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cs.RO机器人相关，共计35篇
【1】 Binarized P-Network: Deep Reinforcement Learning of Robot Control from Raw Images on FPGA 标题：二值化P-网络：基于FPGA的机器人原始图像控制的深度强化学习 链接：https://arxiv.org/abs/2109.04966
作者：Yuki Kadokawa,Yoshihisa Tsurumine,Takamitsu Matsubara 机构：Graduate School of Science and Technology, Nara Institute of Science andTechnology 备注：8 pages, Accepted by Robotics and Automation Letters 摘要：本文探讨了一种深度强化学习（DRL）方法，用于设计在现场可编程门阵列（FPGA）上实现的边缘机器人基于图像的控制。虽然FPGA比CPU和GPU更节能，但典型的（DRL）方法无法应用，因为它们由许多逻辑块（LBs）组成，用于高速逻辑运算，但用于低速实数运算。为了解决这个问题，我们提出了一种称为二值化P网络（BPN）的DRL算法，该算法利用二值化卷积神经网络（BCNNs）学习图像输入控制策略。为了缓解由函数逼近精度低的BCNN引起的强化学习的不稳定性，我们的BPN采用了一种称为保守值迭代的鲁棒值更新方案，该方案能够容忍函数逼近误差。通过在仿真和实际机器人实验中的应用，验证了该算法的有效性。 摘要：This paper explores a Deep Reinforcement Learning (DRL) approach for designing image-based control for edge robots to be implemented on Field Programmable Gate Arrays (FPGAs). Although FPGAs are more power-efficient than CPUs and GPUs, a typical (DRL) method cannot be applied since they are composed of many Logic Blocks (LBs) for high-speed logical operations but low-speed real-number operations. To cope with this problem, we propose a novel DRL algorithm called Binarized P-Network (BPN), which learns image-input control policies using Binarized Convolutional Neural Networks (BCNNs). To alleviate the instability of reinforcement learning caused by a BCNN with low function approximation accuracy, our BPN adopts a robust value update scheme called Conservative Value Iteration, which is tolerant of function approximation errors. We confirmed the BPN's effectiveness through applications to a visual tracking task in simulation and real-robot experiments with FPGA.
【2】 Bodies Uncovered: Learning to Manipulate Real Blankets Around People via Physics Simulations 标题：揭开身体：通过物理模拟学习操纵人周围的真实毯子 链接：https://arxiv.org/abs/2109.04930
作者：Kavya Puthuveetil,Charles C. Kemp,Zackory Erickson 机构： Georgia Institute of Technology, Zackory Erickson is with the Robotics Institute 备注：8 pages, 8 figures, 3 tables 摘要：虽然机器人为老年人和行动不便的人提供了在床上提供身体帮助的机会，但人们经常在床上休息，毯子覆盖了大部分身体。要为许多日常自我护理任务提供帮助，如洗澡、穿衣或步行，护理者必须首先从身体的一部分揭开毯子。在这项工作中，我们介绍了一种机器人床上用品操作公式，其中机器人从目标身体部位揭开毯子，同时确保人体其余部分仍被覆盖。我们比较了强化学习和监督学习两种优化策略的方法，这两种方法为机器人提供了能够发现身体目标部位的抓取点和释放点。我们在物理模拟环境中对这些策略进行了训练和评估，该环境由覆盖仰卧在床上的模拟人的可变形布网格组成。此外，我们将模拟训练的策略转移到真实的移动机械手上，并证明它可以从躺在床上的人体模型的目标身体部位揭开毯子。源代码可以在线获得。 摘要：While robots present an opportunity to provide physical assistance to older adults and people with mobility impairments in bed, people frequently rest in bed with blankets that cover the majority of their body. To provide assistance for many daily self-care tasks, such as bathing, dressing, or ambulating, a caregiver must first uncover blankets from part of a person's body. In this work, we introduce a formulation for robotic bedding manipulation around people in which a robot uncovers a blanket from a target body part while ensuring the rest of the human body remains covered. We compare both reinforcement and supervised learning approaches for optimizing policies which provide a robot with grasp and release points that uncover a target part of the body. We trained and conducted evaluations of these policies in physics simulation environments that consist of a deformable cloth mesh covering a simulated human lying supine on a bed. In addition, we transfer simulation-trained policies to a real mobile manipulator and demonstrate that it can uncover a blanket from target body parts of a manikin lying in bed. Source code is available online.
【3】 Trajectory Optimization with Optimization-Based Dynamics 标题：基于最优化的动力学轨迹优化 链接：https://arxiv.org/abs/2109.04928
作者：Taylor A. Howell,Simon Le Cleac'h,Sumeet Singh,Pete Florence,Zachary Manchester,Vikas Sindhwani 摘要：我们提出了一个双层轨迹优化的框架，其中系统的动力学被编码为约束优化问题的解，并且这个下层问题的平滑梯度被传递给上层轨迹优化器。这种基于优化的动力学表示使约束处理、附加变量和非光滑力能够从上层优化器中抽象出来，并允许经典的无约束优化器合成更复杂系统的轨迹。我们提供了一种有效评估约束动力学的路径跟踪方法，并利用隐函数定理计算该表示的平滑梯度。我们通过对运动、航空航天和操纵领域的系统建模来演示该框架，这些领域包括：带关节限制的acrobot、受库仑摩擦的cart-pole、Raibert hopper、带推力限制的火箭着陆和基于优化动力学的平面推进任务，然后使用迭代LQR优化轨迹。 摘要：We present a framework for bi-level trajectory optimization in which a system's dynamics are encoded as the solution to a constrained optimization problem and smooth gradients of this lower-level problem are passed to an upper-level trajectory optimizer. This optimization-based dynamics representation enables constraint handling, additional variables, and non-smooth forces to be abstracted away from the upper-level optimizer, and allows classical unconstrained optimizers to synthesize trajectories for more complex systems. We provide a path-following method for efficient evaluation of constrained dynamics and utilize the implicit-function theorem to compute smooth gradients of this representation. We demonstrate the framework by modeling systems from locomotion, aerospace, and manipulation domains including: acrobot with joint limits, cart-pole subject to Coulomb friction, Raibert hopper, rocket landing with thrust limits, and planar-push task with optimization-based dynamics and then optimize trajectories using iterative LQR.
【4】 Learning to Swarm with Knowledge-Based Neural Ordinary Differential Equations 标题：用基于知识的神经常微分方程学习群体 链接：https://arxiv.org/abs/2109.04927
作者：Tom Z. Jiahao,Lishuo Pan,M. Ani Hsieh 备注：7 pages, 8 figures 摘要：从自然群中的集体行为理解单智能体动力学对于人工群和多智能体机器人系统中的机器人控制器设计至关重要。然而，agent与agent交互的复杂性和大多数集群的分散性对从全局行为中提取单机器人控制律提出了重大挑战。在这项工作中，我们考虑的重要任务是学习分散的单机器人控制器的基础上的状态观测的群体的轨迹。我们采用基于知识的神经常微分方程（KNODE）——一种能够将人工神经网络与已知agent动力学相结合的混合机器学习方法，提出了一个通用框架。我们的方法不同于大多数以前的工作，因为我们不需要用于学习的行动数据。我们将我们的框架分别应用于二维和三维两种不同的群集，并利用群集信息网络的图形结构演示有效的训练。我们进一步证明，所学习的单机器人控制器不仅可以在原始群体中再现群集行为，而且可以扩展到具有更多机器人的群体。 摘要：Understanding single-agent dynamics from collective behaviors in natural swarms is crucial for informing robot controller designs in artificial swarms and multiagent robotic systems. However, the complexity in agent-to-agent interactions and the decentralized nature of most swarms pose a significant challenge to the extraction of single-robot control laws from global behavior. In this work, we consider the important task of learning decentralized single-robot controllers based solely on the state observations of a swarm's trajectory. We present a general framework by adopting knowledge-based neural ordinary differential equations (KNODE) -- a hybrid machine learning method capable of combining artificial neural networks with known agent dynamics. Our approach distinguishes itself from most prior works in that we do not require action data for learning. We apply our framework to two different flocking swarms in 2D and 3D respectively, and demonstrate efficient training by leveraging the graphical structure of the swarms' information network. We further show that the learnt single-robot controllers can not only reproduce flocking behavior in the original swarm but also scale to swarms with more robots.
【5】 Estimation and Adaption of Indoor Ego Airflow Disturbance with Application to Quadrotor Trajectory Planning 标题：室内EGO气流扰动的估计和自适应及其在四旋翼轨迹规划中的应用 链接：https://arxiv.org/abs/2109.04918
作者：Luqi Wang,Boyu Zhou,Chuhao Liu,Shaojie Shen 机构： All authors are with theDepartment of Electronic and Computer Engineering, Hong Kong Universityof Science and Technology 备注：7 pages, 10 figures, accepted by ICRA2021 摘要：人们普遍认为，在四旋翼无人飞行器（UAV）的自主导航过程中，安全始终是最重要的。然而，据观察，在飞行过程中，ego气流干扰可能是一个重要的不利因素，导致潜在的安全问题，尤其是在狭窄和受限的室内环境中。因此，我们提出了一种新的方法来估计和适应四旋翼的室内气流干扰，并将其应用于轨迹规划。首先，针对邻近效应，对不同的四旋翼进行了悬停实验。然后根据所收集的加速度方差，根据所提出的公式对四转子的扰动进行建模。在不同的复杂环境下，对悬停条件下的扰动模型进行了验证。此外，根据估计出的干扰进行近似的Hamilton-Jacobi可达性分析，以便于安全轨迹规划，包括动力学路径搜索和B样条轨迹优化。整个规划框架在不同室内环境下的多个四旋翼平台上进行了验证。 摘要：It is ubiquitously accepted that during the autonomous navigation of the quadrotors, one of the most widely adopted unmanned aerial vehicles (UAVs), safety always has the highest priority. However, it is observed that the ego airflow disturbance can be a significant adverse factor during flights, causing potential safety issues, especially in narrow and confined indoor environments. Therefore, we propose a novel method to estimate and adapt indoor ego airflow disturbance of quadrotors, meanwhile applying it to trajectory planning. Firstly, the hover experiments for different quadrotors are conducted against the proximity effects. Then with the collected acceleration variance, the disturbances are modeled for the quadrotors according to the proposed formulation. The disturbance model is also verified under hover conditions in different reconstructed complex environments. Furthermore, the approximation of Hamilton-Jacobi reachability analysis is performed according to the estimated disturbances to facilitate the safe trajectory planning, which consists of kinodynamic path search as well as B-spline trajectory optimization. The whole planning framework is validated on multiple quadrotor platforms in different indoor environments.
【6】 Error State Extended Kalman Filter Multi-Sensor Fusion for Unmanned Aerial Vehicle Localization in GPS and Magnetometer Denied Indoor Environments 标题：误差状态扩展卡尔曼过滤多传感器融合无人机定位在室内环境不可观测的磁强计和全球定位系统中的应用 链接：https://arxiv.org/abs/2109.04908
作者：Lovro Markovic,Marin Kovac,Robert Milijas,Marko Car,Stjepan Bogdan 机构：University of Zagreb 摘要：本文讨论了无人机（UAV）室内导航的问题，特别是在GPS和磁强计传感器测量不可用或不可靠的地区。提出的解决方案是在多传感器融合中使用误差状态扩展卡尔曼滤波器（ES-EKF）。它的实现适合于融合来自多个传感器源的测量，并且状态模型被扩展以考虑传感器漂移和可能的校准误差。通过将从PixHawk 2.1飞行控制器获得的IMU数据与激光雷达制图仪SLAM提供的姿态测量值、Intel T265摄像机提供的视觉里程测量值以及Pozyx UWB室内定位系统提供的位置测量值进行融合，进行实验验证。根据Optitrack运动捕获系统的地面真实数据验证了ES-EKF估算的里程计，并演示了其在位置控制回路中用于稳定无人机。 摘要：This paper addresses the issues of unmanned aerial vehicle (UAV) indoor navigation, specifically in areas where GPS and magnetometer sensor measurements are unavailable or unreliable. The proposed solution is to use an error state extended Kalman filter (ES -EKF) in the context of multi-sensor fusion. Its implementation is adapted to fuse measurements from multiple sensor sources and the state model is extended to account for sensor drift and possible calibration inaccuracies. Experimental validation is performed by fusing IMU data obtained from the PixHawk 2.1 flight controller with pose measurements from LiDAR Cartographer SLAM, visual odometry provided by the Intel T265 camera and position measurements from the Pozyx UWB indoor positioning system. The estimated odometry from ES-EKF is validated against ground truth data from the Optitrack motion capture system and its use in a position control loop to stabilize the UAV is demonstrated.
【7】 GPA-Teleoperation: Gaze Enhanced Perception-aware Safe Assistive Aerial Teleoperation 标题：GPA-遥操作：凝视增强型感知安全辅助空中遥操作 链接：https://arxiv.org/abs/2109.04907
作者：Qianhao Wang,Botao He,Zhiren Xun,Chao Xu,Fei Gao 机构： 1 State Key Laboratory of Industrial Control Technology, Institute ofCyber-Systems and Control, 2 Huzhou Institute of Zhejiang University, 3 School of Automation 备注：8 pages, 13 figures, submitted to RA-L with ICRA presentation option 摘要：凝视是一种直观和直接的方式来表达个人的意图。然而，当涉及到旨在实现操作员意图的辅助空中遥操作时，很少有人关注凝视。现有的方法直接从遥控器（RC）输入获取意图，这是不准确、不稳定的，并且对非专业操作员不友好。此外，大多数遥操作工程不考虑环境感知，这是至关重要的，以确保安全。在本文中，我们提出了GPA遥操作，一个凝视增强感知辅助遥操作框架，它系统地解决了上述问题。我们利用凝视信息捕捉意图，并生成与之匹配的拓扑路径。然后，我们将路径细化为安全可行的轨迹，同时增强操作员对感兴趣环境的感知意识。此外，所提出的方法被集成到定制的四转子系统中。大量具有挑战性的室内外真实世界实验和基准比较验证了所提出的系统是可靠的、健壮的，甚至适用于非熟练用户。我们将发布系统的源代码，以利于相关研究。 摘要：Gaze is an intuitive and direct way to represent the intentions of an individual. However, when it comes to assistive aerial teleoperation which aims to perform operators' intention, rare attention has been paid to gaze. Existing methods obtain intention directly from the remote controller (RC) input, which is inaccurate, unstable, and unfriendly to non-professional operators. Further, most teleoperation works do not consider environment perception which is vital to guarantee safety. In this paper, we present GPA-Teleoperation, a gaze enhanced perception-aware assistive teleoperation framework, which addresses the above issues systematically. We capture the intention utilizing gaze information, and generate a topological path matching it. Then we refine the path into a safe and feasible trajectory which simultaneously enhances the perception awareness to the environment operators are interested in. Additionally, the proposed method is integrated into a customized quadrotor system. Extensive challenging indoor and outdoor real-world experiments and benchmark comparisons verify that the proposed system is reliable, robust and applicable to even unskilled users. We will release the source code of our system to benefit related researches.
【8】 SO-SLAM: Semantic Object SLAM with Scale Proportional and Symmetrical Texture Constraints 标题：SO-SLAM：具有比例、比例和对称纹理约束的语义对象SLAM 链接：https://arxiv.org/abs/2109.04884
作者：Ziwei Liao,Yutong Hu,Jiadong Zhang,Xianyu Qi,Xiaoyu Zhang,Wei Wang 备注：Submitted to RAL&ICRA 2022 摘要：对象SLAM将对象的概念引入到同步定位和映射（SLAM）中，并帮助理解移动机器人的室内场景和对象级交互应用程序。最先进的目标SLAM系统面临着诸如部分观测、遮挡、不可观测问题等挑战，限制了映射精度和鲁棒性。提出了一种新的单目语义对象SLAM（SO-SLAM）系统，解决了对象空间约束的引入问题。我们探讨了三种具有代表性的空间约束，包括比例约束、对称纹理约束和平面支撑约束。基于这些语义约束，我们提出了两种新的方法——更健壮的对象初始化方法和方向精细优化方法。我们已经在公共数据集和作者记录的移动机器人数据集上验证了算法的性能，并在映射效果上取得了显著的改进。我们将在此处发布代码：https://github.com/XunshanMan/SoSLAM. 摘要：Object SLAM introduces the concept of objects into Simultaneous Localization and Mapping (SLAM) and helps understand indoor scenes for mobile robots and object-level interactive applications. The state-of-art object SLAM systems face challenges such as partial observations, occlusions, unobservable problems, limiting the mapping accuracy and robustness. This paper proposes a novel monocular Semantic Object SLAM (SO-SLAM) system that addresses the introduction of object spatial constraints. We explore three representative spatial constraints, including scale proportional constraint, symmetrical texture constraint and plane supporting constraint. Based on these semantic constraints, we propose two new methods - a more robust object initialization method and an orientation fine optimization method. We have verified the performance of the algorithm on the public datasets and an author-recorded mobile robot dataset and achieved a significant improvement on mapping effects. We will release the code here: https://github.com/XunshanMan/SoSLAM.
【9】 Discretizing Dynamics for Maximum Likelihood Constraint Inference 标题：最大似然约束推理的离散化动力学 链接：https://arxiv.org/abs/2109.04874
作者：Kaylene C. Stocking,David L. McPherson,Robert P. Matthew,Claire J. Tomlin 机构：∗ Department of Electrical and Computer Engineering, University of California, Berkeley, † Department of Physical Therapy and Rehabilitation Science, University of California, San Francisco 备注：10 pages, 7 figures 摘要：最大似然约束推理是一种强大的技术，用于识别影响演示者在已知目标函数下行为的未建模约束。然而，它最初只针对离散状态-动作空间。连续动力学对于建模许多感兴趣的真实世界系统更为有用，包括人类和机器人的运动。我们提出了一种生成近似连续动力学的表格状态-动作空间的方法，并可用于服从真实系统动力学的演示的约束推理。然后，我们证明了具有2维和4维状态空间的非线性摆系统的精确约束推理，并表明性能对一系列超参数具有鲁棒性。就目标而言，演示不需要是完全最优的，即使演示只覆盖状态空间的一小部分，也可以确定最可能的约束。由于这些原因，所提出的方法可能特别适用于推断人类演示者的约束，这在人机交互和生物力学医学中有着重要的应用。 摘要：Maximum likelihood constraint inference is a powerful technique for identifying unmodeled constraints that affect the behavior of a demonstrator acting under a known objective function. However, it was originally formulated only for discrete state-action spaces. Continuous dynamics are more useful for modeling many real-world systems of interest, including the movements of humans and robots. We present a method to generate a tabular state-action space that approximates continuous dynamics and can be used for constraint inference on demonstrations that obey the true system dynamics. We then demonstrate accurate constraint inference on nonlinear pendulum systems with 2- and 4-dimensional state spaces, and show that performance is robust to a range of hyperparameters. The demonstrations are not required to be fully optimal with respect to the objective, and the most likely constraints can be identified even when demonstrations cover only a small portion of the state space. For these reasons, the proposed approach may be especially useful for inferring constraints on human demonstrators, which has important applications in human-robot interaction and biomechanical medicine.
【10】 PlaTe: Visually-Grounded Planning with Transformers in Procedural Tasks 标题：板块：程序任务中Transformer的视觉接地计划 链接：https://arxiv.org/abs/2109.04869
作者：Jiankai Sun,De-An Huang,Bo Lu,Yun-Hui Liu,Bolei Zhou,Animesh Garg 机构：Chinese University of Hong Kong, ‡University of Toronto &Vector Institute 摘要：在这项工作中，我们研究了如何利用教学视频来促进对人类决策过程的理解的问题，重点是训练一个能够从真实视频中规划目标导向程序的模型。直接从非结构化视频中学习结构化和可规划的状态和动作空间是我们任务的关键技术挑战。存在两个问题：第一，对于非结构化视频，训练和验证数据集之间的外观差距可能很大；其次，这些差距会导致决策错误，这些错误会在步骤中加剧。我们使用Planning Transformer（PlaTe）解决了这些限制，它的优点是避免了在基于模型的长期推广过程中单步模型出现的复合预测错误。我们的方法同时从人类演示中学习指定任务的潜在状态和动作信息以及决策过程的表示。在真实教学视频和交互环境中进行的实验表明，与以前的算法相比，我们的方法在达到指定目标方面可以取得更好的性能。我们还验证了在UR-5平台上应用程序性任务的可能性。 摘要：In this work, we study the problem of how to leverage instructional videos to facilitate the understanding of human decision-making processes, focusing on training a model with the ability to plan a goal-directed procedure from real-world videos. Learning structured and plannable state and action spaces directly from unstructured videos is the key technical challenge of our task. There are two problems: first, the appearance gap between the training and validation datasets could be large for unstructured videos; second, these gaps lead to decision errors that compound over the steps. We address these limitations with Planning Transformer (PlaTe), which has the advantage of circumventing the compounding prediction errors that occur with single-step models during long model-based rollouts. Our method simultaneously learns the latent state and action information of assigned tasks and the representations of the decision-making process from human demonstrations. Experiments conducted on real-world instructional videos and an interactive environment show that our method can achieve a better performance in reaching the indicated goal than previous algorithms. We also validated the possibility of applying procedural tasks on a UR-5 platform.
【11】 Heading Estimation Using Ultra-Wideband Received Signal Strength and Gaussian Processes 标题：基于超宽带接收信号强度和高斯过程的航向估计 链接：https://arxiv.org/abs/2109.04868
作者：Daniil Lisus,Charles Champagne Cossette,Mohammed Shalaby,James Richard Forbes 备注：6 pages, 9 figures, accepted to Robotics and Automation Letters, presented at IROS 2021 摘要：机器人必须能够确定其位置和方向，以便自主执行任务。航向估计在室内环境中尤其具有挑战性，因为在室内环境中，磁畸变使得基于磁强计的航向估计变得困难。超宽带（UWB）收发器在室内定位问题中很常见。这封信实验演示了如何使用超宽带范围和接收信号强度（RSS）测量来估计机器人的航向。超宽带天线的RSS随其方向而变化。因此，高斯过程（GP）用于学习从UWB范围和RSS输入到方向输出的数据驱动关系。结合固定扩展卡尔曼滤波器中的陀螺仪，实现了仅使用超宽带和陀螺仪测量值的航向估计方法。 摘要：It is essential that a robot has the ability to determine its position and orientation to execute tasks autonomously. Heading estimation is especially challenging in indoor environments where magnetic distortions make magnetometer-based heading estimation difficult. Ultra-wideband (UWB) transceivers are common in indoor localization problems. This letter experimentally demonstrates how to use UWB range and received signal strength (RSS) measurements to estimate robot heading. The RSS of a UWB antenna varies with its orientation. As such, a Gaussian process (GP) is used to learn a data-driven relationship from UWB range and RSS inputs to orientation outputs. Combined with a gyroscope in an invariant extended Kalman filter, this realizes a heading estimation method that uses only UWB and gyroscope measurements.
【12】 Human-Robot Interaction via a Joint-Initiative Supervised Autonomy (JISA) Framework 标题：基于联合主动监督自主(JISA)框架的人-机器人交互 链接：https://arxiv.org/abs/2109.04837
作者：Abbas Sidaoui,Naseem Daher,Daniel Asmar 机构： American University of Beirut 摘要：在本文中，我们提出并验证了一个用于人机交互（HRI）的联合主动监督自治（JISA）框架，在该框架中，机器人在执行任务时保持一定程度的自信心（SC），并且仅在SC下降时提示人类监督人寻求帮助。同时，在任务执行过程中，人工主管可以基于其态势感知（SA）干预正在执行的任务。为了评估JISA的适用性和实用性，将其应用于两个不同的HRI任务：基于网格的协作同步定位和映射（SLAM）和自动拼图重建。增强现实（AR）（用于SLAM）和二维图形用户界面（GUI）（用于拼图重建）是定制的，旨在增强人的SA，并允许人和代理之间的直观交互。实验证明了JISA框架的优越性。在SLAM中，JISA生产的高级地图不需要对任何SLAM库存地图进行后处理；此外，与传统方法相比，JISA将所需的映射时间缩短了约50%。在自动拼图重建中，JISA框架优于完全自主的解决方案，也优于由代理提示的按需人工干预产生的解决方案。 摘要：In this paper, we propose and validate a Joint-Initiative Supervised Autonomy (JISA) framework for Human-Robot Interaction (HRI), in which a robot maintains a measure of its self-confidence (SC) while performing a task, and only prompts the human supervisor for help when its SC drops. At the same time, during task execution, a human supervisor can intervene in the task being performed, based on his/her Situation Awareness (SA). To evaluate the applicability and utility of JISA, it is implemented on two different HRI tasks: grid-based collaborative simultaneous localization and mapping (SLAM) and automated jigsaw puzzle reconstruction. Augmented Reality (AR) (for SLAM) and two-dimensional graphical user interfaces (GUI) (for puzzle reconstruction) are custom-designed to enhance human SA and allow intuitive interaction between the human and the agent. The superiority of the JISA framework is demonstrated in experiments. In SLAM, the superior maps produced by JISA preclude the need for post processing of any SLAM stock maps; furthermore, JISA reduces the required mapping time by approximately 50 percent versus traditional approaches. In automated puzzle reconstruction, the JISA framework outperforms both fully autonomous solutions, as well as those resulting from on-demand human intervention prompted by the agent.
【13】 KNODE-MPC: A Knowledge-based Data-driven Predictive Control Framework for Aerial Robots 标题：Knode-MPC：一种基于知识的空中机器人数据驱动预测控制框架 链接：https://arxiv.org/abs/2109.04821
作者：Kong Yao Chee,Tom Z. Jiahao,M. Ani Hsieh 机构：UniversityofPennsylvania 备注：7 pages, 8 figures. *Equal Contribution. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible 摘要：在这项工作中，我们考虑的问题推导和结合模型预测控制（MPC）与四旋翼控制应用的精确动态模型。MPC依靠精确的动态模型来实现所需的闭环性能。然而，复杂系统及其运行环境中存在的不确定性对获得足够精确的系统动力学表示提出了挑战。在这项工作中，我们利用一个深入的学习工具，基于知识的神经常微分方程（KNODE），以扩大从第一原理获得的模型。由此产生的混合模型包括名义第一原理模型和从模拟或真实实验数据中学习的神经网络。使用四转子，我们将我们的混合模型与最先进的高斯过程（GP）模型进行对比，结果表明，混合模型提供了更精确的四转子动力学预测，并且能够推广到训练数据之外。为了提高闭环性能，混合模型被集成到一个新的MPC框架中，称为KNODE-MPC。结果表明，该集成框架在轨迹跟踪性能方面，仿真和物理实验分别提高了73%和14%以上。 摘要：In this work, we consider the problem of deriving and incorporating accurate dynamic models for model predictive control (MPC) with an application to quadrotor control. MPC relies on precise dynamic models to achieve the desired closed-loop performance. However, the presence of uncertainties in complex systems and the environments they operate in poses a challenge in obtaining sufficiently accurate representations of the system dynamics. In this work, we make use of a deep learning tool, knowledge-based neural ordinary differential equations (KNODE), to augment a model obtained from first principles. The resulting hybrid model encompasses both a nominal first-principle model and a neural network learnt from simulated or real-world experimental data. Using a quadrotor, we benchmark our hybrid model against a state-of-the-art Gaussian Process (GP) model and show that the hybrid model provides more accurate predictions of the quadrotor dynamics and is able to generalize beyond the training data. To improve closed-loop performance, the hybrid model is integrated into a novel MPC framework, known as KNODE-MPC. Results show that the integrated framework achieves 73% improvement in simulations and more than 14% in physical experiments, in terms of trajectory tracking performance.
【14】 Closing the Sim2Real Gap in Dynamic Cloth Manipulation 标题：在动态布料处理中缩小Sim2Real差距 链接：https://arxiv.org/abs/2109.04771
作者：Julius Hietala,David Blanco-Mulero,Gokhan Alcan,Ville Kyrki 机构： Department of ElectricalEngineering and Automation (EEA), Aalto University 备注：8 pages, 8 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible 摘要：布料操纵是一项具有挑战性的任务，因为材料的多个自由度和特性会影响布料的动力学。布料的非线性动力学在动态布料操作中具有特别重要的意义，因为布料的某些部分无法直接控制。在本文中，我们提出了一种新的解决动态布料操纵的方法，通过在仿真中使用强化学习（RL）来训练策略，并以零拍的方式将学习到的策略传递到真实世界。该方法在物理模拟器中使用视觉反馈和材料特性随机化来实现真实世界中的泛化。实验结果表明，仅使用视觉反馈就足以使策略以从模拟到真实世界的方式学习动态操作任务。此外，模拟中的动力学随机化可以捕获真实世界中各种布料的行为。 摘要：Cloth manipulation is a challenging task due to the many degrees of freedom and properties of the material affecting the dynamics of the cloth. The nonlinear dynamics of the cloth have particularly strong significance in dynamic cloth manipulation, where some parts of the cloth are not directly controllable. In this paper, we present a novel approach for solving dynamic cloth manipulation by training policies using reinforcement learning (RL) in simulation and transferring the learned policies to the real world in a zero-shot manner. The proposed method uses visual feedback and material property randomization in a physics simulator to achieve generalization in the real world. Experimental results show that using only visual feedback is enough for the policies to learn the dynamic manipulation task in a way that transfers from simulation to the real world. In addition, the randomization of the dynamics in simulation enables capturing the behavior of a variety of cloths in the real world.
【15】 On Inverse Inertia Matrix and Contact-Force Model for Robotic Manipulators at Normal Impacts 标题：机械手在法向碰撞时的逆惯量矩阵和接触力模型 链接：https://arxiv.org/abs/2109.04756
作者：Yuquan Wang,Niels Dehio,Abderrahmane Kheddar 机构：All three authors are with the CNRS-University of Montpellier 摘要：我们重新讨论了关节式机械臂接触其刚性环境时产生的切向接触速度为零的冲击。碰撞行为取决于固定底座和由机动关节连接的多个刚性连杆。我们深入分析的重点是推导合适的逆惯性矩阵和真实的接触力模型。我们使用7自由度熊猫机械手进行了真实的机器人实验，收集了150次不同关节配置和不同末端执行器速度的碰撞数据。我们的研究结果表明，假设关节被锁定，即转换接触点处的复合刚体惯性，并且测量一致的接触力模型是粘弹性的，则计算逆惯性矩阵。 摘要：We revisit the impact with zero tangential contact velocities caused by an articulated robot arm contacting its rigid environment. The impact behavior depends on the fixed base and multiple rigid links connected by motorized joints. Our thorough analysis focuses on deriving the suitable inverse inertia matrix and a realistic contact-force model. We conducted real-robot experiments with the 7 DOF Panda manipulator, collecting data of 150 impacts with varying joint configurations and different end-effector speeds. Our findings suggest computing the inverse inertia matrix assuming the joints are locked, i.e., transform the composite-rigid-body inertia at the contact point, and the measurement-consistent contact-force model is viscoelastic.
【16】 Line as a Visual Sentence: Context-aware Line Descriptor for Visual Localization 标题：作为视觉句子的线条：用于视觉本地化的上下文感知线条描述符 链接：https://arxiv.org/abs/2109.04753
作者：Sungho Yoon,Ayoung Kim 机构： Kim is with the Department of Mechanical Engineering 摘要：除了用于图像匹配的特征点外，线条特征还提供了额外的约束，用于解决机器人技术和计算机视觉（CV）中的视觉几何问题。尽管最近基于卷积神经网络（CNN）的线描述符在视点变化或动态环境中很有前景，但我们认为CNN结构在将可变线长度抽象为固定维描述符方面存在固有的缺点。在本文中，我们有效地介绍了处理可变线路的线路Transformer。受自然语言处理（NLP）任务的启发，在神经网络中句子可以很好地理解和抽象，我们将线段视为包含点（单词）的句子。通过动态处理aline上的可描述点，我们的描述符在可变线长度上表现出色。我们还提出了线签名网络共享线的几何属性的邻域。作为组描述符，网络通过理解线的相对几何结构来增强线描述符。最后，我们在点和线定位（PL Loc）中提出了建议的线描述符和匹配。我们表明，使用我们的线特征可以改进带有特征点的视觉定位。我们验证了所提出的单应估计和视觉定位方法。 摘要：Along with feature points for image matching, line features provide additional constraints to solve visual geometric problems in robotics and computer vision (CV). Although recent convolutional neural network (CNN)-based line descriptors are promising for viewpoint changes or dynamic environments, we claim that the CNN architecture has innate disadvantages to abstract variable line length into the fixed-dimensional descriptor. In this paper, we effectively introduce Line-Transformers dealing with variable lines. Inspired by natural language processing (NLP) tasks where sentences can be understood and abstracted well in neural nets, we view a line segment as a sentence that contains points (words). By attending to well-describable points on aline dynamically, our descriptor performs excellently on variable line length. We also propose line signature networks sharing the line's geometric attributes to neighborhoods. Performing as group descriptors, the networks enhance line descriptors by understanding lines' relative geometries. Finally, we present the proposed line descriptor and matching in a Point and Line Localization (PL-Loc). We show that the visual localization with feature points can be improved using our line features. We validate the proposed method for homography estimation and visual localization.
【17】 A Holistic Approach to Reactive Mobile Manipulation 标题：反应式移动操作的一种整体方法 链接：https://arxiv.org/abs/2109.04749
作者：Jesse Haviland,Niko Sünderhauf,Peter Corke 机构：andPeterCorkearewiththeQueenslandUniversityofTechnologyCentreforRobotics(QCR) 备注：See project website this https URL 摘要：我们提出了一个可任务的反应式移动操作系统的设计和实现。与相关工作相反，我们将手臂和底座的自由度视为一个整体结构，这大大提高了最终运动的速度和流动性。该方法的核心是一个鲁棒的反应式运动控制器，它可以实现理想的末端执行器姿势，同时避免关节位置和速度限制，并确保移动机械手在整个轨迹中可操纵。这可以支持基于传感器的行为，例如闭环视觉抓取。由于我们的方法不涉及任何计划，所以机器人永远不会静止不动地思考下一步该做什么。通过使用行为树实现拾取和放置系统，我们展示了整体运动控制器的多功能性，并在9自由度移动机械手上演示了该任务。此外，我们还为非完整和全方位移动机械手提供了运动控制器的开源实现。 摘要：We present the design and implementation of a taskable reactive mobile manipulation system. Contrary to related work, we treat the arm and base degrees of freedom as a holistic structure which greatly improves the speed and fluidity of the resulting motion. At the core of this approach is a robust and reactive motion controller which can achieve a desired end-effector pose while avoiding joint position and velocity limits, and ensuring the mobile manipulator is manoeuvrable throughout the trajectory. This can support sensor-based behaviours such as closed-loop visual grasping. As no planning is involved in our approach, the robot is never stationary thinking about what to do next. We show the versatility of our holistic motion controller by implementing a pick and place system using behaviour trees and demonstrate this task on a 9-degree-of-freedom mobile manipulator. Additionally, we provide an open-source implementation of our motion controller for both non-holonomic and omnidirectional mobile manipulators.
【18】 A Suitable Hierarchical Framework with Arbitrary Task Dimensions under Unilateral Constraints for physical Human Robot Interaction 标题：单面约束下适合任意任务维度的物理机器人交互分层框架 链接：https://arxiv.org/abs/2109.04743
作者：Juan David Munoz-Osorio,Felix Allmendinger 机构： Germany 2 Author is with the institute of mechatronic systems (IMES), LeibnizUniversity of Hannover 摘要：在过去的几年中，已经提出了几种层次结构框架来处理高度冗余的机器人系统。其中一些系统需要执行多个任务，并与环境进行物理交互。然而，没有一个框架能够管理具有任意任务维度的多个任务，同时尊重位置、速度、加速度和力水平的单边约束，同时能够直观地对外力作出反应。这项工作提出了一个解决这个问题的框架。该框架在仿真和真实机器人上进行了测试。在冗余度协同工业机器人（KUKA-LBR-iiwa）上的实验表明，与最先进的方法相比，该框架具有优势。该框架直观地对外力作出反应，并能够限制关节位置、速度、加速度和力。 摘要：In the last years, several hierarchical frameworks have been proposed to deal with highly-redundant robotic systems. Some of that systems are expected to perform multiple tasks and physically to interact with the environment. However, none of the proposed frameworks is able to manage multiple tasks with arbitrary task dimensions, while respecting unilateral constraints at position, velocity, acceleration and force level, and at the same time, to react intuitively to external forces. This work proposes a framework that addresses this problem. The framework is tested in simulation and on a real robot. The experiments on the redundant collaborative industrial robot (KUKA LBR iiwa) demonstrate the advantage of the framework compared to state-of-the-art approaches. The framework reacts intuitively to external forces and is able to limit joint positions, velocities, accelerations and forces.
【19】 Range, Endurance, and Optimal Speed Estimates for Multicopters 标题：多翼飞行器的射程、耐力和最佳速度估计 链接：https://arxiv.org/abs/2109.04741
作者：Leonard Bauersfeld,Davide Scaramuzza 机构： University of Zurich, and Department of Neuroinformatics, University of Zurich and ETH Zurich 备注：7 pages 1 page references 摘要：多翼机是最通用的移动机器人之一。它们的应用范围从检查和测绘任务到在灾区提供重要侦察，再到包裹运送。多旋翼飞行器在执行任务时能够达到的射程、续航时间和速度不仅是飞行器设计和任务规划的决定性因素，也是决策者决定空中机器人规则和法规的决定性因素。据作者所知，这项工作提出了第一种方法来估计各种多翼飞机的射程、续航时间和最佳飞行速度。通过将基于叶片单元动量理论的最先进的第一原理气动多翼模型与电动机模型和灰箱电池模型相结合，这一进展成为可能。该模型预测电池电压的相对误差仅为1.3%（43.1 mV），即使电池处于非恒定放电速率下。我们的方法通过在测试台上的真实实验以及在世界上最大的运动捕捉系统之一中以高达65 km/h的速度飞行进行了验证。我们还提出了一种精确的纸笔算法来估计多翼机的射程、续航时间和最佳速度，以帮助未来的研究人员建造具有最大射程和续航时间的无人机，确保未来的多旋翼飞行器更加通用。 摘要：Multicopters are among the most versatile mobile robots. Their applications range from inspection and mapping tasks to providing vital reconnaissance in disaster zones and to package delivery. The range, endurance, and speed a multirotor vehicle can achieve while performing its task is a decisive factor not only for vehicle design and mission planning, but also for policy makers deciding on the rules and regulations for aerial robots. To the best of the authors' knowledge, this work proposes the first approach to estimate the range, endurance, and optimal flight speed for a wide variety of multicopters. This advance is made possible by combining a state-of-the-art first-principles aerodynamic multicopter model based on blade-element-momentum theory with an electric-motor model and a graybox battery model. This model predicts the cell voltage with only 1.3% relative error (43.1 mV), even if the battery is subjected to non-constant discharge rates. Our approach is validated with real-world experiments on a test bench as well as with flights at speeds up to 65 km/h in one of the world's largest motion-capture systems. We also present an accurate pen-and-paper algorithm to estimate the range, endurance and optimal speed of multicopters to help future researchers build drones with maximal range and endurance, ensuring that future multirotor vehicles are even more versatile.
【20】 Where Should I Look? Optimised Gaze Control for Whole-Body Collision Avoidance in Dynamic Environments 标题：我应该去哪里看呢？动态环境下全身避碰的优化凝视控制 链接：https://arxiv.org/abs/2109.04721
作者：Mark Nicholas Finean,Wolfgang Merkt,Ioannis Havoutis 机构： such as pan and 1 Oxford Robotics Institute, University of OxfordThis research was supported by UKRIEPSRC through the Universityof Oxford Centre for Doctoral Training 备注：8 pages, 11 figures, submitted to IEEE Robotics and Automation Letters (RA-L) (with ICRA option) 摘要：随着机器人在日益复杂和动态的环境中工作，快速运动重新规划已成为一个广泛探索的研究领域。在实际部署中，我们通常缺乏随时全面观察环境的能力，这就带来了一个挑战，即在不断更新的运动计划下，如何最好地感知环境。我们首次研究了一种用于视线控制的“智能”控制器，目的是为动态和未知环境中的避障和运动规划提供有效的环境感知。我们详细介绍了确定移动机器人在受轨迹约束时的最佳头部摄像头行为的新问题。此外，我们提出了一种基于贪婪优化的解决方案，使用体素化奖励和运动原语的组合。我们证明了我们的方法在2D和3D环境中优于基准方法，无论是在探索当地环境的能力方面，此外，我们发现无碰撞轨迹的成功率更高——我们的方法可以提供7.4倍的地图探索，同时始终获得更高的无碰撞轨迹生成成功率。我们使用GPU加速感知框架验证了我们在物理丰田人类支持机器人（HSR）上的发现。 摘要：As robots operate in increasingly complex and dynamic environments, fast motion re-planning has become a widely explored area of research. In a real-world deployment, we often lack the ability to fully observe the environment at all times, giving rise to the challenge of determining how to best perceive the environment given a continuously updated motion plan. We provide the first investigation into a `smart' controller for gaze control with the objective of providing effective perception of the environment for obstacle avoidance and motion planning in dynamic and unknown environments. We detail the novel problem of determining the best head camera behaviour for mobile robots when constrained by a trajectory. Furthermore, we propose a greedy optimisation-based solution that uses a combination of voxelised rewards and motion primitives. We demonstrate that our method outperforms the benchmark methods in 2D and 3D environments, in respect of both the ability to explore the local surroundings, as well as in a superior success rate of finding collision-free trajectories -- our method is shown to provide 7.4x better map exploration while consistently achieving a higher success rate for generating collision-free trajectories. We verify our findings on a physical Toyota Human Support Robot (HSR) using a GPU-accelerated perception framework.
【21】 xBalloon: Animated Objects with Balloon Plastic Actuator 标题：外部引出序号：使用引出序号塑料执行器的动画对象 链接：https://arxiv.org/abs/2109.04717
作者：Haoran Xie,Takuma Torii,Aoshi Chiba,Qiukai Qi 机构：Japan Advanced Institute of Science and Technology, Ishikawa, Japan 备注：Proceedings of AH 2021.6 pages, 10 figures 摘要：形状改变界面对于用户改变普通物体的物理特性是很有希望的。然而，驱动装置的主流方法需要专业设备或非专业用户通常无法使用的材料。在这项工作中，我们重点研究带有充气结构的可控软致动器，因为它们是软的，因此对人机交互是安全的。我们提出了一种软致动器设计，称为Xballon，它是可行的，廉价的，易于制造。它由气球和塑料等日常材料组成，可以非常有效地实现弯曲驱动。为了表征，我们制备了具有不同几何参数的Xballon样品，并测试了它们的弯曲性能，找到了描述形状和弯曲宽度之间关系的分析模型。然后，我们使用Xballons为一系列常见对象设置动画，所有对象都可以令人满意地工作。我们进一步验证了用户对制作的体验，发现即使是那些事先没有机器人知识的人也可以轻松自信地制作Xballons。鉴于所有这些优势，我们相信Xballon是交互设计和娱乐应用的理想平台。 摘要：Shape-changing interfaces are promising for users to change the physical properties of common objects. However, prevailing approaches of actuation devices require either professional equipment or materials that are not commonly accessible to non-professional users. In this work, we focus on the controllable soft actuators with inflatable structures because they are soft thus safe for human computer interaction. We propose a soft actuator design, called xBalloon, that is workable, inexpensive and easy-to-fabricate. It consists of daily materials including balloons and plastics and can realize bending actuation very effectively. For characterization, we fabricated xBalloon samples with different geometrical parameters and tested them regarding the bending performance and found the analytical model describing the relationship between the shape and the bending width. We then used xBalloons to animate a series of common objects and all can work satisfactorily. We further verified the user experience about the the fabrication and found that even those with no prior robotic knowledge can fabricate xBalloons with ease and confidence. Given all these advantages, we believe that xBalloon is an ideal platform for interaction design and entertainment applications.
【22】 TIE: An Autonomous and Adaptive Terrestrial-Aerial Quadrotor 标题：TIE：一种自主自适应的陆空四旋翼 链接：https://arxiv.org/abs/2109.04706
作者：Ruibin Zhang,Yuze Wu,Lixian Zhang,Chao Xu,Fei Gao 机构： Zhejiang University, 3 School of Astronautics, Harbin Institute of Technology 备注：8pages, 12figures, submitted to RA-L and ICRA2022 摘要：这封信介绍了一个完全自主的机器人系统，它具有地面和空中的机动性。我们首先开发了一种携带足够传感和计算资源的轻型地面空中四旋翼。它结合了无人机的高机动性和无人机的长续航能力。然后提出了一种自适应导航框架，使其具有完全的自主性。在该框架中，提出了一种分层运动规划器，用于在未知环境中生成安全、低功耗的地面空中轨迹。此外，我们提出了一个统一的运动控制器，动态调整地面运动中的能量消耗。大量的真实世界实验和基准比较验证了所提出系统的鲁棒性和优异性能。在测试期间，它通过地面-空中综合机动安全穿越复杂环境，并在地面移动中实现7倍的节能。最后，我们将以开源软件包的形式发布我们的代码和硬件配置。 摘要：This letter presents a fully autonomous robot system that possesses both terrestrial and aerial mobility. We firstly develop a lightweight terrestrial-aerial quadrotor that carries sufficient sensing and computing resources. It incorporates both the high mobility of unmanned aerial vehicles and the long endurance of unmanned ground vehicles. An adaptive navigation framework is then proposed that brings complete autonomy to it. In this framework, a hierarchical motion planner is proposed to generate safe and low-power terrestrial-aerial trajectories in unknown environments. Moreover, we present a unified motion controller which dynamically adjusts energy consumption in terrestrial locomotion. Extensive realworld experiments and benchmark comparisons validate the robustness and outstanding performance of the proposed system. During the tests, it safely traverses complex environments with terrestrial aerial integrated mobility, and achieves 7 times energy savings in terrestrial locomotion. Finally, we will release our code and hardware configuration as an open-source package.
【23】 CLINS: Continuous-Time Trajectory Estimation for LiDAR-Inertial System 标题：CLINS：激光雷达-惯性系统的连续时间弹道估计 链接：https://arxiv.org/abs/2109.04687
作者：Jiajun Lv,Kewei Hu,Jinhong Xu,Yong Liu,Xiushui Ma,Xingxing Zuo 机构： Zhejiang University, cn) 2 NingboTech University 备注：IROS-2021 摘要：在本文中，我们提出了一个高精度的连续时间轨迹估计框架，专门用于SLAM（同步定位和映射）应用，它可以有效地融合高频和异步传感器数据。我们将提出的框架应用于三维激光雷达惯性系统进行评估。该方法采用非刚性配准方法进行连续时间轨迹估计，同时消除了激光雷达扫描中的运动失真。此外，我们还提出了一种两状态连续时间轨迹修正方法，以有效地解决环路闭合时难以计算的全局优化问题。我们在几个公开的数据集和我们收集的数据上检验了所提出方法的准确性。实验结果表明，该方法在精度方面优于离散时间方法，尤其是在攻击性运动发生时。此外，我们在url中打开了我们的代码{https://github.com/APRIL-ZJU/clins}使研究界受益。 摘要：In this paper, we propose a highly accurate continuous-time trajectory estimation framework dedicated to SLAM (Simultaneous Localization and Mapping) applications, which enables fuse high-frequency and asynchronous sensor data effectively. We apply the proposed framework in a 3D LiDAR-inertial system for evaluations. The proposed method adopts a non-rigid registration method for continuous-time trajectory estimation and simultaneously removing the motion distortion in LiDAR scans. Additionally, we propose a two-state continuous-time trajectory correction method to efficiently and efficiently tackle the computationally-intractable global optimization problem when loop closure happens. We examine the accuracy of the proposed approach on several publicly available datasets and the data we collected. The experimental results indicate that the proposed method outperforms the discrete-time methods regarding accuracy especially when aggressive motion occurs. Furthermore, we open source our code at url{https://github.com/APRIL-ZJU/clins} to benefit research community.
【24】 Jammkle: Fibre jamming 3D printed multi-material tendons and their application in a robotic ankle 标题：Jammkle：纤维干扰3D打印多材料肌腱及其在机器人踝关节中的应用 链接：https://arxiv.org/abs/2109.04681
作者：James Brett,Katrina Lo Surdo,Lauren Hanson,Joshua Pinskier,David Howard 备注：This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible 摘要：光纤干扰是一种相对较新且尚未研究的软机器人机制，以前在刚度可调的手臂和手指中使用时已获得成功。然而，到目前为止，研究人员还没有充分利用现代制造技术（包括多材料3D打印）在创建光纤干扰结构时提供的自由度。在这项研究中，我们提出了一种新型的、模块化的、多材料的、3D打印的、纤维干扰的肌腱单元，用于刚度可调的兼容机器人踝关节或Jammkle。我们描述了Jammkle的设计和制造，并强调了它与现代文献中的示例相比的优势。我们建立了肌腱单元的多物理模型，与实验数据吻合良好。最后，我们通过将多个肌腱单元集成到机器人脚踝中来演示实际应用，并执行广泛的测试和特征描述。我们表明，Jammkle在柔度、阻尼和防滑方面优于比较腿结构。 摘要：Fibre jamming is a relatively new and understudied soft robotic mechanism that has previously found success when used in stiffness-tuneable arms and fingers. However, to date researchers have not fully taken advantage of the freedom offered by contemporary fabrication techniques including multi-material 3D printing in the creation of fibre jamming structures. In this research, we present a novel, modular, multi-material, 3D printed, fibre jamming tendon unit for use in a stiffness-tuneable compliant robotic ankle, or Jammkle. We describe the design and fabrication of the Jammkle and highlight its advantages compared to examples from modern literature. We develop a multiphysics model of the tendon unit, showing good agreement with experimental data. Finally, we demonstrate a practical application by integrating multiple tendon units into a robotic ankle and perform extensive testing and characterisation. We show that the Jammkle outperforms comparative leg structures in terms of compliance, damping, and slip prevention.
【25】 Optimizing Space Utilization for More Effective Multi-Robot Path Planning 标题：优化空间利用，实现更有效的多机器人路径规划 链接：https://arxiv.org/abs/2109.04677
作者：Shuai D. Han,Jingjin Yu 机构：RutgersUniversity 备注：Submitting to ICRA 2022 摘要：我们对空间利用优化（SUO）原理进行了系统的探索，将其作为一种启发式方法，用于在解耦多机器人路径规划器中规划更好的单独路径，并将其应用于一次性和终身多机器人路径规划问题。我们证明了分散启发式集SU-I保持了单路径最优性，并显著减少了当规划多条路径时自然发生的拥塞。由于冲突数量显著减少，将SU-I集成到完整的规划器中，大大缩短了计算时间，并在具有中型和大型地图的各种评估场景中，为一次性和终身问题设置带来了可观的解决方案优化收益。 摘要：We perform a systematic exploration of the principle of Space Utilization Optimization (SUO) as a heuristic for planning better individual paths in a decoupled multi-robot path planner, with applications to both one-shot and life-long multi-robot path planning problems. We show that the decentralized heuristic set, SU-I, preserves single path optimality and significantly reduces congestion that naturally happens when many paths are planned without coordination. Integration of SU-I into complete planners brings dramatic reductions in computation time due to the significantly reduced number of conflicts and leads to sizable solution optimality gains in diverse evaluation scenarios with medium and large maps, for both one-shot and life-long problem settings.
【26】 Follow the Gradient: Crossing the Reality Gap using Differentiable Physics (RealityGrad) 标题：跟随梯度：使用微分物理跨越现实鸿沟(RealityGrad) 链接：https://arxiv.org/abs/2109.04674
作者：Jack Collins,Ross Brown,Jürgen Leitner,David Howard 机构：C acknowl-edges continued support from the Queensland University of Technology(QUT) through the QUT Centre for Robotics, Australia 2 Queensland University of Technology (QUT) 备注：8 Pages 摘要：我们提出了一种新的迭代方法来跨越现实鸿沟，该方法利用了实时机器人展开和可微物理。我们的方法RealityGrad首次展示了有效的sim2real传输与real2sim模型优化相结合，以缩小现实差距。由于自动微分库、计算库和非线性优化库的发展，微分物理学已经成为经典刚体模拟的诱人替代品。我们的方法建立在这一进展的基础上，并采用微分物理进行有效的轨迹优化。我们在一个串联机器人操作器的动态控制任务上演示了RealitGrad，并给出了结果，表明它的效率和能力不仅可以快速提高机器人在实际任务中的性能，而且还可以增强未来任务的仿真模型。RealityGrad的一次迭代在台式计算机上只需不到22分钟，同时将错误减少了2/3，与其他sim2real方法相比，它在计算和时间上都非常有效。我们的可微物理方法和应用为跨越现实鸿沟建立了一种有希望的方法，并具有扩展到复杂环境的巨大潜力。 摘要：We propose a novel iterative approach for crossing the reality gap that utilises live robot rollouts and differentiable physics. Our method, RealityGrad, demonstrates for the first time, an efficient sim2real transfer in combination with a real2sim model optimisation for closing the reality gap. Differentiable physics has become an alluring alternative to classical rigid-body simulation due to the current culmination of automatic differentiation libraries, compute and non-linear optimisation libraries. Our method builds on this progress and employs differentiable physics for efficient trajectory optimisation. We demonstrate RealitGrad on a dynamic control task for a serial link robot manipulator and present results that show its efficiency and ability to quickly improve not just the robot's performance in real world tasks but also enhance the simulation model for future tasks. One iteration of RealityGrad takes less than 22 minutes on a desktop computer while reducing the error by 2/3, making it efficient compared to other sim2real methods in both compute and time. Our methodology and application of differentiable physics establishes a promising approach for crossing the reality gap and has great potential for scaling to complex environments.
【27】 Risk-bounded Path Planning for Urban Air Mobility Operations under Uncertainty 标题：不确定条件下城市空中机动性运营的风险界路径规划 链接：https://arxiv.org/abs/2109.04606
作者：Pengcheng Wu,Junfei Xie,Yanchao Liu,Jun Chen 机构： Xie is with the Department of Electrical and Computer Engineering, SanDiego State University 备注：13pages, IEEE journal article 摘要：防撞是飞行器在动态和不确定城市环境中自主运行的一个基本问题。本文介绍了一种在这种环境下运行的无人机（UAV）的风险有界路径规划算法。该算法提出了带有机会约束的快速探索随机树（RRT），以生成对车辆和环境障碍物不确定性具有鲁棒性的概率保证无碰撞路径。假设所有不确定性服从高斯分布，通过将动态和概率约束转化为等效的静态和确定性约束来建立机会约束。通过将机会约束引入RRT算法，该算法不仅继承了基于采样的算法的计算优势，而且保证了在每个时间步都有一个概率可行的飞行区。仿真结果表明，该算法具有良好的性能。 摘要：Collision avoidance is an essential concern for the autonomous operations of aerial vehicles in dynamic and uncertain urban environments. This paper introduces a risk-bounded path planning algorithm for unmanned aerial vehicles (UAVs) operating in such environments. This algorithm advances the rapidly-exploring random tree (RRT) with chance constraints to generate probabilistically guaranteed collision-free paths that are robust to vehicle and environmental obstacle uncertainties. Assuming all uncertainties follow Gaussian distributions, the chance constraints are established through converting dynamic and probabilistic constraints into equivalent static and deterministic constraints. By incorporating chance constraints into the RRT algorithm, the proposed algorithm not only inherits the computational advantage of sampling-based algorithms but also guarantees a probabilistically feasible flying zone at every time step. Simulation results show the promising performance of the proposed algorithm.
【28】 A Unified Model with Inertia Shaping for Highly Dynamic Jumps of Legged Robots 标题：具有惯性成形的腿机器人高动态跳跃统一模型 链接：https://arxiv.org/abs/2109.04581
作者：Ke Wang,Guiyang Xin,Songyan Xin,Michael Mistry,Sethu Vijayakumar,Petar Kormushev 机构： University of Edinburgh 备注：8 pages 摘要：为了实现腿部机器人的高动态跳跃，必须控制机器人的旋转动力学。在本文中，我们的目标是通过提出一个规划高度动态跳跃的统一模型来提高跳跃性能，该模型可以近似模拟质心惯性。该模型将机器人抽象为一个单独的刚体，用于基础和腿部的点质量。该模型称为单刚体块腿模型（LL-SRBM），可用于规划两足和四足机器人的运动。通过考虑腿部动力学的影响，LL-SRBM为运动规划人员提供了一种计算效率高的方法来改变具有不同腿部结构的机器人的质心惯性。同时，我们提出了一种基于平均空间速度范数的接触检测方法。检测到接触后，控制器切换到强制控制以实现软着陆。在两足机器人滑块和四足机器人ANYmal上的扭转跳跃和向前跳跃实验表明，通过主动改变质心惯性，跳跃性能得到了改善。这些实验也表明了综合规划与控制框架的通用性和鲁棒性。 摘要：To achieve highly dynamic jumps of legged robots, it is essential to control the rotational dynamics of the robot. In this paper, we aim to improve the jumping performance by proposing a unified model for planning highly dynamic jumps that can approximately model the centroidal inertia. This model abstracts the robot as a single rigid body for the base and point masses for the legs. The model is called the Lump Leg Single Rigid Body Model (LL-SRBM) and can be used to plan motions for both bipedal and quadrupedal robots. By taking the effects of leg dynamics into account, LL-SRBM provides a computationally efficient way for the motion planner to change the centroidal inertia of the robot with various leg configurations. Concurrently, we propose a novel contact detection method by using the norm of the average spatial velocity. After the contact is detected, the controller is switched to force control to achieve a soft landing. Twisting jump and forward jump experiments on the bipedal robot SLIDER and quadrupedal robot ANYmal demonstrate the improved jump performance by actively changing the centroidal inertia. These experiments also show the generalization and the robustness of the integrated planning and control framework.
【29】 Object recognition for robotics from tactile time series data utilising different neural network architectures 标题：基于不同神经网络结构的机器人触觉时间序列目标识别 链接：https://arxiv.org/abs/2109.04573
作者：Wolfgang Bottcher,Pedro Machado,Nikesh Lama,T. M. McGinnity 机构：ID, Computational Neurosciences and Cognitive Robotics Group, School of Science and Technology, Nottingham Trent University, Nottingham, UK, T.M. McGinnity, Intelligent Systems Research Centre, Ulster University, Northern Ireland, UK 备注：None 摘要：机器人需要利用被抓取物体的高质量信息与物理环境进行交互。因此，触觉数据可用于补充视觉模态。本文研究了卷积神经网络（CNN）和长短时记忆（LSTM）神经网络结构在时空触觉抓取数据对象分类中的应用。此外，我们在相同的物理设置下，使用来自两个不同指尖传感器（即BioTac SP和WTS-FT）的数据对这些方法进行了比较，从而对相同触觉对象分类数据集的方法和传感器进行了现实的比较。此外，我们还提出了一种从记录的数据中创建更多训练示例的方法。结果表明，该方法将两种传感器类型的最大精度从82.4%（BioTac SP fingertips）和90.7%（WTS-FT fingertips）提高到94%。 摘要：Robots need to exploit high-quality information on grasped objects to interact with the physical environment. Haptic data can therefore be used for supplementing the visual modality. This paper investigates the use of Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) neural network architectures for object classification on Spatio-temporal tactile grasping data. Furthermore, we compared these methods using data from two different fingertip sensors (namely the BioTac SP and WTS-FT) in the same physical setup, allowing for a realistic comparison across methods and sensors for the same tactile object classification dataset. Additionally, we propose a way to create more training examples from the recorded data. The results show that the proposed method improves the maximum accuracy from 82.4% (BioTac SP fingertips) and 90.7% (WTS-FT fingertips) with complete time-series data to about 94% for both sensor types.
【30】 Risk-perception-aware control design under dynamic spatial risks 标题：动态空间风险下的风险感知控制设计 链接：https://arxiv.org/abs/2109.04570
作者：Aamodh Suresh,Sonia Martinez 机构： Mart´ınez are with the Department of Mechanical andAerospace Engineering, University of California at San Diego 备注：8 pages 摘要：这项工作提出了一种新的风险感知感知（RPA）控制设计，使用与不确定动态空间成本相关的风险的非理性感知。我们使用累积前景理论（CPT）对决策者的风险感知进行建模，并使用它构建感知风险函数，将不确定的动态空间成本转化为决策者的确定性感知风险。然后，这些风险被用于构建安全集，该安全集可以表示对风险不敏感感知的风险厌恶。我们基于安全集合定义了“包容性”和“多功能性”的概念，并将其与条件风险价值（CVaR）和预期风险（ER）等其他模型进行比较。我们从理论上证明，在风险感知感知控制的背景下，CPT是最“包容”和“通用”的批次模型。我们进一步利用感知风险函数和控制障碍函数（CBF）的思想构造了一类感知风险CBF。对于一类截断高斯代价，我们找到了这类CBF有效性的充分几何条件，从而保证了安全性。然后，我们使用二次规划（QP）生成感知安全关键控制，以根据给定的感知风险模型安全地指导代理。我们在2D环境中进行仿真，以说明所提出控制器的性能。 摘要：This work proposes a novel risk-perception-aware (RPA) control design using non-rational perception of risks associated with uncertain dynamic spatial costs. We use Cumulative Prospect Theory (CPT) to model the risk perception of a decision maker (DM) and use it to construct perceived risk functions that transform the uncertain dynamic spatial cost to deterministic perceived risks of a DM. These risks are then used to build safety sets which can represent risk-averse to risk-insensitive perception. We define a notions of "inclusiveness" and "versatility" based on safety sets and use it to compare with other models such as Conditional value at Risk (CVaR) and Expected risk (ER). We theoretically prove that CPT is the most "inclusive" and "versatile" model of the lot in the context of risk-perception-aware controls. We further use the perceived risk function along with ideas from control barrier functions (CBF) to construct a class of perceived risk CBFs. For a class of truncated-Gaussian costs, we find sufficient geometric conditions for the validity of this class of CBFs, thus guaranteeing safety. Then, we generate perceived-safety-critical controls using a Quadratic program (QP) to guide an agent safely according to a given perceived risk model. We present simulations in a 2D environment to illustrate the performance of the proposed controller.
【31】 Fine Manipulation and Dynamic Interaction in Haptic Teleoperation 标题：触觉遥操作中的精细操作和动态交互 链接：https://arxiv.org/abs/2109.04524
作者：Carlo Tiseo,Quentin Rouxel,Zhibin Li,Michael Mistry 机构：SchoolofInformat-ics, UniversityofEdinburgh 摘要：机器人的远程操作可以远程干预远距离和危险的任务，而不会对操作员造成伤害。然而，由于通信延迟和带宽的限制，远程操作面临着根本性的挑战。该工作通过在触觉遥操作管道中集成最新的操作结构，改进了基于分形阻抗控制器（FIC）的遥操作结构的性能。更新后的控制器利用了操纵中的反向运动学优化，因此在不放弃FIC控制器鲁棒性的情况下改善了精细操纵过程中的动态交互。此外，所提出的方法允许在操纵控制器和遥操作行为之间进行在线权衡，允许这两种行为的安全叠加。经验证的实验结果表明，该方法对减少通信带宽和延迟具有鲁棒性。此外，我们还证明了远程遥控机器人保持稳定和安全，即使与主端的通信突然中断。 摘要：Teleoperation of robots enables remote intervention in distant and dangerous tasks without putting the operator in harm's way. However, remote operation faces fundamental challenges due to limits in communication delay and bandwidth. The proposed work improves the performances of teleoperation architecture based on Fractal Impedance Controller (FIC), by integrating the most recent manipulation architecture in the haptic teleoperation pipeline. The updated controller takes advantage of the inverse kinematics optimisation in the manipulation, and hence improves dynamic interactions during fine manipulation without renouncing the robustness of the FIC controller. Additionally, the proposed method allows an online trade-off between the manipulation controller and the teleoperated behaviour, allowing a safe superimposition of these two behaviours. The validated experimental results show that the proposed method is robust to reduced communication bandwidth and delays. Moreover, we demonstrated that the remote teleoperated robot remains stable and safe to interact with, even when the communication with the master side is abruptly interrupted.
【32】 Robust Impedance Control for Dexterous Interaction Using Fractal Impedance Controller with IK-Optimisation 标题：基于IK优化分形阻抗控制器的灵巧交互鲁棒阻抗控制 链接：https://arxiv.org/abs/2109.04516
作者：Carlo Tiseo,Quentin Rouxel,Zhibin Li,Michael Mistry 机构： Instituteof Perception Action and Behaviour, School of Informatics, University ofEdinburgh 摘要：在日常环境中与人类一起移动机器人需要强大的动态交互。优化和学习方法已被用于模拟和再现人体运动。然而，它们通常并不健壮，并且它们的推广是有限的。这项工作提出了一种机器人的分层控制体系结构，并提供了在未知交互动力学过程中再现类人运动的能力。我们的结果表明，再现的末端执行器轨迹可以保留通过运动捕捉系统记录的初始人体运动的主要特征，并且对外部扰动具有鲁棒性。数据表明，由于硬件的物理限制，无法达到人体运动记录的相同速度，一些详细的运动很难再现。然而，这些技术问题可以通过使用更好的硬件来解决，我们提出的算法仍然可以用于产生模拟运动。 摘要：Robust dynamic interactions are required to move robots in daily environments alongside humans. Optimisation and learning methods have been used to mimic and reproduce human movements. However, they are often not robust and their generalisation is limited. This work proposed a hierarchical control architecture for robot manipulators and provided capabilities of reproducing human-like motions during unknown interaction dynamics. Our results show that the reproduced end-effector trajectories can preserve the main characteristics of the initial human motion recorded via a motion capture system, and are robust against external perturbations. The data indicate that some detailed movements are hard to reproduce due to the physical limits of the hardware that cannot reach the same velocity recorded in human movements. Nevertheless, these technical problems can be addressed by using better hardware and our proposed algorithms can still be applied to produce imitated motions.
【33】 BDPGO: Balanced Distributed Pose Graph Optimization Framework for Swarm Robotics 标题：BDPGO：群体机器人平衡分布位姿图优化框架 链接：https://arxiv.org/abs/2109.04502
作者：Hao Xu,Shaojie Shen 机构： the robotsneed to solve the sub-problems individually as well as ex-All authors are with the Department of Electronic and Computer Engineer-ing, Hong Kong University of Science and Technology 备注：Submitted to IEEE Robotics and Automation Letters (RA-L) with ICRA 2022 摘要：分布式位姿图优化（DPGO）是群机器人的基本技术之一。目前，DPGO的子问题是基于本地姿势构建的。我们的验证证明，这种方法可能会在现实场景中引入子问题大小的不平衡，从而影响DPGO优化的速度，并可能增加通信需求。此外，当群中的机器人发生故障或部分机器人断开连接时，无法保证估计姿态的一致性。在本文中，我们提出了BDPGO，一个平衡的分布式位姿图优化框架，使用了解耦机器人位姿和DPGO的思想。BDPGO通过引入两阶段图划分方法构建平衡子问题，以平衡的方式将位姿图中的位姿重新分配给机器人群。我们的验证表明，BDPGO在不改变实际数据集中DPGO的特定算法的情况下，显著提高了优化速度。此外，我们还验证了BDPGO对机器人故障、无线网络变化的鲁棒性。BDPGO能够在这些情况下保持估计姿势的一致性。该框架还可以应用于分布式求解因子图所涉及的其他协作同步定位和映射（CSLAM）问题。 摘要：Distributed pose graph optimization (DPGO) is one of the fundamental techniques of swarm robotics. Currently, the sub-problems of DPGO are built on the native poses. Our validation proves that this approach may introduce an imbalance in the sizes of the sub-problems in real-world scenarios, which affects the speed of DPGO optimization, and potentially increases communication requirements. In addition, the coherence of the estimated poses is not guaranteed when the robots in the swarm fail, or partial robots are disconnected. In this paper, we propose BDPGO, a balanced distributed pose graph optimization framework using the idea of decoupling the robot poses and DPGO. BDPGO re-distributes the poses in the pose graph to the robot swarm in a balanced way by introducing a two-stage graph partitioning method to build balanced subproblems. Our validation demonstrates that BDPGO significantly improves the optimization speed without changing the specific algorithm of DPGO in realistic datasets. What's more, we also validate that BDPGO is robust to robot failure, changes in the wireless network. BDPGO has capable of keeps the coherence of the estimated poses in these situations. The framework also has the potential to be applied to other collaborative simultaneous localization and mapping (CSLAM) problems involved in distributedly solving the factor graph.
【34】 GPS-Denied Navigation Using Low-Cost Inertial Sensors and Recurrent Neural Networks 标题：基于低成本惯性传感器和递归神经网络的GPS拒绝导航 链接：https://arxiv.org/abs/2109.04861
作者：Ahmed AbdulMajuid,Osama Mohamady,Mohannad Draz,Gamal El-bayoumi 机构：Aerospace Engineering Department, Cairo University, ElGamaa St., Giza , Egypt 备注：17 pages, 39 figures 摘要：无人驾驶飞机的自主任务要求对无人驾驶飞机的姿态、速度和位置进行连续可靠的估计。传统上，这些状态是通过将扩展卡尔曼滤波器（EKF）应用于加速度计、陀螺仪、气压计、磁强计和GPS测量来估计的。当GPS信号丢失时，位置和速度估计会迅速恶化，尤其是在使用低成本惯性传感器时。本文提出了一种使用递归神经网络（RNN）的估计方法，以便在没有GPS信号的情况下可靠地估计无人机的位置和速度。RNN在使用Pixhawk收集的公共数据集上进行训练。这种低成本的商用自动驾驶仪记录原始传感器测量值（网络输入）和相应的EKF估计值（地面真值输出）。该数据集由548个不同的飞行日志组成，飞行持续时间从4分钟到32分钟不等。训练共使用465次航班，共计45小时。其余83次航班共计8小时被推迟验证。单次飞行中的误差被视为RNN预测（无GPS）和地面真实值（EKF有GPS）之间3D位置的最大绝对差（MPE）。在验证集上，平均MPE为35米。使用所提出的方法可以在5分钟的飞行中获得低至2.7米的MPE值。90%的验证飞行的MPE限制在166米以下。对该网络进行了实验测试并实时工作。 摘要：Autonomous missions of drones require continuous and reliable estimates for the drone's attitude, velocity, and position. Traditionally, these states are estimated by applying Extended Kalman Filter (EKF) to Accelerometer, Gyroscope, Barometer, Magnetometer, and GPS measurements. When the GPS signal is lost, position and velocity estimates deteriorate quickly, especially when using low-cost inertial sensors. This paper proposes an estimation method that uses a Recurrent Neural Network (RNN) to allow reliable estimation of a drone's position and velocity in the absence of GPS signal. The RNN is trained on a public dataset collected using Pixhawk. This low-cost commercial autopilot logs the raw sensor measurements (network inputs) and corresponding EKF estimates (ground truth outputs). The dataset is comprised of 548 different flight logs with flight durations ranging from 4 to 32 minutes. For training, 465 flights are used, totaling 45 hours. The remaining 83 flights totaling 8 hours are held out for validation. Error in a single flight is taken to be the maximum absolute difference in 3D position (MPE) between the RNN predictions (without GPS) and the ground truth (EKF with GPS). On the validation set, the median MPE is 35 meters. MPE values as low as 2.7 meters in a 5-minutes flight could be achieved using the proposed method. The MPE in 90% of the validation flights is bounded below 166 meters. The network was experimentally tested and worked in real-time.
【35】 DIRECT: A Differential Dynamic Programming Based Framework for Trajectory Generation 标题：DIRECT：一种基于差分动态规划的弹道生成框架 链接：https://arxiv.org/abs/2109.04686
作者：Kun Cao,Muqing Cao,Shenghai Yuan,Lihua Xie 机构： Xie (cor-responding author) are with School of Electrical and Electronic Engineering, Nanyang Technological University 备注：8 pages, 5 figures 摘要：介绍了一种基于微分动态规划（DDP）的微分平坦系统多项式轨迹生成框架。特别是，不像文献中那样，使用尺寸不断增大的线性方程来表示多个多项式段，我们从状态空间表示的角度出发，将线性方程简化为具有固定状态维数的有限时域控制系统，并自动满足连续多项式所需的连续性条件。因此，约束轨迹生成问题（包括有时间优化和无时间优化）可以转化为具有不等式约束的离散时间有限时域最优控制问题，这可以通过最近发展的内点DDP（IPDDP）算法来实现。此外，对于具有预分配时间的无约束轨迹生成，我们证明了该问题确实是一个线性二次跟踪（LQT）问题（精确一次迭代的DDP算法）。所有这些算法都具有与分段数相关的线性复杂度。通过与最新方法的数值比较和物理实验，验证了我们理论结果的有效性。实施代码将是开源的， 摘要：This paper introduces a differential dynamic programming (DDP) based framework for polynomial trajectory generation for differentially flat systems. In particular, instead of using a linear equation with increasing size to represent multiple polynomial segments as in literature, we take a new perspective from state-space representation such that the linear equation reduces to a finite horizon control system with a fixed state dimension and the required continuity conditions for consecutive polynomials are automatically satisfied. Consequently, the constrained trajectory generation problem (both with and without time optimization) can be converted to a discrete-time finite-horizon optimal control problem with inequality constraints, which can be approached by a recently developed interior-point DDP (IPDDP) algorithm. Furthermore, for unconstrained trajectory generation with preallocated time, we show that this problem is indeed a linear-quadratic tracking (LQT) problem (DDP algorithm with exact one iteration). All these algorithms enjoy linear complexity with respect to the number of segments. Both numerical comparisons with state-of-the-art methods and physical experiments are presented to verify and validate the effectiveness of our theoretical findings. The implementation code will be open-sourced,
