannis Aloimonos 机构：a b 备注：11 pages, 10 figures, 6 tables. Accepted in Robotics: Science and Systems (RSS) 2021 链接：https://arxiv.org/abs/2106.15045 摘要：无人机或无人机的无障碍性迅速增加，对一般安全和保密构成威胁。大多数商用或定制的无人机都是多旋翼的，由多个螺旋桨组成。由于这些推进器以高速旋转，它们通常是图像中移动最快的部分，在没有严重运动模糊的情况下，经典相机无法直接“看到”。我们利用了一类传感器，这些传感器特别适合这种场景，称为事件摄像机，具有高时间分辨率、低延迟和高动态范围。在本文中，我们建立了螺旋桨的几何模型，并用它来生成模拟事件，这些事件被用来训练一个叫做EVPropNet的深层神经网络，从事件摄像机的数据中检测螺旋桨。EVPropNet直接转移到现实世界中，无需任何微调或再训练。我们介绍了我们的网络的两个应用：（a）跟踪和跟踪一个无标记的无人机和（b）降落在一个近悬停无人机。我们在许多不同螺旋桨形状和尺寸的真实实验中成功地评估和演示了所提出的方法。我们的网络能够以85.1%的速度检测螺旋桨，即使60%的螺旋桨被阻塞，并且可以在2W功率预算下以高达35Hz的频率运行。据我们所知，这是第一个基于深度学习的螺旋桨探测（无人机探测）解决方案。最后，我们的应用也显示了令人印象深刻的成功率为92%和90%的跟踪和着陆任务分别。 摘要：The rapid rise of accessibility of unmanned aerial vehicles or drones pose a threat to general security and confidentiality. Most of the commercially available or custom-built drones are multi-rotors and are comprised of multiple propellers. Since these propellers rotate at a high-speed, they are generally the fastest moving parts of an image and cannot be directly "seen" by a classical camera without severe motion blur. We utilize a class of sensors that are particularly suitable for such scenarios called event cameras, which have a high temporal resolution, low-latency, and high dynamic range. In this paper, we model the geometry of a propeller and use it to generate simulated events which are used to train a deep neural network called EVPropNet to detect propellers from the data of an event camera. EVPropNet directly transfers to the real world without any fine-tuning or retraining. We present two applications of our network: (a) tracking and following an unmarked drone and (b) landing on a near-hover drone. We successfully evaluate and demonstrate the proposed approach in many real-world experiments with different propeller shapes and sizes. Our network can detect propellers at a rate of 85.1% even when 60% of the propeller is occluded and can run at upto 35Hz on a 2W power budget. To our knowledge, this is the first deep learning-based solution for detecting propellers (to detect drones). Finally, our applications also show an impressive success rate of 92% and 90% for the tracking and landing tasks respectively.

【12】 Multi-Sensor Fusion based Robust Row Following for Compact Agricultural Robots 标题：基于多传感器融合的紧凑型农业机器人鲁棒行进跟踪

作者：Andres Eduardo Baquero Velasquez,Vitor Akihiro Hisano Higuti,Mateus Valverde Gasparino,Arun Narenthiran Sivakumar,Marcelo Becker,Girish Chowdhary 机构：Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL , (Corresponding Author), EarthSense Co., Champaign, IL , Department of Mechanical Engineering, University of Sao Paulo, Sao Carlos, SP, Brazil 链接：https://arxiv.org/abs/2106.15029 摘要：提出了一种基于激光雷达的棚下农业机器人自主导航系统。冠层下农业导航一直是一个具有挑战性的问题，因为全球导航卫星系统和其他定位传感器容易受到作物叶和茎引起的注意和多路径影响而产生重大误差。利用激光雷达测量探测作物行的反应式导航是GPS的一个更好的替代方法，但由于树冠下的树叶遮挡，它面临着挑战。我们的系统通过在低成本硬件上使用扩展的Kalman滤波框架融合IMU和LiDAR测量来解决这一挑战。此外，还引入了一个局部目标发生器，为车载控制器提供局部最优的参考轨迹。我们的系统在50.88公里的真实野外环境中，在不同地点的不同野外条件下，在多个机器人上进行了广泛的验证。我们报告了最先进的干预结果之间的距离，表明我们的系统能够在不干预的情况下安全地导航386.9米，平均在作物行没有明显间隙的田地，56.1米在生产领域和47.5米在有间隙的田地（行两边没有植物的间距为1米）。 摘要：This paper presents a state-of-the-art LiDAR based autonomous navigation system for under-canopy agricultural robots. Under-canopy agricultural navigation has been a challenging problem because GNSS and other positioning sensors are prone to significant errors due to attentuation and multi-path caused by crop leaves and stems. Reactive navigation by detecting crop rows using LiDAR measurements is a better alternative to GPS but suffers from challenges due to occlusion from leaves under the canopy. Our system addresses this challenge by fusing IMU and LiDAR measurements using an Extended Kalman Filter framework on low-cost hardwware. In addition, a local goal generator is introduced to provide locally optimal reference trajectories to the onboard controller. Our system is validated extensively in real-world field environments over a distance of 50.88~km on multiple robots in different field conditions across different locations. We report state-of-the-art distance between intervention results, showing that our system is able to safely navigate without interventions for 386.9~m on average in fields without significant gaps in the crop rows, 56.1~m in production fields and 47.5~m in fields with gaps (space of 1~m without plants in both sides of the row).

【13】 A Survey on Trust Metrics for Autonomous Robotic Systems 标题：自主机器人系统信任度量研究综述

作者：Vincenzo DiLuoffo,William R. Michalson 机构：Robotics Engineering, Worcester Polytechnic Institute (WPI), Institute Rd, Worcester, MA 链接：https://arxiv.org/abs/2106.15015 摘要：本文综述了与自主机器人系统安全性相关的信任度量领域。随着机器人工业从程序化的、面向任务的系统向人工智能的学习转变，这些自主系统变得容易受到多种安全风险的影响，因此对这些系统进行安全评估至关重要。因此，我们的重点是评估系统信任的整体方法，这需要将系统、硬件、软件、认知稳健性和供应商级信任度量纳入统一的信任模型。我们着手确定是否已经有信任度量定义了这样一个整体系统方法。虽然有大量的著作涉及机器人系统的各个方面，如风险管理、安全、安保保证等，但每个来源仅涵盖整个系统的子集，并没有一致地将相关成本纳入其指标中。本文试图将这一先前的工作放在透视图中，并展示如何将其扩展到开发用于评估复杂机器人（和其他）系统的有用的系统级信任度量。 摘要：This paper surveys the area of Trust Metrics related to security for autonomous robotic systems. As the robotics industry undergoes a transformation from programmed, task oriented, systems to Artificial Intelligence-enabled learning, these autonomous systems become vulnerable to several security risks, making a security assessment of these systems of critical importance. Therefore, our focus is on a holistic approach for assessing system trust which requires incorporating system, hardware, software, cognitive robustness, and supplier level trust metrics into a unified model of trust. We set out to determine if there were already trust metrics that defined such a holistic system approach. While there are extensive writings related to various aspects of robotic systems such as, risk management, safety, security assurance and so on, each source only covered subsets of an overall system and did not consistently incorporate the relevant costs in their metrics. This paper attempts to put this prior work into perspective, and to show how it might be extended to develop useful system-level trust metrics for evaluating complex robotic (and other) systems.

【14】 Multimodal Trajectory Prediction Conditioned on Lane-Graph Traversals 标题：基于车道图遍历条件的多模态轨迹预测

作者：Nachiket Deo,Eric M. Wolff,Oscar Beijbom 机构：University of California San Diego, Motional 链接：https://arxiv.org/abs/2106.15004 摘要：准确预测周围车辆的未来运动需要对目标和驾驶行为中固有的不确定性进行推理。这种不确定性可以松散地分解为横向（如保持车道、转弯）和纵向（如加速、制动）。我们提出了一种新的方法，结合学习离散策略卷展和集中解码器的子集的车道图。根据我们目前的观察，政策的推出探索了不同的目标，确保模型捕捉到横向变化。我们的新型潜变量模型解码器以车道图的不同子集为条件，捕捉到了车道图的纵向变化。我们的模型在nuScenes运动预测数据集上达到了最先进的性能，并且定性地证明了良好的场景遵从性。详细的烧蚀强调了策略展开和解码器架构的重要性。 摘要：Accurately predicting the future motion of surrounding vehicles requires reasoning about the inherent uncertainty in goals and driving behavior. This uncertainty can be loosely decoupled into lateral (e.g., keeping lane, turning) and longitudinal (e.g., accelerating, braking). We present a novel method that combines learned discrete policy rollouts with a focused decoder on subsets of the lane graph. The policy rollouts explore different goals given our current observations, ensuring that the model captures lateral variability. The longitudinal variability is captured by our novel latent variable model decoder that is conditioned on various subsets of the lane graph. Our model achieves state-of-the-art performance on the nuScenes motion prediction dataset, and qualitatively demonstrates excellent scene compliance. Detailed ablations highlight the importance of both the policy rollouts and the decoder architecture.

【15】 Multi-Task Learning for Scalable and Dense Multi-Layer Bayesian Map Inference 标题：可扩展稠密多层贝叶斯地图推理的多任务学习

作者：Lu Gan,Youngji Kim,Jessy W. Grizzle,Jeffrey M. Walls,Ayoung Kim,Ryan M. Eustice,Maani Ghaffari 机构： Kim was financially supported by the National ResearchFoundation of Korea (NRF) ( 20 19K 1A 3A 1A 1 20697 4 1) during her visit toUniversity of Michigan 链接：https://arxiv.org/abs/2106.14986 摘要：提出了一种新的、灵活的多任务多层贝叶斯映射框架，该框架具有易于扩展的属性层。所提出的框架超越了现代的度量语义映射，在利用现有的层间关联的同时，以单一的映射形式为机器人提供更丰富的环境信息。它消除了机器人在执行复杂任务时访问和处理许多独立地图上的信息的需要，并受益于地图层之间的关联，提高了机器人与环境的交互方式。为此，我们设计了一个以注意机制为前端的多任务深度神经网络，为多个地图图层同时提供多个观测值。我们的后端运行一个可伸缩的闭式贝叶斯推理，时间复杂度只有对数。我们应用该框架构建了一个包含度量语义占用层和可遍历层的稠密机器人地图。可遍历性地面真值标签是由外部感知数据以自我监督的方式自动生成的。我们提出了广泛的实验结果公开的数据集和数据收集的3D双足机器人平台在密歇根大学北校区，并显示可靠的映射性能在不同的环境中。最后，我们还讨论了如何使用高斯映射层来扩展现有的框架，以包含更多的信息，如摩擦、信号强度、温度和物理量浓度。用于再现呈现结果或在定制数据上运行的软件是公开的。 摘要：This paper presents a novel and flexible multi-task multi-layer Bayesian mapping framework with readily extendable attribute layers. The proposed framework goes beyond modern metric-semantic maps to provide even richer environmental information for robots in a single mapping formalism while exploiting existing inter-layer correlations. It removes the need for a robot to access and process information from many separate maps when performing a complex task and benefits from the correlation between map layers, advancing the way robots interact with their environments. To this end, we design a multi-task deep neural network with attention mechanisms as our front-end to provide multiple observations for multiple map layers simultaneously. Our back-end runs a scalable closed-form Bayesian inference with only logarithmic time complexity. We apply the framework to build a dense robotic map including metric-semantic occupancy and traversability layers. Traversability ground truth labels are automatically generated from exteroceptive sensory data in a self-supervised manner. We present extensive experimental results on publicly available data sets and data collected by a 3D bipedal robot platform on the University of Michigan North Campus and show reliable mapping performance in different environments. Finally, we also discuss how the current framework can be extended to incorporate more information such as friction, signal strength, temperature, and physical quantity concentration using Gaussian map layers. The software for reproducing the presented results or running on customized data is made publicly available.

【16】 Online Estimation and Coverage Control with Heterogeneous Sensing Information 标题：异质传感信息的在线估计与覆盖控制

作者：Andrew McDonald,Lai Wei,Vaibhav Srivastava 机构： McDonald is with the Department of Computer Science and Engineer-ing, Michigan State University, Srivastava are with the Department of Electrical andComputer Engineering 备注：6 pages, 2 figures, accepted to IEEE CCTA'21 链接：https://arxiv.org/abs/2106.14984 摘要：异构多机器人传感系统比同构系统能够更全面地描述物理过程。对感官数据的多种形式的访问允许这样的系统在互补的来源之间融合信息，并学习对感兴趣的现象的更丰富的表示。通常，这些数据是相关的，但保真度不同，即精度（偏差）和精度（噪声）。低保真数据可能更丰富，而高保真数据可能更可信。本文通过结合低保真度和高保真度数据来学习和覆盖感兴趣的感官函数，来解决多机器人在线估计和覆盖控制问题。针对这一异构学习和覆盖任务，我们提出了两种算法，即多保真学习和覆盖的随机排序算法（SMLC）和多保真学习和覆盖的确定性排序算法（DMLC），并证明了它们的渐近收敛性。此外，我们还通过数值模拟验证了SMLC和DMLC的经验有效性。 摘要：Heterogeneous multi-robot sensing systems are able to characterize physical processes more comprehensively than homogeneous systems. Access to multiple modalities of sensory data allow such systems to fuse information between complementary sources and learn richer representations of a phenomenon of interest. Often, these data are correlated but vary in fidelity, i.e., accuracy (bias) and precision (noise). Low-fidelity data may be more plentiful, while high-fidelity data may be more trustworthy. In this paper, we address the problem of multi-robot online estimation and coverage control by combining low- and high-fidelity data to learn and cover a sensory function of interest. We propose two algorithms for this task of heterogeneous learning and coverage -- namely Stochastic Sequencing of Multi-fidelity Learning and Coverage (SMLC) and Deterministic Sequencing of Multi-fidelity Learning and Coverage (DMLC) -- and prove that they converge asymptotically. In addition, we demonstrate the empirical efficacy of SMLC and DMLC through numerical simulations.

【17】 GIFT: Generalizable Interaction-aware Functional Tool Affordances without Labels 标题：GIFT：无标签的可泛化交互感知功能工具Affordance

作者：Dylan Turpin,Liquan Wang,Stavros Tsogkas,Sven Dickinson,Animesh Garg 机构：∗University of Toronto, †Vector Institute, ‡Samsung AI Center Toronto, §Nvidia, hooking, reaching, &, across three manipulation tasks:, by interacting with procedurally-generated tools, Discover tool afordances, from RGBD observations of unknown objects 备注：Qualitative results available at this https URL 链接：https://arxiv.org/abs/2106.14973 摘要：工具的使用需要对对象的启示和任务的需求之间的匹配进行推理。视觉启示学习可以受益于目标导向的交互体验，但目前的技术依赖于人类标签或专家演示来生成这些数据。在这篇文章中，我们描述了一种方法，在物理交互的基础上提供替代，从而消除了对人类标签或专家政策的需要。我们使用一种有效的基于抽样的方法来生成成功的轨迹，这些轨迹提供了接触数据，然后用来揭示启示表示。我们的框架GIFT分为两个阶段：首先，我们通过一组程序生成的工具从目标导向的交互中发现视觉启示；其次，我们训练一个模型，以自我监督的方式预测新工具上发现的启示的新实例。在我们的实验中，我们发现GIFT可以利用稀疏的关键点表示来预测抓取和交互点，以适应多个任务，例如钩住、够到和锤击。GIFT在所有任务上都优于基线，在使用新工具的三个任务中的两个任务上与人类甲骨文相匹配。 摘要：Tool use requires reasoning about the fit between an object's affordances and the demands of a task. Visual affordance learning can benefit from goal-directed interaction experience, but current techniques rely on human labels or expert demonstrations to generate this data. In this paper, we describe a method that grounds affordances in physical interactions instead, thus removing the need for human labels or expert policies. We use an efficient sampling-based method to generate successful trajectories that provide contact data, which are then used to reveal affordance representations. Our framework, GIFT, operates in two phases: first, we discover visual affordances from goal-directed interaction with a set of procedurally generated tools; second, we train a model to predict new instances of the discovered affordances on novel tools in a self-supervised fashion. In our experiments, we show that GIFT can leverage a sparse keypoint representation to predict grasp and interaction points to accommodate multiple tasks, such as hooking, reaching, and hammering. GIFT outperforms baselines on all tasks and matches a human oracle on two of three tasks using novel tools.

【18】 Design for a blind stereoscopic picture taker 标题：一种盲人立体照相机的设计

作者：Damian Moctezuma Enriquez,Eduardo Rodarte Leyva 链接：https://arxiv.org/abs/2106.14949 摘要：一种自动拍照机的示意图设计，用于从室内拍摄的照片中获取点云。在这种情况下，我们建议使用在嵌入式系统中编程的方程，该方程将跟踪房间内的点，然后打开两个相同类型的摄像头之间的空间，以便拍摄照片，稍后将用于创建数学模型的云点，后一个用户将应用于该照片。 摘要：An Schematical Design for an Autonomous Picture taker used for obtaining Point clouds from pictures taken inside a House. In this case we are proposing the use of an equation programmed inside an embedded system that will be tracking the points inside a room and then, open the space between two cameras of same type in order to take pictures that later will be used to create the cloud points for the mathematical model that the latter user will apply to that pictures.

【19】 Trust is not all about performance: trust biases in interaction with humans, robots and computers 标题：信任并不完全取决于表现：与人类、机器人和计算机互动时的信任偏差

作者：Joshua Zonca,Anna Folso,Alessandra Sciutti 机构：. Cognitive Architecture for Collaborative Technologies (CONTACT) Unit, Italian Institute of Technology, Genoa, Italy., . Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa 备注：arXiv admin note: text overlap with arXiv:2106.14832 链接：https://arxiv.org/abs/2106.14888 摘要：信任对于维持人类之间的合作至关重要。同样的原则也适用于与计算机和机器人的交互：如果我们不信任他们，我们就不会接受他们的帮助。大量证据表明，我们对其他代理人的信任取决于他们的表现。然而，在不确定的环境中，人类可能无法正确估计其他代理的性能，这可能导致对对等机和机器的不信任或过度信任。在目前的研究中，我们调查了在不确定的交互环境中，人类对同伴、计算机和机器人的信任是否受到先前信念的影响。参与者做出感知判断，观察人类参与者、计算机或社交机器人的模拟估计。参与者可以根据这些反馈修改自己的判断。结果表明，尽管参与者的判断是一致的，但他们对互动伙伴的本质的信念使他们对合作伙伴的判断产生了偏差。令人惊讶的是，社交机器人比计算机和人类伙伴更受信任。对所谓的人类伴侣的信任并不能完全通过其感知的表现来预测，这表明同伴互动中出现了规范的过程。我们的发现为理解对同伴和自主代理的信任机制提供了新的见解。 摘要：Trust is essential for sustaining cooperation among humans. The same principle applies during interaction with computers and robots: if we do not trust them, we will not accept help from them. Extensive evidence has shown that our trust in other agents depends on their performance. However, in uncertain environments, humans may not be able to estimate correctly other agents' performance, potentially leading to distrust or over-trust in peers and machines. In the current study, we investigate whether humans' trust towards peers, computers and robots is biased by prior beliefs in uncertain interactive settings. Participants made perceptual judgments and observed the simulated estimates of either a human participant, a computer or a social robot. Participants could modify their judgments based on this feedback. Results show that participants' belief about the nature of the interacting partner biased their compliance with the partners' judgments, although the partners' judgments were identical. Surprisingly, the social robot was trusted more than the computer and the human partner. Trust in the alleged human partner was not fully predicted by its perceived performance, suggesting the emergence of normative processes in peer interaction. Our findings offer novel insights in the understanding of the mechanisms underlying trust towards peers and autonomous agents.