机器人相关学术速递[9.6]

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cs.RO机器人相关，共计14篇

【1】 Continuous-Time Behavior Trees as Discontinuous Dynamical Systems 标题：作为不连续动力系统的连续时间行为树 链接：https://arxiv.org/abs/2109.01575

作者：Christopher Iliffe Sprague,Petter Ögren 机构： School of Electrical Engineering and ComputerScience, Royal Institute of Technology (KTH) 备注：To be submitted to the IEEE Control Systems Letters (L-CSS) 摘要：行为树表示一种层次化和模块化的方式，将几个低级控制策略组合成高级任务切换策略。混合动力系统也可以通过不同策略之间的任务切换来观察，因此，已经对行为树和混合动力系统进行了一些比较，但只是非正式地，并且仅在离散时间内。缺乏行为树的正式连续时间公式。此外，已经对特定类别的行为树设计进行了收敛性分析，但没有对一般设计进行收敛性分析。在这封信中，我们提供了行为树的第一个连续时间公式，表明它们可以被视为不连续动力系统（混合动力系统的一个子类），这使得存在性和唯一性结果能够应用于行为树，最后，提供充分条件，使此类系统收敛到一般设计所需的状态空间区域。有了这些结果，可以在设计行为树控制器时使用连续时间动力系统的大量结果。 摘要：Behavior trees represent a hierarchical and modular way of combining several low-level control policies into a high-level task-switching policy. Hybrid dynamical systems can also be seen in terms of task switching between different policies, and therefore several comparisons between behavior trees and hybrid dynamical systems have been made, but only informally, and only in discrete time. A formal continuous-time formulation of behavior trees has been lacking. Additionally, convergence analyses of specific classes of behavior tree designs have been made, but not for general designs. In this letter, we provide the first continuous-time formulation of behavior trees, show that they can be seen as discontinuous dynamical systems (a subclass of hybrid dynamical systems), which enables the application of existence and uniqueness results to behavior trees, and finally, provide sufficient conditions under which such systems will converge to a desired region of the state space for general designs. With these results, a large body of results on continuous-time dynamical systems can be brought to use when designing behavior tree controllers.

【2】 Deep Metric Learning for Ground Images 标题：地面图像的深度度量学习 链接：https://arxiv.org/abs/2109.01569

作者：Raaghav Radhakrishnan,Jan Fabian Schmid,Randolf Scholz,Lars Schmidt-Thieme 机构： Robert Bosch GmbH, Hildesheim, Germany, University of Hildesheim, Goethe University, Frankfurt am Main, Germany 摘要：基于地面纹理的定位方法是低成本、高精度机器人自定位解决方案的潜在前景。这些方法估计给定查询图像的姿势，即相对于一组在应用区域中姿势已知的参考图像，从下向相机对地面的当前观察。在这项工作中，我们处理初始定位任务，其中我们没有关于当前机器人定位的先验知识。在这种情况下，定位方法必须考虑所有可用的参考图像。然而，为了减少计算工作量和接收错误结果的风险，我们只考虑那些与查询图像实际上重叠的参考图像。为此，我们提出了一种深度度量学习方法，用于检索与查询图像最相似的参考图像。与现有的地面图像图像检索方法相比，我们的方法实现了显著更好的召回性能，并提高了基于最先进地面纹理的定位方法的定位性能。 摘要：Ground texture based localization methods are potential prospects for low-cost, high-accuracy self-localization solutions for robots. These methods estimate the pose of a given query image, i.e. the current observation of the ground from a downward-facing camera, in respect to a set of reference images whose poses are known in the application area. In this work, we deal with the initial localization task, in which we have no prior knowledge about the current robot positioning. In this situation, the localization method would have to consider all available reference images. However, in order to reduce computational effort and the risk of receiving a wrong result, we would like to consider only those reference images that are actually overlapping with the query image. For this purpose, we propose a deep metric learning approach that retrieves the most similar reference images to the query image. In contrast to existing approaches to image retrieval for ground images, our approach achieves significantly better recall performance and improves the localization performance of a state-of-the-art ground texture based localization method.

【3】 Model-Based Parameter Optimization for Ground Texture Based Localization Methods 标题：基于模型的地面纹理定位方法参数优化 链接：https://arxiv.org/abs/2109.01559

作者：Jan Fabian Schmid,Stephan F. Simon,Rudolf Mester 机构： Robert Bosch GmbH, Hildesheim, Germany, VSI Lab, CS Dept., Goethe University, Frankfurt am Main, Germany, Norwegian Open AI Lab, CS Dept., NTNU Trondheim, Norway 摘要：基于地面纹理的机器人定位是一种很有前途的精确定位方法。这是基于地面图像的视觉特征能够实现类似指纹的地点识别的观察。我们解决了这类方法的有效参数化问题，导出了定位性能的预测模型，该模型只需要一小部分应用领域的样本图像。在第一步中，我们检验模型是否能够预测改变基于特征的定位方法的一个最重要参数（提取的特征数量）的效果。我们研究了两种定位方法，在这两种情况下，我们的评估表明预测是足够准确的。由于该模型可用于为任何参数找到合适的值，因此我们提出了一个整体参数优化框架，该框架可找到合适的纹理特定参数配置，仅使用该模型评估所考虑的参数配置。 摘要：A promising approach to accurate positioning of robots is ground texture based localization. It is based on the observation that visual features of ground images enable fingerprint-like place recognition. We tackle the issue of efficient parametrization of such methods, deriving a prediction model for localization performance, which requires only a small collection of sample images of an application area. In a first step, we examine whether the model can predict the effects of changing one of the most important parameters of feature-based localization methods: the number of extracted features. We examine two localization methods, and in both cases our evaluation shows that the predictions are sufficiently accurate. Since this model can be used to find suitable values for any parameter, we then present a holistic parameter optimization framework, which finds suitable texture-specific parameter configurations, using only the model to evaluate the considered parameter configurations.

【4】 UnDeepLIO: Unsupervised Deep Lidar-Inertial Odometry 标题：UnDeepLIO：无监控深度激光雷达惯性里程计 链接：https://arxiv.org/abs/2109.01533

作者：Yiming Tu,Jin Xie 机构： PCA Lab, Key Lab of Intelligent Perception and Systems for High-Dimensional, Information of Ministry of Education, Nanjing University of Science and Technology, Jiangsu Key Lab of Image and Video Understanding for Social Security 备注：14 pages, accepted by ACPR2021 摘要：广泛的研究致力于基于深度学习的里程计。然而，很少有人在无监督的深激光雷达里程计方面做出努力。在本文中，我们设计了一个新的框架，用于带IMU的无监督激光雷达里程测量，这在其他深度方法中从未使用过。首先，使用一对暹罗LSTM从IMU的线加速度和角速度获取初始姿态。对于初始姿势，我们在当前帧上执行刚性变换，并将其与最后一帧对齐。然后，我们从变换后的点云及其法线中提取顶点和法线特征。接下来，提出了两个分支注意模块，分别从提取的顶点和法线特征估计剩余旋转和平移。最后，我们的模型输出初始姿势和剩余姿势之和作为最终姿势。对于无监督训练，我们引入了一种用于体素化点云的无监督损失函数。所提出的方法在KITTI里程估计基准上进行了评估，并与其他最先进的方法取得了相当的性能。 摘要：Extensive research efforts have been dedicated to deep learning based odometry. Nonetheless, few efforts are made on the unsupervised deep lidar odometry. In this paper, we design a novel framework for unsupervised lidar odometry with the IMU, which is never used in other deep methods. First, a pair of siamese LSTMs are used to obtain the initial pose from the linear acceleration and angular velocity of IMU. With the initial pose, we perform the rigid transform on the current frame and align it closer to the last frame. Then, we extract vertex and normal features from the transformed point clouds and its normals. Next a two-branches attention modules are proposed to estimate residual rotation and translation from the extracted vertex and normal features, respectively. Finally, our model outputs the sum of initial and residual poses as the final pose. For unsupervised training, we introduce an unsupervised loss function which is employed on the voxelized point clouds. The proposed approach is evaluated on the KITTI odometry estimation benchmark and achieves comparable performances against other state-of-the-art methods.

【5】 Real-Time Volumetric-Semantic Exploration and Mapping: An Uncertainty-Aware Approach 标题：实时体-语义探索与制图：一种不确定性感知的方法 链接：https://arxiv.org/abs/2109.01474

作者：Rui Pimentel de Figueiredo,Jonas le Fevre Sejersen,Jakob Grimm Hansen,Martim Brandão,Erdal Kayacan 机构： the Department ofElectrical and Computer Engineering, Aarhus University 备注：IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021 摘要：在这项工作中，我们提出了一个自治空中检查任务的整体框架，使用语义感知，但计算效率高的规划和映射算法。该系统利用最先进的后退地平线探测技术，利用最先进的深度卷积神经网络（DCNN）提供的几何和语义分割信息，进行下一个最佳视图（NBV）规划，目的是丰富环境表示。本文的贡献有三个方面，首先，我们提出了一个有效的传感器观测模型和一个奖励函数，该函数编码了从特定视角观测得到的预期信息收益。其次，我们扩展了奖励函数，不仅包含几何信息，还包含语义概率信息，这些信息由DCNN提供，用于实时操作的语义分割。在环境表示中加入语义信息允许偏向于特定对象的探索，而在规划过程中忽略与任务无关的对象。最后，我们将我们的方法应用于自主无人机船厂检查任务中。在现实场景中进行的一组模拟表明，与最先进的框架相比，该框架具有效率和有效性。 摘要：In this work we propose a holistic framework for autonomous aerial inspection tasks, using semantically-aware, yet, computationally efficient planning and mapping algorithms. The system leverages state-of-the-art receding horizon exploration techniques for next-best-view (NBV) planning with geometric and semantic segmentation information provided by state-of-the-art deep convolutional neural networks (DCNNs), with the goal of enriching environment representations. The contributions of this article are threefold, first we propose an efficient sensor observation model, and a reward function that encodes the expected information gains from the observations taken from specific view points. Second, we extend the reward function to incorporate not only geometric but also semantic probabilistic information, provided by a DCNN for semantic segmentation that operates in real-time. The incorporation of semantic information in the environment representation allows biasing exploration towards specific objects, while ignoring task-irrelevant ones during planning. Finally, we employ our approaches in an autonomous drone shipyard inspection task. A set of simulations in realistic scenarios demonstrate the efficacy and efficiency of the proposed framework when compared with the state-of-the-art.

【6】 A Multi-Sensor Interface to Improve the Teaching and Learning Experience in Arc Welding Training Tasks 标题：一种改善弧焊训练任务教与学体验的多传感器接口 链接：https://arxiv.org/abs/2109.01383

作者：Hoi-Yin Lee,Peng Zhou,Victor Wu,David Navarro-Alarcon 机构： in part bythe Jiangsu Industrial Technology Research Institute Collaborative ResearchProgram Scheme under grant ZG9V, and in part by the Hong Kong Polytechnic University undergrant 8B0 1, All authors are with The Hong Kong Polytechnic University (PolyU) 摘要：本文介绍了混合现实多传感器平台的开发，以提高弧焊任务的教与学体验。获得手眼协调技能的传统方法通常通过一对一的指导进行，学员/训练师必须戴上防护头盔，并使用金属工件进行多次动手测试。这种方法效率低下，因为电弧发出的有害光妨碍了对焊接过程的密切监控（从业人员只能观察到一个小亮点，大多数几何信息无法感知）。一些新的教学方法引入了虚拟现实的使用，作为一种安全模拟过程和可视化工件几何图形的方法。然而，这些模拟器的综合性质降低了平台的有效性。作为这些问题的可行解决方案，本研究提出了一种新的多传感器教学界面，该界面由HDR摄像机（用于实时监控焊接点）、深度传感器（用于捕捉场景的3D几何体）和VR耳机（用于安全地可视化过程）组成。与传统系统相比，我们的新平台为学员提供了接缝几何图形的虚拟线索、自动点跟踪和性能分数（有助于教员评估任务）。为了验证该方法的可行性，我们对几个焊接教学任务进行了详细的实验研究，并将其与当前实践和虚拟焊接解决方案进行了比较。 摘要：This paper presents the development of mixed reality multi-sensor platform to improve the teaching and learning experience of arc welding tasks. Traditional methods to acquire hand-eye coordination skills are typically conducted through one-to-one instruction where trainees/trainers must wear protective helmets and conduct several hands-on tests with metal workpieces. This approach is inefficient as the harmful light emitted from the electric arc impedes the close monitoring of the welding process (practitioners can only observe a small bright spot and most geometric information cannot be perceived). Some new teaching methods have introduced the use of virtual reality as a way to safely simulate the process and visualize the geometry of the workpieces. However, the synthetic nature of these simulators reduces the effectiveness of the platforms. As a feasible solution to these problems, this work presents a new multi-sensor teaching-learning interface composed of a HDR camera (to monitor the welding spot in real-time), a depth sensor (to capture the scene's 3D geometry), and a VR headset (to visualize the process safely). In contrast with traditional systems, our new platform provides trainees with virtual cues of the seam geometry, automatic spot tracking, and a performance score (useful for instructors to assess the task). To validate the method's feasibility, we conducted a detailed experimental study with several teaching and learning welding tasks, and compared its advantages with the current practice and virtual welding solutions.

【7】 A Comparative Study of Nonlinear MPC and Differential-Flatness-Based Control for Quadrotor Agile Flight 标题：四旋翼敏捷飞行非线性预测控制与基于微分平坦度控制的比较研究 链接：https://arxiv.org/abs/2109.01365

作者：Sihao Sun,Angel Romero,Philipp Foehn,Elia Kaufmann,Davide Scaramuzza 机构： University ofZurich 备注：14 pages, 13 figures 摘要：四旋翼的精确轨迹跟踪控制对于在杂乱环境中的安全导航至关重要。然而，由于非线性动力学、复杂的气动效应和驱动约束，这在敏捷飞行中具有挑战性。在本文中，我们通过跟踪速度高达72 km/h的各种灵活轨迹，对两种最先进的控制框架：非线性模型预测控制器（NMPC）和基于微分平坦度的控制器（DFBC）进行了经验比较。在仿真和真实环境中进行了比较，从跟踪精度、鲁棒性和计算效率方面系统地评估了这两种方法。我们展示了NMPC在动态跟踪不可行轨迹方面的优势，但代价是较高的计算时间和数值收敛问题的风险。对于这两种方法，我们还使用增量非线性动态反演（INDI）方法定量研究了添加内环控制器的效果，以及添加气动阻力模型的效果。我们在世界上最大的运动捕捉系统之一上进行的实际实验表明，NMPC和DFBC的跟踪误差降低了78%以上，这表明使用内环控制器和气动阻力模型进行敏捷轨迹跟踪的必要性。 摘要：Accurate trajectory tracking control for quadrotors is essential for safe navigation in cluttered environments. However, this is challenging in agile flights due to nonlinear dynamics, complex aerodynamic effects, and actuation constraints. In this article, we empirically compare two state-of-the-art control frameworks: the nonlinear-model-predictive controller (NMPC) and the differential-flatness-based controller (DFBC), by tracking a wide variety of agile trajectories at speeds up to 72 km/h. The comparisons are performed in both simulation and real-world environments to systematically evaluate both methods from the aspect of tracking accuracy, robustness, and computational efficiency. We show the superiority of NMPC in tracking dynamically infeasible trajectories, at the cost of higher computation time and risk of numerical convergence issues. For both methods, we also quantitatively study the effect of adding an inner-loop controller using the incremental nonlinear dynamic inversion (INDI) method, and the effect of adding an aerodynamic drag model. Our real-world experiments, performed in one of the world's largest motion capture systems, demonstrate more than 78% tracking error reduction of both NMPC and DFBC, indicating the necessity of using an inner-loop controller and aerodynamic drag model for agile trajectory tracking.

【8】 Theory of Mind Based Assistive Communication in Complex Human Robot Cooperation 标题：复杂人类机器人协作中基于心理理论的辅助交流 链接：https://arxiv.org/abs/2109.01355

作者：Moritz C. Buehler,Jürgen Adamy,Thomas H. Weisswange 机构：∗† 备注：16 pages, 6 figures 摘要：当与人类合作时，机器人不仅要关心它的环境和任务，还要理解伙伴的推理。为了在复杂任务中支持人类伙伴，机器人可以共享它知道的信息。然而，简单地交流一切都会让人烦恼和分心，因为他们可能已经意识到，而且并非所有信息都与当前情况相关。人类何时以及需要什么类型的信息的问题，是通过基于思维的交流理论的概念来解决的，该理论根据相关性评估和对人类信仰的估计来选择信息共享行动。我们将其集成到通信助手中，以支持协作环境中的人员并评估性能优势。我们设计了一个人类机器人寿司制作任务，该任务对人类具有挑战性，并产生不同的情况，其中人类没有意识到，交流可能是有益的。我们通过一项用户研究来评估以人为中心的沟通理念对绩效的影响。与没有信息交流的情况相比，辅助参与者可以更早地从无意识状态中恢复。这种方法比其他方法更尊重沟通成本，更好地平衡中断。通过提供适合于特定情况的信息，机器人不会指导人类，而是让人类做出正确的决策。 摘要：When cooperating with a human, a robot should not only care about its environment and task but also develop an understanding of the partner's reasoning. To support its human partner in complex tasks, the robot can share information that it knows. However simply communicating everything will annoy and distract humans since they might already be aware of and not all information is relevant in the current situation. The questions when and what type of information the human needs, are addressed through the concept of Theory of Mind based Communication which selects information sharing actions based on evaluation of relevance and an estimation of human beliefs. We integrate this into a communication assistant to support humans in a cooperative setting and evaluate performance benefits. We designed a human robot Sushi making task that is challenging for the human and generates different situations where humans are unaware and communication could be beneficial. We evaluate the influence of the human centric communication concept on performance with a user study. Compared to the condition without information exchange, assisted participants can recover from unawareness much earlier. The approach respects the costs of communication and balances interruptions better than other approaches. By providing information adapted to specific situations, the robot does not instruct but enable the human to make good decision.

【9】 On the similarities between Control Barrier Functions (CBFs) and Behavior Control Lyapunov Functions (BCLFs) 标题：论控制屏障函数与行为控制李亚普诺夫函数的相似性 链接：https://arxiv.org/abs/2109.01343

作者：Petter Ögren 备注：3 pages 摘要：控制屏障功能（CBF）是一种重要的工具，用于解决具有多个并发控制目标的情况，如安全和目标收敛。在本文中，我们研究了CBFs和所谓的行为控制Lyapunov函数（BCLFs）之间的相似性，这些函数被提出用于解决航空领域中的相同类型的问题。CBFs和BCLFs的关键结果是描述呈现给定集不变的控件集。我们比较了相应的定理，并证明了如果我们将CBFs中的一般类K函数限制为BCLFs的一般线性函数，并且将BCLFs中的目标数和优先级数限制为一，则在容许控制集方面的结果是等价的。此外，两篇论文都证明了不变集是渐近稳定的。 摘要：Control Barrier Functions (CBFs) is an important tool used to address situations with multiple concurrent control objectives, such as safety and goal convergence. In this paper we investigate the similarities between CBFs and so-called Behavior Control Lyapunov Functions (BCLFs) that have been proposed to address the same type of problems in the aeronautics domain. The key results of both CBFs and BCLFs is the description of the set of controls that render a given set invariant. We compare the corresponding theorems, and show that if we restrict the general class K function in CBFs to be the general linear function of BCLFs, and restrict the number of objectives as well as the number of priority levels to be just one in BCLFs, the results in terms of admissible control sets are equivalent. Furthermore, both papers show that the invariant set is made asymptotically stable.

【10】 Iterative Imitation Policy Improvement for Interactive Autonomous Driving 标题：交互式自主驾驶的迭代仿真策略改进 链接：https://arxiv.org/abs/2109.01288

作者：Zhao-Heng Yin,Chenran Li,Liting Sun,Masayoshi Tomizuka,Wei Zhan 机构： the learned policy will output improper and erroneouscontrol when the traffic observation does not come from the 1Zhao-Heng Yin is with the Department of Electronic and ComputerEngineering, The Hong Kong University of Science and Technology 摘要：我们提出了一个模拟学习系统的自动驾驶在城市交通与互动。我们训练了一种行为克隆（BC）策略来模拟从真实城市交通中收集的驾驶行为，并应用数据聚合算法来迭代提高其性能。在此设置中应用数据聚合面临两个挑战。第一个挑战是，在真实的城市交通中收集在线发布数据既昂贵又危险。在类似CARLA的模拟器中创建类似的流量场景，用于在线卷展收集也很困难。相反，我们建议从训练数据集创建一个弱模拟器，其中所有周围车辆都遵循数据集提供的数据轨迹。我们发现，在这样一个模拟器中收集的在线数据仍然可以用来提高BC策略的性能。第二个挑战是在线推出过程中繁琐且耗时的人类标签过程。为了解决这个问题，我们使用A$^*$planner作为伪专家来提供类似专家的演示。我们在真实的城市交通场景中验证了我们提出的模拟学习系统。实验结果表明，我们的系统能够显著提高基线BC策略的性能。 摘要：We propose an imitation learning system for autonomous driving in urban traffic with interactions. We train a Behavioral Cloning~(BC) policy to imitate driving behavior collected from the real urban traffic, and apply the data aggregation algorithm to improve its performance iteratively. Applying data aggregation in this setting comes with two challenges. The first challenge is that it is expensive and dangerous to collect online rollout data in the real urban traffic. Creating similar traffic scenarios in simulator like CARLA for online rollout collection can also be difficult. Instead, we propose to create a weak simulator from the training dataset, in which all the surrounding vehicles follow the data trajectory provided by the dataset. We find that the collected online data in such a simulator can still be used to improve BC policy's performance. The second challenge is the tedious and time-consuming process of human labelling process during online rollout. To solve this problem, we use an A$^*$ planner as a pseudo-expert to provide expert-like demonstration. We validate our proposed imitation learning system in the real urban traffic scenarios. The experimental results show that our system can significantly improve the performance of baseline BC policy.

【11】 Provably Safe Model-Based Meta Reinforcement Learning: An Abstraction-Based Approach 标题：可证明安全的基于模型的元强化学习：一种基于抽象的方法 链接：https://arxiv.org/abs/2109.01255

作者：Xiaowu Sun,Wael Fatnassi,Ulices Santa Cruz,Yasser Shoukry 机构：UniversityofCalifornia 摘要：传统的强化学习侧重于设计能够执行一项任务的代理，而元学习旨在解决设计代理的问题，这些代理可以概括为在设计或训练这些代理时未考虑的不同任务（例如，环境、障碍和目标）。本着这一精神，在本文中，我们考虑的问题，训练一个可证明安全的神经网络（NN）控制器的不确定非线性动力系统，可以推广到新的任务，不存在于训练数据，同时保持强大的安全保证。我们的方法是在训练阶段学习一组神经网络控制器。当任务在运行时可用时，我们的框架将仔细选择这些NN控制器的子集，并将它们组合成最终的NN控制器。我们的方法的关键是能够计算非线性动力系统的有限状态抽象。该抽象模型捕获了闭环系统在所有可能的神经网络权重下的行为，并用于训练神经网络，并在任务可用时组合它们。我们提供了理论上的保证来控制结果NN的正确性。我们评估了在训练数据中不存在的杂乱环境中控制轮式机器人的方法。 摘要：While conventional reinforcement learning focuses on designing agents that can perform one task, meta-learning aims, instead, to solve the problem of designing agents that can generalize to different tasks (e.g., environments, obstacles, and goals) that were not considered during the design or the training of these agents. In this spirit, in this paper, we consider the problem of training a provably safe Neural Network (NN) controller for uncertain nonlinear dynamical systems that can generalize to new tasks that were not present in the training data while preserving strong safety guarantees. Our approach is to learn a set of NN controllers during the training phase. When the task becomes available at runtime, our framework will carefully select a subset of these NN controllers and compose them to form the final NN controller. Critical to our approach is the ability to compute a finite-state abstraction of the nonlinear dynamical system. This abstract model captures the behavior of the closed-loop system under all possible NN weights, and is used to train the NNs and compose them when the task becomes available. We provide theoretical guarantees that govern the correctness of the resulting NN. We evaluated our approach on the problem of controlling a wheeled robot in cluttered environments that were not present in the training data.

【12】 Invariant Filtering for Bipedal Walking on Dynamic Rigid Surfaces with Orientation-based Measurement Model 标题：基于方位测量模型的动态刚体表面双足行走不变滤波 链接：https://arxiv.org/abs/2109.01241

作者：Yuan Gao,Yan Gu 机构：Department of Mechanical Engineering, University of Massachusetts Lowell, Lowell, USA 摘要：双足机器人行走的实际应用需要精确、实时的状态估计。动态刚性表面（DRS）上的运动状态估计（如电梯、船舶、公共交通车辆和飞机）仍处于探索阶段，尽管已对静止刚性表面的状态估计设计进行了广泛的研究。在状态估计中解决DRS运动是一个具有挑战性的问题，这主要是由于行走动力学的非线性、混合性、非平稳的表面-脚接触点以及硬件缺陷（例如，有限的可用性、噪声和车载传感器的漂移）。为了解决这一问题，我们引入了一种不变扩展卡尔曼滤波器（iNKF），其过程和测量模型明确地考虑了DRS运动和混合步行行为，同时分别满足组仿射条件和不变形式。由于这些吸引人的性质，该滤波器的估计误差收敛是可证明的混合DRS运动保证。该滤波器的测量模型还利用了与支撑脚和表面方向相关的完整约束，在该约束下，机器人在世界上的偏航角在存在一般DRS运动的情况下变得可见。在摇摆式跑步机上的双足步行实验结果表明，该滤波器保证了快速误差收敛和可观测的基础偏航角。 摘要：Real-world applications of bipedal robot walking require accurate, real-time state estimation. State estimation for locomotion over dynamic rigid surfaces (DRS), such as elevators, ships, public transport vehicles, and aircraft, remains under-explored, although state estimator designs for stationary rigid surfaces have been extensively studied. Addressing DRS locomotion in state estimation is a challenging problem mainly due to the nonlinear, hybrid nature of walking dynamics, the nonstationary surface-foot contact points, and hardware imperfections (e.g., limited availability, noise, and drift of onboard sensors). Towards solving this problem, we introduce an Invariant Extended Kalman Filter (InEKF) whose process and measurement models explicitly consider the DRS movement and hybrid walking behaviors while respectively satisfying the group-affine condition and invariant form. Due to these attractive properties, the estimation error convergence of the filter is provably guaranteed for hybrid DRS locomotion. The measurement model of the filter also exploits the holonomic constraint associated with the support-foot and surface orientations, under which the robot's yaw angle in the world becomes observable in the presence of general DRS movement. Experimental results of bipedal walking on a rocking treadmill demonstrate the proposed filter ensures the rapid error convergence and observable base yaw angle.

【13】 Mechanical Chameleons: Evaluating the effects of a social robot's non-verbal behavior on social influence 标题：机械变色龙：评估社交机器人的非语言行为对社会影响的影响 链接：https://arxiv.org/abs/2109.01206

作者：Patrik Jonell,Anna Deichler,Ilaria Torre,Iolanda Leite,Jonas Beskow 机构： 1KTH Royal Institute of TechnologyThis work was supported by the Swedish Foundation for StrategicResearch 备注：In proceedings of SCRITA 2021 (arXiv:2108.08092), a workshop at IEEE RO-MAN 2021: this https URL 摘要：在这篇论文中，我们提出了一项初步研究，调查非语言行为如何影响社会机器人的社会影响力。我们还提出了一个模块化系统，该系统能够根据对话者的面部姿势（头部动作和面部表情）实时控制非言语行为，并研究了面部姿势的三种不同策略（“静止”、“自然运动”，即从另一次对话中记录的动作，而“复制”，即模仿用户（延迟4秒）对“生存任务”中的社会影响和决策有任何影响。我们的初步结果表明，这三种情况之间没有显著差异，但这可能是由于参与研究的人数较少（12）。 摘要：In this paper we present a pilot study which investigates how non-verbal behavior affects social influence in social robots. We also present a modular system which is capable of controlling the non-verbal behavior based on the interlocutor's facial gestures (head movements and facial expressions) in real time, and a study investigating whether three different strategies for facial gestures ("still", "natural movement", i.e. movements recorded from another conversation, and "copy", i.e. mimicking the user with a four second delay) has any affect on social influence and decision making in a "survival task". Our preliminary results show there was no significant difference between the three conditions, but this might be due to among other things a low number of study participants (12).

【14】 Optimal Target Shape for LiDAR Pose Estimation 标题：激光雷达位姿估计的最优目标形状 链接：https://arxiv.org/abs/2109.01181

作者：Jiunn-Kai Huang,William Clark,Jessy W. Grizzle 机构： are with the RoboticsInstitute, University of Michigan 摘要：目标在诸如杂乱或无纹理环境中的目标跟踪、摄像机（和多传感器）校准任务以及同步定位和映射（SLAM）等问题中至关重要。这些任务的目标形状通常是对称的（正方形、矩形或圆形），适用于结构化、密集的传感器数据，如像素阵列（即图像）。然而，当使用稀疏传感器数据（如激光雷达点云）时，对称形状会导致姿态模糊，并且会受到激光雷达量化不确定性的影响。本文介绍了优化目标形状以消除激光雷达点云姿态模糊的概念。目标被设计为在相对于激光雷达旋转和平移的情况下在边缘点处诱导大梯度，以改善与点云稀疏性相关的量化不确定性。此外，给定目标形状，我们提出了一种方法，利用目标的几何体来估计目标的顶点，同时全局估计姿势。仿真和实验结果（通过运动捕捉系统验证）都证实，通过使用最佳形状和全局解算器，即使将部分照明的目标放置在30米之外，我们也可以实现厘米的平移误差和几度的旋转误差。所有的实现和数据集都可以在https://github.com/UMich-BipedLab/optimal_shape_global_pose_estimation. 摘要：Targets are essential in problems such as object tracking in cluttered or textureless environments, camera (and multi-sensor) calibration tasks, and simultaneous localization and mapping (SLAM). Target shapes for these tasks typically are symmetric (square, rectangular, or circular) and work well for structured, dense sensor data such as pixel arrays (i.e., image). However, symmetric shapes lead to pose ambiguity when using sparse sensor data such as LiDAR point clouds and suffer from the quantization uncertainty of the LiDAR. This paper introduces the concept of optimizing target shape to remove pose ambiguity for LiDAR point clouds. A target is designed to induce large gradients at edge points under rotation and translation relative to the LiDAR to ameliorate the quantization uncertainty associated with point cloud sparseness. Moreover, given a target shape, we present a means that leverages the target's geometry to estimate the target's vertices while globally estimating the pose. Both the simulation and the experimental results (verified by a motion capture system) confirm that by using the optimal shape and the global solver, we achieve centimeter error in translation and a few degrees in rotation even when a partially illuminated target is placed 30 meters away. All the implementations and datasets are available at https://github.com/UMich-BipedLab/optimal_shape_global_pose_estimation.

机器翻译，仅供参考