机器人相关学术速递[12.21]

cs.RO机器人相关，共计15篇

【1】 Learning Spatio-Temporal Specifications for Dynamical Systems 标题：学习动态系统的时空规范 链接：https://arxiv.org/abs/2112.10714

作者：Suhail Alsalehi,Erfan Aasi,Ron Weiss,Calin Belta 机构：: Division of Systems Engineering, Boston University, Boston, MA, USA, : Mechanical Engineering Department, Boston University, Boston, MA, USA, : Biological Engineering Department, Massachusetts Institute of Technology, Cambridge, MA, USA 备注：12 pages, submitted to L4DC 2021 摘要：从数据中学习动力系统的属性提供了重要的见解，帮助我们理解此类系统并缓解不期望的结果。在这项工作中，我们提出了一个从数据中学习时空（ST）属性作为形式逻辑规范的框架。我们介绍了SVM-STL，它是信号时序逻辑（STL）的一个扩展，能够描述各种具有时变空间模式的动态系统的时空特性。我们的框架利用机器学习技术从空间模式序列给出的系统执行中学习SVM-STL规范。我们提出了处理标记和未标记数据的方法。此外，给定SVM-STL规范形式的系统需求，我们提供了一种参数合成方法，以找到最大程度满足此类规范的参数。我们的学习框架和参数综合方法在一个反应扩散系统的例子中得到了展示。 摘要：Learning dynamical systems properties from data provides important insights that help us understand such systems and mitigate undesired outcomes. In this work, we propose a framework for learning spatio-temporal (ST) properties as formal logic specifications from data. We introduce SVM-STL, an extension of Signal Signal Temporal Logic (STL), capable of specifying spatial and temporal properties of a wide range of dynamical systems that exhibit time-varying spatial patterns. Our framework utilizes machine learning techniques to learn SVM-STL specifications from system executions given by sequences of spatial patterns. We present methods to deal with both labeled and unlabeled data. In addition, given system requirements in the form of SVM-STL specifications, we provide an approach for parameter synthesis to find parameters that maximize the satisfaction of such specifications. Our learning framework and parameter synthesis approach are showcased in an example of a reaction-diffusion system.

【2】 Omni-Roach: A legged robot capable of traversing multiple types of large obstacles and self-righting 标题：Omni-Roach：一种能够穿越多种类型的大型障碍物并自扶正的腿部机器人 链接：https://arxiv.org/abs/2112.10614

作者：Jonathan Mi,Yaqing Wang,Chen Li 摘要：机器人擅长躲避障碍物，但仍然难以穿越复杂的三维地形，这些地形上有杂乱的大型障碍物。相比之下，像蟑螂这样的昆虫更擅长这样做。我们实验室最近的研究阐明了在复杂三维地形中抽象出的各种运动挑战中，如何从运动环境交互中产生运动过渡，以及哪些策略可以克服这些挑战。在这里，我们基于这些基本见解开发了一种蟑螂启发的腿型机器人，Om ni Roach，它集成了这些多功能运动策略。该机器人基于RHex级设计，具有六条柔顺的腿，具有圆形的身体形状和两个可以打开的翅膀，具有俯仰和偏航自由度的活动尾巴。通过两次开发和测试迭代，我们的机器人能够以高性能和成功率克服所有运动挑战。它穿过杂乱的刚性支柱，相隔仅1.1倍的机器人身体宽度，2.5倍的臀部高度，0.75倍的身体长度间隙，密集杂乱的柔性梁，相隔仅65%的身体宽度，并在4秒内自行扶正。通过对尾部使用和接近角度的系统实验，可以发现一个指向下方的主动尾部和对地面的轻击有助于将身体滚动到间隙中，并打破摩擦和互锁接触以进行横向移动。 摘要：Robots excel at avoiding obstacles but still struggle to traverse complex 3-D terrain with cluttered large obstacles. By contrast, insects like cockroaches excel at doing so. Recent research in our lab elucidated how locomotor transitions emerge from locomotor-environment interaction for diverse locomotor challenges abstracted for complex 3-D terrain and what strategies overcome these challenges. Here, we build on these fundamental insights to develop a cockroach-inspired legged robot, Om-ni-Roach, that integrates these strategies for multi-functional locomotion. The robot is based on the RHex-class design with six compliant legs and features a rounded body shape with two wings that can open, an active tail with pitch and yaw degrees of freedom. Through two development and testing iterations, our robot was capable of overcoming all locomotor challenges with high performance and success rate. It traversed cluttered rigid pillars only 1.1X robot body width apart, a 2.5X hip height bump, a 0.75X body length gap, densely cluttered flexible beams only 65% its body width apart, and self-righted within 4 seconds. Systematic experiments on tail use and approach angle for beam traversal revealed that an active tail pointed downward and tapping against the ground helps roll the body into the gap and break frictional and interlocking contact to traverse.

【3】 Benchmarking Safe Deep Reinforcement Learning in Aquatic Navigation 标题：水上航海安全深度强化学习的标杆研究 链接：https://arxiv.org/abs/2112.10593

作者：Enrico Marchesini,Davide Corsi,Alessandro Farinelli 机构： This aspect is par-ticularly important in our scenario where the unpredictablemovements of the water significantly increase the complexityequal contributionAuthors are with the Department of Computer Science, University ofVerona 备注：6 pages, 5 figures, 1 table. Accepted at IROS 2021 摘要：我们提出了一种新的基于水上导航的安全强化学习基准环境。由于非平稳环境和机器人平台的不确定性，水上导航是一项极具挑战性的任务，因此，通过分析训练网络的行为以避免危险情况（例如碰撞），考虑问题的安全方面是至关重要的。为此，我们考虑了一种基于价值和策略梯度的深度强化学习（DRL），并提出了一种基于交叉的策略，它结合了基于梯度和梯度的DRL来提高样本效率。此外，我们提出了一种基于区间分析的验证策略，用于检查训练模型在一组期望属性上的行为。我们的结果表明，基于交叉的训练优于先前的DRL方法，而我们的验证允许我们量化违反属性描述的行为的配置数量。至关重要的是，这将作为未来在这一应用领域研究的基准。 摘要：We propose a novel benchmark environment for Safe Reinforcement Learning focusing on aquatic navigation. Aquatic navigation is an extremely challenging task due to the non-stationary environment and the uncertainties of the robotic platform, hence it is crucial to consider the safety aspect of the problem, by analyzing the behavior of the trained network to avoid dangerous situations (e.g., collisions). To this end, we consider a value-based and policy-gradient Deep Reinforcement Learning (DRL) and we propose a crossover-based strategy that combines gradient-based and gradient-free DRL to improve sample-efficiency. Moreover, we propose a verification strategy based on interval analysis that checks the behavior of the trained models over a set of desired properties. Our results show that the crossover-based training outperforms prior DRL approaches, while our verification allows us to quantify the number of configurations that violate the behaviors that are described by the properties. Crucially, this will serve as a benchmark for future research in this domain of applications.

【4】 From Robot Self-Localization to Global-Localization: An RSSI Based Approach 标题：从机器人自定位到全局定位：一种基于RSSI的方法 链接：https://arxiv.org/abs/2112.10578

作者：Athanasios Lentzas,Dimitris Vrakas 机构： Themajor drawback of this approach is the increased cost of the 1AthanasiosLentzasisPhDcandidateintheSchoolofInformaticsatAristotleUniversityofThessaloniki, gr 2DimitrisVrakasisanassistantprofessorintheSchoolofInformaticsatAristotleUniversityofThessaloniki 备注：submitted to ICRA2022 摘要：定位是自主移动机器人在其环境中成功移动到目标位置的关键任务。通常这是以机器人为中心的方式完成的，机器人以身体为中心维护地图。在群机器人应用中，一组机器人需要协调以实现其共同目标，以机器人为中心的定位将不够，因为群的每个成员都有自己的参考框架。解决这个问题的一种方法是在swarm成员之间创建、维护和共享一个公共地图（全球坐标系）。本文提出了一种在未知、GPS和无地标环境中对一组机器人进行全局定位的方法，该方法扩展了瓢虫算法的定位方案。其主要思想是依靠蜂群成员保持静止，充当信标，发射电磁信号。这些静止的机器人形成一个全局参考框架，其余的机器人使用接收到的信号强度指示器（RSSI）将自己定位在其中。对提出的方法进行了评价，实验结果令人满意。 摘要：Localization is a crucial task for autonomous mobile robots in order to successfully move to goal locations in their environment. Usually this is done in a robot-centric manner, where the robot maintains a map with its body in the center. In swarm robotics applications, where a group of robots need to coordinate in order to achieve their common goals, robot-centric localization will not suffice as each member of the swarm has its own frame of reference. One way to deal with this problem is to create, maintain and share a common map (global coordinate system), among the members of the swarm. This paper presents an approach to global localization for a group of robots in unknown, GPS and landmark free environments that extends the localization scheme of the LadyBug algorithm. The main idea relies on members of the swarm stay still and act as beacons, emitting electromagnetic signals. These stationary robots form a global frame of reference and the rest of the group localize themselves in it using the received signal strength indicator (RSSI). The proposed method is evaluated, and the results obtained from the experiments are promising.

【5】 Prioritized Hierarchical Compliance Control for Dual-Arm Robot Stable Clamping 标题：双臂机器人稳定夹紧的优先递阶柔顺控制 链接：https://arxiv.org/abs/2112.10444

作者：Xiaoyu Ren,Liqun Huang,Mingguo Zhao 机构： Tsinghua University and Beijing InnovationCenter for Future Chips 备注：7 pages, 7 figures, accepted by IEEE ROBIO 2021 摘要：当双臂机器人在人类环境中夹紧刚性物体时，环境或协作人会对被操作物体或机器人手臂施加偶然干扰，导致夹紧失败，损坏机器人甚至伤害人类。本研究提出了一种优先级递阶柔顺控制，以同时处理双臂机器人夹紧过程中的两种干扰。首先，我们使用层次二次规划（HQP）来求解机器人在关节约束下的逆运动学，并优先考虑对象上的干扰对机器人手臂上的干扰。其次，我们使用F/T传感器估计整个动量观测器的干扰力，并采用导纳控制来实现柔度。最后，我们在一个14自由度位置控制的双臂机器人WalkerX上进行了验证实验，稳定地夹紧刚性物体，同时实现了对干扰的遵从性。 摘要：When a dual-arm robot clamps a rigid object in an environment for human beings, the environment or the collaborating human will impose incidental disturbance on the operated object or the robot arm, leading to clamping failure, damaging the robot even hurting the human. This research proposes a prioritized hierarchical compliance control to simultaneously deal with the two types of disturbances in the dual-arm robot clamping. First, we use hierarchical quadratic programming (HQP) to solve the robot inverse kinematics under the joint constraints and prioritize the compliance for the disturbance on the object over that on the robot arm. Second, we estimate the disturbance forces throughout the momentum observer with the F/T sensors and adopt admittance control to realize the compliances. Finally, we perform the verify experiments on a 14-DOF position-controlled dual-arm robot WalkerX, clamping a rigid object stably while realizing the compliance against the disturbances.

【6】 Lane Departure Prediction Based on Closed-Loop Vehicle Dynamics 标题：基于闭环车辆动力学的车道偏离预测 链接：https://arxiv.org/abs/2112.10379

作者：Daofei Li,Siyuan Lin,Guanming Liu 备注：12 pages, 23 figures 摘要：自动驾驶系统应具有监督自身性能的能力，并在必要时请求人工驾驶人接管。在车道保持场景中，车辆未来轨迹的预测是实现安全可靠驾驶自动化的关键。以往对车辆轨迹预测的研究主要分为两类，即基于物理的方法和基于操纵的方法。采用基于物理的方法，提出了一种基于闭环车辆动力学模型的车道偏离预测算法。我们使用扩展卡尔曼滤波器根据传感模块输出估计当前车辆状态。然后，使用具有实际车道保持控制律的卡尔曼预测器来预测未来的转向动作和车辆状态。车道偏离评估模块评估车辆转弯位置的概率分布，并决定是否发起人工接管请求。该预测算法能够描述未来车辆姿态的随机特性，仿真试验初步证明了这一点。最后，以15～50km/h的速度进行的道路试验进一步表明，提出的方法能够准确地预测车辆的未来轨迹。对于自动车道保持功能的车道偏离风险评估，它可能是一个有前途的解决方案。 摘要：An automated driving system should have the ability to supervise its own performance and to request human driver to take over when necessary. In the lane keeping scenario, the prediction of vehicle future trajectory is the key to realize safe and trustworthy driving automation. Previous studies on vehicle trajectory prediction mainly fall into two categories, i.e. physics-based and manoeuvre-based methods. Using a physics-based methodology, this paper proposes a lane departure prediction algorithm based on closed-loop vehicle dynamics model. We use extended Kalman filter to estimate the current vehicle states based on sensing module outputs. Then a Kalman Predictor with actual lane keeping control law is used to predict steering actions and vehicle states in the future. A lane departure assessment module evaluates the probabilistic distribution of vehicle corner positions and decides whether to initiate a human takeover request. The prediction algorithm is capable to describe the stochastic characteristics of future vehicle pose, which is preliminarily proved in simulated tests. Finally, the on-road tests at speeds of 15 to 50 km/h further show that the pro-posed method can accurately predict vehicle future trajectory. It may work as a promising solution to lane departure risk assessment for automated lane keeping functions.

【7】 Towards Computational Awareness in Autonomous Robots: An Empirical Study of Computational Kernels 标题：自主机器人的计算意识：计算内核的实证研究 链接：https://arxiv.org/abs/2112.10303

作者：Ashrarul H. Sifat,Burhanuddin Bharmal,Haibo Zeng,Jia-Bin Huang,Changhee Jung,Ryan K. Williams 机构：Williams, Accepted: TBD 备注：25 pages, 20 figures, submitted to Journal of Intelligent and Robotic Systems 摘要：自动机器人对日常生活的潜在影响在精密农业、搜索和救援以及基础设施检查等新兴应用中显而易见。然而，此类应用需要在未知和非结构化环境中运行，这些环境具有广泛而复杂的目标集，所有这些都受到严格的计算和电源限制。因此，我们认为，实现机器人自主的计算内核必须进行调度和优化，以保证及时和正确的行为，同时允许在运行时重新配置调度参数。在本文中，我们认为一个必要的第一步，这一目标的自主机器人的计算意识：从资源管理的角度来看，一套基本的计算内核的实证研究。具体而言，我们对三个嵌入式计算平台上用于定位和映射、路径规划、任务分配、深度估计和光流的内核的时间、功率和内存性能进行了数据驱动的研究。我们对这些内核进行分析，以深入了解具有计算意识的自主机器人的调度和动态资源管理。值得注意的是，我们的结果表明，内核性能与机器人的操作环境存在相关性，这证明了计算感知机器人的概念，以及为什么我们的工作是实现这一目标的关键一步。 摘要：The potential impact of autonomous robots on everyday life is evident in emerging applications such as precision agriculture, search and rescue, and infrastructure inspection. However, such applications necessitate operation in unknown and unstructured environments with a broad and sophisticated set of objectives, all under strict computation and power limitations. We therefore argue that the computational kernels enabling robotic autonomy must be scheduled and optimized to guarantee timely and correct behavior, while allowing for reconfiguration of scheduling parameters at run time. In this paper, we consider a necessary first step towards this goal of computational awareness in autonomous robots: an empirical study of a base set of computational kernels from the resource management perspective. Specifically, we conduct a data-driven study of the timing, power, and memory performance of kernels for localization and mapping, path planning, task allocation, depth estimation, and optical flow, across three embedded computing platforms. We profile and analyze these kernels to provide insight into scheduling and dynamic resource management for computation-aware autonomous robots. Notably, our results show that there is a correlation of kernel performance with a robot's operational environment, justifying the notion of computation-aware robots and why our work is a crucial step towards this goal.

【8】 Distributed Adaptive and Resilient Control of Multi-Robot Systems with Limited Field of View Interactions 标题：有限视场交互多机器人系统的分布式自适应弹性控制 链接：https://arxiv.org/abs/2112.10285

作者：Pratik Mukherjee,Matteo Santilli,Andrea Gasparri,Ryan K. Williams 机构： Virginia Polytechnic Institute and State University, RomaTreUniversity 备注：9 pages, 5 figures, submitted to IEEE Robotics and Automation Letters. arXiv admin note: substantial text overlap with arXiv:2011.06179 摘要：在本文中，我们考虑两个耦合问题的分布式多机器人系统（MRSS）协调与有限视场（FOV）传感器：自适应调整交互增益和拒绝传感器攻击。首先，分布式控制框架（例如，势场）的一个典型缺点是，整个系统行为对分配给相对交互的增益高度敏感。其次，具有有限FOV传感器的MRS可能更容易受到针对其FOV的传感器攻击，因此必须能够抵御此类攻击。基于这些缺点，我们提出了一个综合解决方案，将自适应增益调整和攻击恢复能力结合起来，解决具有有限FOV的MRS拓扑控制问题。具体地说，我们首先推导了一种基于满足标称成对交互的自适应增益调节方案，该方案在机器人的邻域中产生交互强度的动态平衡。然后，我们对附加传感器和执行器攻击（或故障）进行建模，并通过采用静态输出反馈技术推导出H∞控制协议，从而保证攻击（故障）信号引起的误差的有界L2增益。最后，使用ROS Gazebo的模拟结果支持了我们的理论发现。 摘要：In this paper, we consider two coupled problems for distributed multi-robot systems (MRSs) coordinating with limited field of view (FOV) sensors: adaptive tuning of interaction gains and rejection of sensor attacks. First, a typical shortcoming of distributed control frameworks (e.g., potential fields) is that the overall system behavior is highly sensitive to the gain assigned to relative interactions. Second, MRSs with limited FOV sensors can be more susceptible to sensor attacks aimed at their FOVs, and therefore must be resilient to such attacks. Based on these shortcomings, we propose a comprehensive solution that combines efforts in adaptive gain tuning and attack resilience to the problem of topology control for MRSs with limited FOVs. Specifically, we first derive an adaptive gain tuning scheme based on satisfying nominal pairwise interactions, which yields a dynamic balancing of interaction strengths in a robot's neighborhood. We then model additive sensor and actuator attacks (or faults) and derive H infinity control protocols by employing a static output-feedback technique, guaranteeing bounded L2 gains of the error induced by the attack (fault) signals. Finally, simulation results using ROS Gazebo are provided to support our theoretical findings.

【9】 Control of a Hexapod Robot Considering Terrain Interaction 标题：考虑地形相互作用的六足机器人控制 链接：https://arxiv.org/abs/2112.10206

作者：Marco Zangrandi,Stefano Arrigoni,Francesco Braghin 机构：Politecnico di Milano, Mechanical Department, via G. La Masa, - , Milano Italy 摘要：仿生步行六足机器人是机器人技术中一个相对年轻的分支，无论在技术水平还是应用领域。尽管它们的冗余设计带来了高度的灵活性和适应性，但与它们的能力互补的研究领域仍然非常缺乏。本文将提出最先进的六足机器人专用控制体系结构，允许对机器人速度、身体方向和行走步态类型进行完全控制。此外，将对地形交互进行深入研究，从而开发出地形自适应控制算法，使机器人能够对地形形状和粗糙度（如工作空间内的非线性和非连续性）做出快速反应。将提供一个从六足动物运动解释中得出的动力学模型，该模型可与基础平台PKM机器的动力学模型相比较，并将通过Matlab SimMechanicsTM物理仿真验证该模型。反馈控制系统能够识别腿部地形接触，并做出相应反应，以确保运动稳定性。最后报告了Trossen RobotosTM基于PhantomX AX金属六足Mark II机器人平台的实验活动的结果。 摘要：Bio-inspired walking hexapod robots are a relatively young branch in robotics in both state of the art and applications. Despite their high degree of flexibility and adaptability derived by their redundant design, the research field that compliments their abilities is still very lacking. In this paper will be proposed state-of-the-art hexapod robot specific control architecture that allows for full control over robot speed, body orientation and walk gait type to employ. Furthermore terrain interaction will be deeply investigated, leading to the development of a terrain-adapting control algorithm that will allow the robot to react swiftly to terrain shape and asperities such as non-linearities and non-continuity within the workspace. It will be presented a dynamic model derived from the interpretation of the hexapod movement to be comparable to these of the base-platform PKM machines, and said model will be validated through Matlab SimMechanicsTM physics simulation. A feed-back control system able to recognize leg-terrain touch and react accordingly to assure movement stability will then be developed. Finally results coming from an experimental campaign based of the PhantomX AX Metal Hexapod Mark II robotic platform by Trossen RoboticsTM is reported.

【10】 RoboAssembly: Learning Generalizable Furniture Assembly Policy in a Novel Multi-robot Contact-rich Simulation Environment 标题：RoboAssembly：在新型多机器人富接触仿真环境中学习泛化家具装配策略 链接：https://arxiv.org/abs/2112.10143

作者：Mingxin Yu,Lin Shao,Zhehuan Chen,Tianhao Wu,Qingnan Fan,Kaichun Mo,Hao Dong 机构： Peking University, Stanford University 备注：Submitted to IEEE International Conference on Robotics and Automation (ICRA) 2022 摘要：零件组装是机器人技术中一项典型但具有挑战性的任务，机器人将一组单独的零件组装成一个完整的形状。在本文中，我们开发了一个用于家具装配的机器人装配仿真环境。我们将零件装配任务描述为一个具体的强化学习问题，并提出了一个机器人学习装配各种椅子的管道。实验表明，当使用看不见的椅子进行测试时，我们的方法在以对象为中心设置下的成功率为74.5%，在完全设置下的成功率为50.0%。我们采用RRT连接算法作为基线，在计算时间显著延长后，成功率仅为18.8%。补充材料和视频可在我们的项目网页上找到。 摘要：Part assembly is a typical but challenging task in robotics, where robots assemble a set of individual parts into a complete shape. In this paper, we develop a robotic assembly simulation environment for furniture assembly. We formulate the part assembly task as a concrete reinforcement learning problem and propose a pipeline for robots to learn to assemble a diverse set of chairs. Experiments show that when testing with unseen chairs, our approach achieves a success rate of 74.5% under the object-centric setting and 50.0% under the full setting. We adopt an RRT-Connect algorithm as the baseline, which only achieves a success rate of 18.8% after a significantly longer computation time. Supplemental materials and videos are available on our project webpage.

【11】 Online Grounding of PDDL Domains by Acting and Sensing in Unknown Environments 标题：在未知环境中通过作用和传感实现PDDL域的在线接地 链接：https://arxiv.org/abs/2112.10007

作者：Leonardo Lamanna,Luciano Serafini,Alessandro Saetti,Alfonso Gerevini,Paolo Traverso 机构：Traverso , Fondazione Bruno Kessler (FBK), Trento, Italy, Department of Information Engineering, University of Brescia, Italy 摘要：为了有效地使用抽象（PDDL）规划域在未知环境中实现目标，代理必须使用环境对象及其属性实例化此类域。如果代理对环境有一个以自我为中心的局部视图，那么它需要在规划域中对感知到的数据进行操作、感知和抽象。此外，代理需要将由符号规划器计算的计划编译为可由其执行器执行的低级操作。本文提出了一个框架，旨在实现上述观点，并允许代理执行不同的任务。为此，我们集成了机器学习模型来提取感官数据、目标实现的符号规划和导航的路径规划。我们在精确的模拟环境中评估了该方法，其中传感器为RGB-D车载摄像机、GPS和指南针。 摘要：To effectively use an abstract (PDDL) planning domain to achieve goals in an unknown environment, an agent must instantiate such a domain with the objects of the environment and their properties. If the agent has an egocentric and partial view of the environment, it needs to act, sense, and abstract the perceived data in the planning domain. Furthermore, the agent needs to compile the plans computed by a symbolic planner into low level actions executable by its actuators. This paper proposes a framework that aims to accomplish the aforementioned perspective and allows an agent to perform different tasks. For this purpose, we integrate machine learning models to abstract the sensory data, symbolic planning for goal achievement and path planning for navigation. We evaluate the proposed method in accurate simulated environments, where the sensors are RGB-D on-board camera, GPS and compass.

【12】 Learning-based methods to model small body gravity fields for proximity operations: Safety and Robustness 标题：用于近距离操作的基于学习的小物体重力场建模方法：安全性和健壮性 链接：https://arxiv.org/abs/2112.09998

作者：Daniel Neamati,Yashwanth Kumar Nakka,Soon-Jo Chung 机构：California Institute of Technology, Pasadena, CA 备注：Accepted Scitech, AI for Space 摘要：精确的重力场模型对于小天体周围的安全接近操作至关重要。最先进的技术使用球谐函数或高保真多面体形状模型。不幸的是，这些技术可能在小天体表面附近变得不准确，或具有较高的计算成本，特别是对于二进制或异构小天体。新的基于学习的技术不编码预定义的结构，更通用。作为多功能性的交换，基于学习的技术在训练数据域之外可能不那么健壮。在部署过程中，航天器轨道是动力学数据的主要来源。因此，训练数据域应包括航天器轨迹，以准确评估学习模型的安全性和鲁棒性。我们开发了一种基于学习的重力模型的新方法，该方法直接使用航天器过去的轨迹。我们进一步介绍了一种通过比较训练域内外的准确性来评估基于学习的技术的安全性和鲁棒性的方法。我们在两个基于学习的框架：高斯过程和神经网络中演示了这种安全性和鲁棒性方法。根据提供的详细分析，我们根据经验确定了当用于接近操作时，需要对学习的重力模型进行鲁棒性验证。 摘要：Accurate gravity field models are essential for safe proximity operations around small bodies. State-of-the-art techniques use spherical harmonics or high-fidelity polyhedron shape models. Unfortunately, these techniques can become inaccurate near the surface of the small body or have high computational costs, especially for binary or heterogeneous small bodies. New learning-based techniques do not encode a predefined structure and are more versatile. In exchange for versatility, learning-based techniques can be less robust outside the training data domain. In deployment, the spacecraft trajectory is the primary source of dynamics data. Therefore, the training data domain should include spacecraft trajectories to accurately evaluate the learned model's safety and robustness. We have developed a novel method for learning-based gravity models that directly uses the spacecraft's past trajectories. We further introduce a method to evaluate the safety and robustness of learning-based techniques via comparing accuracy within and outside of the training domain. We demonstrate this safety and robustness method for two learning-based frameworks: Gaussian processes and neural networks. Along with the detailed analysis provided, we empirically establish the need for robustness verification of learned gravity models when used for proximity operations.

【13】 Derivative Action Control: Smooth Model Predictive Path Integral Control without Smoothing 标题：导数作用控制：无平滑的平滑模型预测路径积分控制 链接：https://arxiv.org/abs/2112.09988

作者：Taekyung Kim,Gyuhyun Park,Jihwan Bae,Wonsuk Lee 机构： ThisThe authors are with the Ground Technology Research Institute 备注：7 pages, 5 figures 摘要：在这里，我们提出了一种新的方法来生成平滑控制序列的模型预测路径积分控制（MPPI）任务没有任何额外的平滑算法。我们的方法有效地减轻了采样时的抖动，而MPPI的信息论推导保持不变。我们在一个具有挑战性的自主驾驶任务中演示了该方法，并对不同的算法进行了定量评估。还提出了一种用于在不同路面摩擦条件下估计系统动力学的神经网络车辆模型。我们的视频可以在以下网址找到：url{https://youtu.be/o3Nmi0UJFqg}. 摘要：Here, we present a new approach to generate smooth control sequences in Model Predictive Path Integral control (MPPI) tasks without any additional smoothing algorithms. Our method effectively alleviates the chattering in sampling, while the information theoretic derivation of MPPI remains the same. We demonstrated the proposed method in a challenging autonomous driving task with quantitative evaluation of different algorithms. A neural network vehicle model for estimating system dynamics under varying road friction conditions is also presented. Our video can be found at: url{https://youtu.be/o3Nmi0UJFqg}.

【14】 Fast and Robust Registration of Partially Overlapping Point Clouds 标题：快速稳健的部分重叠点云配准 链接：https://arxiv.org/abs/2112.09922

作者：Eduardo Arnold,Sajjad Mozaffari,Mehrdad Dianati 机构： University of Warwick 备注：Accepted at IEEE Robotics and Automation Letters (RA-L). 8 pages, 6 figures, 3 tables 摘要：部分重叠点云的实时配准在自主车辆协作感知和多智能体SLAM中有着新兴的应用。在这些应用中，点云之间的相对平移比传统SLAM和里程计应用中的相对平移要高，这对通信的识别和成功注册提出了挑战。在本文中，我们提出了一种新的部分重叠点云配准方法，其中对应关系使用有效的逐点特征编码器学习，并使用基于图的注意网络进行细化。该注意网络利用关键点之间的几何关系来改善低重叠点云中的匹配。推理时，通过样本一致性对对应关系进行稳健拟合，得到相对位姿变换。评估是在KITTI数据集和一个新的合成数据集上进行的，该数据集包括位移高达30m的低重叠点云。该方法在KITTI数据集上实现了与最新方法相当的性能，并且优于现有的低重叠点云方法。此外，所提出的方法实现了显著更快的推理时间，低至410ms，比竞争方法快5到35倍。我们的代码和数据集可在https://github.com/eduardohenriquearnold/fastreg. 摘要：Real-time registration of partially overlapping point clouds has emerging applications in cooperative perception for autonomous vehicles and multi-agent SLAM. The relative translation between point clouds in these applications is higher than in traditional SLAM and odometry applications, which challenges the identification of correspondences and a successful registration. In this paper, we propose a novel registration method for partially overlapping point clouds where correspondences are learned using an efficient point-wise feature encoder, and refined using a graph-based attention network. This attention network exploits geometrical relationships between key points to improve the matching in point clouds with low overlap. At inference time, the relative pose transformation is obtained by robustly fitting the correspondences through sample consensus. The evaluation is performed on the KITTI dataset and a novel synthetic dataset including low-overlapping point clouds with displacements of up to 30m. The proposed method achieves on-par performance with state-of-the-art methods on the KITTI dataset, and outperforms existing methods for low overlapping point clouds. Additionally, the proposed method achieves significantly faster inference times, as low as 410ms, between 5 and 35 times faster than competing methods. Our code and dataset are available at https://github.com/eduardohenriquearnold/fastreg.

【15】 Multi-Object Grasping -- Generating Efficient Robotic Picking and Transferring Policy 标题：多目标抓取--生成高效的机器人拾取和输送策略 链接：https://arxiv.org/abs/2112.09829

作者：Adheesh Shenoy,Tianze Chen,Yu Sun 机构： University of SouthFlorida 摘要：在存储箱之间传输多个对象是许多应用程序的常见任务。在机器人技术中，一种标准的方法是一次拾取一个物体并转移它。然而，抓取和拾取多个对象并同时将它们转移到一起更有效。本文提出了一套新的策略来有效地抓取一个箱子中的多个物体，并将它们转移到另一个箱子中。这些策略使机器人手能够识别最佳准备手配置（预抓取），并根据所需的抓取对象数量计算弯曲协同效应。本文还提出了一种方法，当所需数量大于单个抓取的能力时，使用马尔可夫决策过程（MDP）对拾取转移例程进行建模。使用MDP模型，所提出的方法可以生成一个最优的拾取传输例程，该例程可以最小化传输次数，表示效率。该方法已在仿真环境和真实机器人系统上进行了评估。结果表明，与最佳单目标拾取传输解决方案相比，该方法减少了59%的传输次数和58%的提升次数。 摘要：Transferring multiple objects between bins is a common task for many applications. In robotics, a standard approach is to pick up one object and transfer it at a time. However, grasping and picking up multiple objects and transferring them together at once is more efficient. This paper presents a set of novel strategies for efficiently grasping multiple objects in a bin to transfer them to another. The strategies enable a robotic hand to identify an optimal ready hand configuration (pre-grasp) and calculate a flexion synergy based on the desired quantity of objects to be grasped. This paper also presents an approach that uses the Markov decision process (MDP) to model the pick-transfer routines when the required quantity is larger than the capability of a single grasp. Using the MDP model, the proposed approach can generate an optimal pick-transfer routine that minimizes the number of transfers, representing efficiency. The proposed approach has been evaluated in both a simulation environment and on a real robotic system. The results show the approach reduces the number of transfers by 59% and the number of lifts by 58% compared to an optimal single object pick-transfer solution.

机器翻译，仅供参考