cs.RO机器人相关，共计17篇
【1】 Human Hands as Probes for Interactive Object Understanding 标题：人的手作为交互式物体理解的探针 链接：https://arxiv.org/abs/2112.09120
作者：Mohit Goyal,Sahil Modi,Rishabh Goyal,Saurabh Gupta 备注：Project website at this https URL 摘要：交互式对象理解，或者说我们可以对对象做什么，以及如何做，是计算机视觉的一个长期目标。在本文中，我们通过在以自我为中心的视频中观察人类的手来解决这个问题。我们证明了观察人的手与什么相互作用以及如何提供相关数据和必要的监督。注意手，容易定位和稳定活动对象进行学习，并揭示与对象发生交互的位置。通过分析手，我们可以了解我们可以对物体做什么，以及如何处理。我们将这些基本原则应用于EPIC-KITCHENS数据集，并通过观察以自我为中心的视频中的手，成功地学习了状态敏感特征和对象启示（交互区域和提供的抓握）。 摘要：Interactive object understanding, or what we can do to objects and how is a long-standing goal of computer vision. In this paper, we tackle this problem through observation of human hands in in-the-wild egocentric videos. We demonstrate that observation of what human hands interact with and how can provide both the relevant data and the necessary supervision. Attending to hands, readily localizes and stabilizes active objects for learning and reveals places where interactions with objects occur. Analyzing the hands shows what we can do to objects and how. We apply these basic principles on the EPIC-KITCHENS dataset, and successfully learn state-sensitive features, and object affordances (regions of interaction and afforded grasps), purely by observing hands in egocentric videos.
【2】 CrossLoc: Scalable Aerial Localization Assisted by Multimodal Synthetic Data 标题：CrossLoc：多模态合成数据辅助的可伸缩空中定位 链接：https://arxiv.org/abs/2112.09081
作者：Qi Yan,Jianhao Zheng,Simon Reding,Shanci Li,Iordan Doytchinov 备注：Preprint. Our code is available at this https URL 摘要：我们提出了一个视觉定位系统，学习估计摄像机姿态在现实世界中的帮助下，合成数据。尽管近年来取得了重大进展，但大多数基于学习的视觉定位方法都只针对单个领域，需要地理标记图像的密集数据库才能正常工作。为了缓解数据稀缺问题并提高神经定位模型的可伸缩性，我们引入了TOPO DataGen，这是一种多功能的合成数据生成工具，可在真实世界和虚拟世界之间平滑地进行遍历，它依赖于地理摄像机视点。提出了新的大规模模拟真实基准数据集，以展示和评估所述合成数据的效用。我们的实验表明，合成数据通常会提高神经网络在真实数据上的性能。此外，我们还介绍了CrossLoc，一种用于姿态估计的跨模态视觉表示学习方法，该方法通过自我监督充分利用场景坐标地面真实性。在没有任何额外数据的情况下，CrossLoc显著优于最先进的方法，并实现了更高的实际数据采样效率。我们的代码可在https://github.com/TOPO-EPFL/CrossLoc. 摘要：We present a visual localization system that learns to estimate camera poses in the real world with the help of synthetic data. Despite significant progress in recent years, most learning-based approaches to visual localization target at a single domain and require a dense database of geo-tagged images to function well. To mitigate the data scarcity issue and improve the scalability of the neural localization models, we introduce TOPO-DataGen, a versatile synthetic data generation tool that traverses smoothly between the real and virtual world, hinged on the geographic camera viewpoint. New large-scale sim-to-real benchmark datasets are proposed to showcase and evaluate the utility of the said synthetic data. Our experiments reveal that synthetic data generically enhances the neural network performance on real data. Furthermore, we introduce CrossLoc, a cross-modal visual representation learning approach to pose estimation that makes full use of the scene coordinate ground truth via self-supervision. Without any extra data, CrossLoc significantly outperforms the state-of-the-art methods and achieves substantially higher real-data sample efficiency. Our code is available at https://github.com/TOPO-EPFL/CrossLoc.
【3】 SenSnake: A snake robot with contact force sensing for studying locomotion in complex 3-D terrain 标题：SenSnake：一种用于研究复杂三维地形运动的接触力感知蛇形机器人 链接：https://arxiv.org/abs/2112.09078
作者：Divya Ramesh,Qiyuan Fu,Chen Li 摘要：尽管在各种环境中取得了进步，蛇机器人在穿越复杂的三维地形和巨大障碍物方面仍然远远落后于蛇。这是因为缺乏对如何控制三维身体弯曲以推动地形特征以产生和控制推进力的理解。生物学研究表明，多面手蛇使用接触力感应来实时调整身体弯曲。然而，由于缺乏关于蛇的力感应器官如何工作的基本知识，研究蛇的这种感觉调制力控制是一项挑战。在这里，我们采用机器人物理学的方法取得进展，首先开发一种蛇形机器人，能够通过接触力传感进行三维身体弯曲，从而实现系统的运动实验和力测量。通过两次开发和测试迭代，我们创建了一个12段机器人，其中36个压阻片传感器分布在所有段上，具有30 Hz采样频率的柔性壳体。该机器人在穿越大型障碍物时，采用高重复性的垂直弯曲测量接触力，实现了为系统实验提供平台的目标。最后，考虑到压阻式传感器的粘弹性行为，我们探索了基于模型的校准方法，这将有助于未来的研究。 摘要：Despite advances in a diversity of environments, snake robots are still far behind snakes in traversing complex 3-D terrain with large obstacles. This is due to a lack of understanding of how to control 3-D body bending to push against terrain features to generate and control propulsion. Biological studies suggested that generalist snakes use contact force sensing to adjust body bending in real time to do so. However, studying this sensory-modulated force control in snakes is challenging, due to a lack of basic knowledge of how their force sensing organs work. Here, we take a robophysics approach to make progress, starting by developing a snake robot capable of 3-D body bending with contact force sensing to enable systematic locomotion experiments and force measurements. Through two development and testing iterations, we created a 12-segment robot with 36 piezo-resistive sheet sensors distributed on all segments with compliant shells with a sampling frequency of 30 Hz. The robot measured contact forces while traversing a large obstacle using vertical bending with high repeatability, achieving the goal of providing a platform for systematic experiments. Finally, we explored model-based calibration considering the viscoelastic behavior of the piezo-resistive sensor, which will for useful for future studies.
【4】 A minimalistic stochastic dynamics model of cluttered obstacle traversal 标题：杂波穿越障碍物的最小随机动力学模型 链接：https://arxiv.org/abs/2112.09075
作者：Bokun Zheng,Qihan Xuan,Chen Li 摘要：机器人在穿越搜索和救援等重要应用所需的杂乱无章的大型障碍物方面仍然很差。相比之下，动物在这方面做得很好，通常与障碍物直接进行身体互动，而不是避开障碍物。在这里，为了理解凌乱障碍物穿越的动力学，我们开发了一个极简随机动力学模拟，这是受我们最近研究昆虫穿越草状梁的启发。二维模型系统由一个向前的自推进圆形运动器组成，该运动器在无摩擦水平面上以横向随机力进行平移，并与构成闸门的两个相邻水平梁相互作用。我们发现，穿越概率随推进力单调增加，但随随机力大小先增加后减小。对于具有不同刚度的不对称梁，横向更可能朝向刚度较低梁的一侧。这些观察结果与从势能景观方法中预期的结果一致。此外，我们将单个栅极扩展为晶格结构，形成一个大的杂乱障碍场。应用马尔可夫链蒙特卡罗方法，利用单门模拟得到的输入输出概率图，预测大视场中的穿越。该方法在预测障碍物场中物体最终位置的统计分布时获得了较高的精度，同时节省了10^5倍的计算时间。 摘要：Robots are still poor at traversing cluttered large obstacles required for important applications like search and rescue. By contrast, animals are excellent at doing so, often using direct physical interaction with obstacles rather than avoiding them. Here, towards understanding the dynamics of cluttered obstacle traversal, we developed a minimalistic stochastic dynamics simulation inspired by our recent study of insects traversing grass-like beams. The 2-D model system consists of a forward self-propelled circular locomotor translating on a frictionless level plane with a lateral random force and interacting with two adjacent horizontal beams that form a gate. We found that traversal probability increases monotonically with propulsive force, but first increases then decreases with random force magnitude. For asymmetric beams with different stiffness, traversal is more likely towards the side of the less stiff beam. These observations are in accord with those expected from a potential energy landscape approach. Furthermore, we extended the single gate in a lattice configuration to form a large cluttered obstacle field. A Markov chain Monte Carlo method was applied to predict traversal in the large field, using the input-output probability map obtained from single gate simulations. This method achieved high accuracy in predicting the statistical distribution of the final location of the body within the obstacle field, while saving computation time by a factor of 10^5.
【5】 Influence of Pedestrian Collision Warning Systems on Driver Behavior: A Driving Simulator Study 标题：行人碰撞预警系统对驾驶员驾驶行为影响的驾驶模拟器研究 链接：https://arxiv.org/abs/2112.09074
作者：Snehanshu Banerjee,Mansoureh Jeihani,Nashid K Khadem,Md. Muhib Kabir 备注：8 figures and 4 tables 摘要：随着连接和自动车辆（CAV）技术的出现，越来越需要在使用此类技术时评估驾驶员的行为。在这项首次研究中，在驾驶模拟器环境中引入了采用CAV技术的行人碰撞警告（PCW）系统，以评估驾驶员在行人横穿马路时的制动行为。本研究共招募了93名来自不同社会经济背景的参与者，为其创建了巴尔的摩市中心的虚拟网络。眼睛跟踪装置也被用来观察分心和头部运动。采用对数逻辑加速失效时间（AFT）分布模型进行分析，计算减速时间；从行人可见到达到最低速度允许行人通过的时间。PCW系统的存在显著影响了减速时间和减速率，因为它增加了前者，降低了后者，这证明了该系统通过大幅降低速度提供有效驾驶操作的有效性。进行了急动分析，以分析制动和加速的突然性。凝视分析表明，该系统能够吸引驾驶员的注意力，因为大多数驾驶员都注意到显示的警告。驾驶员对路线和连接车辆的熟悉减少了减速时间；性别也会产生重大影响，因为男性往往有更长的减速时间，即有更多的时间舒适地刹车并让行人通过。 摘要：With the advent of connected and automated vehicle (CAV) technology, there is an increasing need to evaluate driver behavior while using such technology. In this first of a kind study, a pedestrian collision warning (PCW) system using CAV technology, was introduced in a driving simulator environment, to evaluate driver braking behavior, in the presence of a jaywalking pedestrian. A total of 93 participants from diverse socio-economic backgrounds were recruited for this study, for which a virtual network of downtown Baltimore was created. An eye tracking device was also used to observe distractions and head movements. A Log logistic accelerated failure time (AFT) distribution model was used for this analysis, to calculate speed reduction times; time from the moment the pedestrian becomes visible, to the point where a minimum speed was reached, to allow the pedestrian to pass. The presence of the PCW system significantly impacted the speed reduction time and deceleration rate, as it increased the former and reduced the latter, which proves the effectiveness of this system in providing an effective driving maneuver, by drastically reducing speed. A jerk analysis is conducted to analyze the suddenness of braking and acceleration. Gaze analysis showed that the system was able to attract the attention of the drivers, as the majority of the drivers noticed the displayed warning. The familiarity of the driver with the route and connected vehicles reduces the speed reduction time; gender also can have a significant impact as males tend to have longer speed reduction time, i.e. more time to comfortably brake and allow the pedestrian to pass.
【6】 Centralizing State-Values in Dueling Networks for Multi-Robot Reinforcement Learning Mapless Navigation 标题：多机器人强化学习无人导航的决斗网络状态值集中 链接：https://arxiv.org/abs/2112.09012
作者：Enrico Marchesini,Alessandro Farinelli 备注：6 pages, 5 figures, 1 table. Accepted at IROS 2021 摘要：我们研究了流行的集中训练和分散执行（CTDE）模式下的多机器人mapless导航问题。当每个机器人考虑其路径而不与其他机器人明确共享观测值时，该问题具有挑战性，并可能导致深度强化学习（DRL）中的非平稳问题。典型的CTDE算法将联合行动价值函数分解为单独的行动价值函数，以利于合作并实现分散执行。这种因式分解涉及限制个体中出现新行为的约束（例如，单调性），因为每个代理都是从联合动作值开始训练的。相比之下，我们提出了一种新的CTDE体系结构，该体系结构使用集中式状态值网络来计算联合状态值，用于在基于值的代理更新中注入全局状态信息。因此，考虑到环境的整体状态，每个模型计算其权重的梯度更新。我们的想法遵循了决斗网络的观点，因为对关节状态值的单独估计既有提高样本效率的优势，又能为每个机器人提供全局状态是否有价值的信息。在2个、4个和8个机器人的机器人导航任务中进行的实验，证实了我们的方法比以前的CTDE方法（例如VDN、QMIX）具有更高的性能。 摘要：We study the problem of multi-robot mapless navigation in the popular Centralized Training and Decentralized Execution (CTDE) paradigm. This problem is challenging when each robot considers its path without explicitly sharing observations with other robots and can lead to non-stationary issues in Deep Reinforcement Learning (DRL). The typical CTDE algorithm factorizes the joint action-value function into individual ones, to favor cooperation and achieve decentralized execution. Such factorization involves constraints (e.g., monotonicity) that limit the emergence of novel behaviors in an individual as each agent is trained starting from a joint action-value. In contrast, we propose a novel architecture for CTDE that uses a centralized state-value network to compute a joint state-value, which is used to inject global state information in the value-based updates of the agents. Consequently, each model computes its gradient update for the weights, considering the overall state of the environment. Our idea follows the insights of Dueling Networks as a separate estimation of the joint state-value has both the advantage of improving sample efficiency, while providing each robot information whether the global state is (or is not) valuable. Experiments in a robotic navigation task with 2 4, and 8 robots, confirm the superior performance of our approach over prior CTDE methods (e.g., VDN, QMIX).
【7】 End-to-End Multi-Task Deep Learning and Model Based Control Algorithm for Autonomous Driving 标题：端到端多任务深度学习和基于模型的自主驾驶控制算法 链接：https://arxiv.org/abs/2112.08967
作者：Der-Hau Lee,Jinn-Liang Liu 备注：10 pages, 7 figures 摘要：采用深度学习神经网络（DNN）的端到端驾驶已成为工业界和学术界快速发展的自主驾驶范例。然而，安全措施和可解释性仍然对这种模式构成挑战。我们提出了一种端到端驱动算法，该算法将多任务DNN、路径预测和控制模型集成在一条数据流管道中，数据流从传感器设备通过这些模型传输到驱动决策。它提供了定量的措施来评估端到端驱动系统的整体、动态和实时性能，从而可以量化其安全性和可解释性。DNN是一种改进的UNet，它是一种著名的语义分段编码-解码神经网络。它包括一个分割、一个回归和两个车道分割、路径预测和车辆控制的分类任务。我们提出了具有不同复杂性的改进的UNet体系结构的三种变体，在单任务和多任务（MT）体系结构的四种静态度量中对它们在不同任务上进行比较，然后在实时仿真中通过两种额外的动态度量来确定最佳的一种。我们还提出了一种基于学习和模型的纵向控制器，采用模型预测控制方法。使用Stanley横向控制器，我们的结果表明，MTUNet在正常速度下弯曲道路上的曲率和横向偏移估计方面优于早期修改的UNet，这已在真实道路上驾驶的真实汽车上进行了测试。 摘要：End-to-end driving with a deep learning neural network (DNN) has become a rapidly growing paradigm of autonomous driving in industry and academia. Yet safety measures and interpretability still pose challenges to this paradigm. We propose an end-to-end driving algorithm that integrates multi-task DNN, path prediction, and control models in a pipeline of data flow from sensory devices through these models to driving decisions. It provides quantitative measures to evaluate the holistic, dynamic, and real-time performance of end-to-end driving systems, and thus allows to quantify their safety and interpretability. The DNN is a modified UNet, a well known encoder-decoder neural network of semantic segmentation. It consists of one segmentation, one regression, and two classification tasks for lane segmentation, path prediction, and vehicle controls. We present three variants of the modified UNet architecture having different complexities, compare them on different tasks in four static measures for both single and multi-task (MT) architectures, and then identify the best one by two additional dynamic measures in real-time simulation. We also propose a learning- and model-based longitudinal controller using model predictive control method. With the Stanley lateral controller, our results show that MTUNet outperforms an earlier modified UNet in terms of curvature and lateral offset estimation on curvy roads at normal speed, which has been tested in a real car driving on real roads.
【8】 Autonomous Driving in Adverse Weather Conditions: A Survey 标题：恶劣天气条件下的自动驾驶研究综述 链接：https://arxiv.org/abs/2112.08936
作者：Yuxiao Zhang,Alexander Carballo,Hanting Yang,Kazuya Takeda 摘要：自动驾驶系统（ADS）为汽车行业开辟了一个新领域，并为未来的交通提供了更高的效率和舒适的体验。然而，恶劣天气条件下的自动驾驶一直是阻碍自动驾驶车辆（AVs）进入4级或更高自主性的问题。本文以分析和统计的方式评估了天气给ADS传感器带来的影响和挑战，并调查了恶劣天气条件下的解决方案。关于每种天气感知增强的最新技术都有详尽的报道。外部辅助解决方案，如V2X技术、当前可用数据集中的天气条件覆盖、模拟器以及带有气象室的实验设施，都被清晰地分类。通过指出自动驾驶领域目前面临的各种主要天气问题，并回顾近年来的硬件和计算机科学解决方案，本次调查有助于全面概述不利天气驾驶条件下ADS发展的障碍和方向。 摘要：Automated Driving Systems (ADS) open up a new domain for the automotive industry and offer new possibilities for future transportation with higher efficiency and comfortable experiences. However, autonomous driving under adverse weather conditions has been the problem that keeps autonomous vehicles (AVs) from going to level 4 or higher autonomy for a long time. This paper assesses the influences and challenges that weather brings to ADS sensors in an analytic and statistical way, and surveys the solutions against inclement weather conditions. State-of-the-art techniques on perception enhancement with regard to each kind of weather are thoroughly reported. External auxiliary solutions like V2X technology, weather conditions coverage in currently available datasets, simulators, and experimental facilities with weather chambers are distinctly sorted out. By pointing out all kinds of major weather problems the autonomous driving field is currently facing, and reviewing both hardware and computer science solutions in recent years, this survey contributes a holistic overview on the obstacles and directions of ADS development in terms of adverse weather driving conditions.
【9】 Learning from Guided Play: A Scheduled Hierarchical Approach for Improving Exploration in Adversarial Imitation Learning 标题：从引导性游戏中学习：改进对抗性模仿学习探索性的一种有计划的分层方法 链接：https://arxiv.org/abs/2112.08932
作者：Trevor Ablett,Bryan Chan,Jonathan Kelly 备注：Accepted at the Neurips 2021 Deep Reinforcement Learning Workshop, Sydney, Australia 摘要：有效的探索仍然是一个重大的挑战，它阻碍了许多物理系统中强化学习的部署。对于具有连续和高维状态和动作空间的系统，如机器人，尤其如此。这种挑战在稀疏奖励设置中更加突出，在这种设置中，密集奖励设计所需的低级状态信息不可用。对抗性模仿学习（AIL）可以通过利用专家生成的最佳行为演示，并从本质上替代密集的奖励信息，部分克服这一障碍。不幸的是，专家演示的可用性并不一定能提高代理有效探索的能力，正如我们的经验所表明的那样，这可能导致学习效率低下或停滞。我们介绍了引导式游戏学习（LfGP），这是一个框架，在该框架中，除了一个主要任务外，我们还利用专家演示多个辅助任务。随后，使用分层模型通过修改的AIL过程学习每个任务奖励和策略，其中通过将不同任务组合在一起的调度器执行对所有任务的探索。这提供了许多好处：对于具有挑战性瓶颈转换的主要任务，学习效率得到了提高，专家数据在任务之间变得可重用，并且通过重用已学习的辅助任务模型实现转移学习成为可能。我们在一个具有挑战性的多任务机器人操作领域的实验结果表明，我们的方法优于有监督的模仿学习和最先进的AIL方法。代码可在https://github.com/utiasSTARS/lfgp. 摘要：Effective exploration continues to be a significant challenge that prevents the deployment of reinforcement learning for many physical systems. This is particularly true for systems with continuous and high-dimensional state and action spaces, such as robotic manipulators. The challenge is accentuated in the sparse rewards setting, where the low-level state information required for the design of dense rewards is unavailable. Adversarial imitation learning (AIL) can partially overcome this barrier by leveraging expert-generated demonstrations of optimal behaviour and providing, essentially, a replacement for dense reward information. Unfortunately, the availability of expert demonstrations does not necessarily improve an agent's capability to explore effectively and, as we empirically show, can lead to inefficient or stagnated learning. We present Learning from Guided Play (LfGP), a framework in which we leverage expert demonstrations of, in addition to a main task, multiple auxiliary tasks. Subsequently, a hierarchical model is used to learn each task reward and policy through a modified AIL procedure, in which exploration of all tasks is enforced via a scheduler composing different tasks together. This affords many benefits: learning efficiency is improved for main tasks with challenging bottleneck transitions, expert data becomes reusable between tasks, and transfer learning through the reuse of learned auxiliary task models becomes possible. Our experimental results in a challenging multitask robotic manipulation domain indicate that our method compares favourably to supervised imitation learning and to a state-of-the-art AIL method. Code is available at https://github.com/utiasSTARS/lfgp.
【10】 Multi-Camera LiDAR Inertial Extension to the Newer College Dataset 标题：多相机LiDAR惯性扩展到较新的学院数据集 链接：https://arxiv.org/abs/2112.08854
作者：Lintong Zhang,Marco Camurri,Maurice Fallon 摘要：在本文中，我们提出了一个4.5km步行距离的多相机激光雷达惯性数据集，作为对较新的大学数据集的扩展。全局快门多摄像头设备与IMU和激光雷达硬件同步。该数据集还提供了六个自由度（DoF）地面真实姿态，激光雷达频率为10hz。描述了三个数据收集，并举例说明了多摄像机视觉惯性里程计的使用。该扩展数据集包含小型和狭窄通道、大型开放空间以及植被覆盖区域，用于测试定位和绘图系统。此外，一些序列呈现出挑战性的情况，例如突然的灯光变化、无纹理的表面和攻击性的运动。该数据集可从以下网址获得：https://ori-drs.github.io/newer-college-dataset 摘要：In this paper, we present a multi-camera LiDAR inertial dataset of 4.5km walking distance as an expansion to the Newer College Dataset. The global shutter multi-camera device is hardware synchronized with the IMU and the LiDAR. This dataset also provides six Degrees of Freedom (DoF) ground truth poses, at the LiDAR frequency of 10hz. Three data collections are described and example usage of multi-camera visual-inertial odometry is demonstrated. This expansion dataset contains small and narrow passages, large scale open spaces as well as vegetated areas to test localization and mapping systems. Furthermore, some sequences present challenging situations such as abrupt lighting change, textureless surfaces, and aggressive motion. The dataset is available at: https://ori-drs.github.io/newer-college-dataset
【11】 Reprogrammable Surfaces Through Star Graph Metamaterials 标题：通过星图超材料实现表面可重编程 链接：https://arxiv.org/abs/2112.08597
作者：Sawyer Thomas,Jeffrey Lipton 摘要：改变表面轮廓的能力使生物系统能够有效地操纵并融入周围环境。当前的曲面变形技术要么依赖于少量的固定状态，要么依赖于直接驱动整个系统。我们发现了一部分与尺度无关的auxetic超材料具有星图结构的状态轨迹。在中心节点，小的轻推可以在轨迹之间移动材质，允许我们局部移动泊松比，导致材质在加载下呈现不同的形状。虽然可能形状的数量随着材料的大小呈指数增长，但随机找到一个形状的概率却非常小。通过主动引导材料通过节点点，我们生成了一个可重新编程的曲面，该曲面不需要输入来保持形状，并且可以显示任意二维信息并呈现复杂的三维形状。我们的工作为微型设备、触觉显示器、制造业和机器人系统带来了新的机遇。 摘要：The ability to change a surface's profile allows biological systems to effectively manipulate and blend into their surroundings. Current surface morphing techniques rely either on having a small number of fixed states or on directly driving the entire system. We discovered a subset of scale-independent auxetic metamaterials have a state trajectory with a star-graph structure. At the central node, small nudges can move the material between trajectories, allowing us to locally shift Poisson's ratio, causing the material to take on different shapes under loading. While the number of possible shapes grows exponentially with the size of the material, the probability of finding one at random is vanishingly small. By actively guiding the material through the node points, we produce a reprogrammable surface that does not require inputs to maintain shape and can display arbitrary 2D information and take on complex 3D shapes. Our work opens new opportunities in micro devices, tactile displays, manufacturing, and robotic systems.
【12】 Rail Vehicle Localization and Mapping with LiDAR-Vision-Inertial-GNSS Fusion 标题：基于LiDAR-VISION-INTIAL-GNSS融合的轨道车辆定位与测绘 链接：https://arxiv.org/abs/2112.08563
作者：Yusheng Wang,Weiwei Song,Yidong Lou,Yi Zhang,Fei Huang,Zhiyong Tu,Qiangsheng Liang 备注：arXiv admin note: substantial text overlap with arXiv:2111.15043 摘要：在本文中，我们提出了一种全球导航卫星系统（GNSS）辅助的激光雷达视觉惯性方案RailLoMer-V，用于精确和稳健的轨道车辆定位和测绘。RailLoMer-V在因子图上表示，由两个子系统组成：里程表辅助激光雷达惯性系统（OLIS）和里程表集成视觉惯性系统（OVIS）。这两个子系统都利用了铁路上的典型几何结构。提取的轨道的平面约束用于补充OLIS中的旋转和垂直误差。此外，利用线特征和消失点约束OVIS中的旋转漂移。在800公里以上的数据集上对提议的框架进行了广泛的评估，这些数据集在普通高速铁路和高速铁路上收集了一年多，日夜不间断。利用来自单个传感器的所有测量数据的紧密耦合集成，我们的框架在任务期间可以精确到很长时间，并且足够健壮，可以处理严重退化的场景（铁路隧道）。此外，可通过车载计算机实现实时性能。 摘要：In this paper, we present a global navigation satellite system (GNSS) aided LiDAR-visual-inertial scheme, RailLoMer-V, for accurate and robust rail vehicle localization and mapping. RailLoMer-V is formulated atop a factor graph and consists of two subsystems: an odometer assisted LiDAR-inertial system (OLIS) and an odometer integrated Visual-inertial system (OVIS). Both the subsystem exploits the typical geometry structure on the railroads. The plane constraints from extracted rail tracks are used to complement the rotation and vertical errors in OLIS. Besides, the line features and vanishing points are leveraged to constrain rotation drifts in OVIS. The proposed framework is extensively evaluated on datasets over 800 km, gathered for more than a year on both general-speed and high-speed railways, day and night. Taking advantage of the tightly-coupled integration of all measurements from individual sensors, our framework is accurate to long-during tasks and robust enough to grievously degenerated scenarios (railway tunnels). In addition, the real-time performance can be achieved with an onboard computer.
【13】 Integrated Guidance and Control for Lunar Landing using a Stabilized Seeker 标题：稳定导引头用于月球着陆的综合制导与控制 链接：https://arxiv.org/abs/2112.08540
作者：Brian Gaudet,Roberto Furfaro 备注：Accepted for 2022 AIAA Scitech GN&C. arXiv admin note: text overlap with arXiv:2107.14764, arXiv:2004.09978, arXiv:2110.00634, arXiv:2109.03880 摘要：我们开发了一个综合制导和控制系统，该系统与稳定导引头和着陆点探测软件相结合，可以实现精确和安全的行星着陆。导引头通过调整导引头仰角和方位角来跟踪指定的着陆点，使指定的着陆点位于传感器视野的中心。导引头角度、接近速度和到指定着陆点的距离用于形成速度场，制导和控制系统使用该速度场在指定着陆点实现安全着陆。导航和控制系统将速度场、姿态和旋转速度直接映射到着陆器四个发动机的指令推力矢量。制导和控制系统作为一种策略，使用强化元学习进行优化。我们证明了制导和控制系统在动力下降阶段与多个转向兼容，并且对导引头滞后、执行器滞后和退化以及由燃油消耗引起的质心变化具有鲁棒性。我们概述了几种作战概念，包括使用预先放置的着陆信标的方法。 摘要：We develop an integrated guidance and control system that in conjunction with a stabilized seeker and landing site detection software can achieve precise and safe planetary landing. The seeker tracks the designated landing site by adjusting seeker elevation and azimuth angles to center the designated landing site in the sensor field of view. The seeker angles, closing speed, and range to the designated landing site are used to formulate a velocity field that is used by the guidance and control system to achieve a safe landing at the designated landing site. The guidance and control system maps this velocity field, attitude, and rotational velocity directly to a commanded thrust vector for the lander's four engines. The guidance and control system is implemented as a policy optimized using reinforcement meta learning. We demonstrate that the guidance and control system is compatible with multiple diverts during the powered descent phase, and is robust to seeker lag, actuator lag and degradation, and center of mass variation induced by fuel consumption. We outline several concepts of operations, including an approach using a preplaced landing beacon.
【14】 Invariance Through Inference 标题：推论不变性 链接：https://arxiv.org/abs/2112.08526
作者：Takuma Yoneda,Ge Yang,Matthew R. Walter,Bradly Stadie 备注：In submission to ICLR2022. Here's our project page: this https URL 摘要：我们介绍了一种称为推理不变性的通用方法，用于在感知变化未知的部署环境中提高代理的测试时性能。与通过插值产生不变的视觉特征不同，通过推理产生的不变性将部署时的自适应转化为无监督学习问题。这在实践中是通过部署一个简单的算法来实现的，该算法尝试将潜在特征的分布与代理的先前经验相匹配，而不依赖于成对的数据。虽然很简单，但我们表明，这一想法可以在各种适应场景中带来令人惊讶的改进，而无需获得部署时间奖励，包括改变相机姿势和照明条件。结果显示在具有挑战性的干扰物控制套件上，这是一个基于图像观察的机器人环境。 摘要：We introduce a general approach, called Invariance through Inference, for improving the test-time performance of an agent in deployment environments with unknown perceptual variations. Instead of producing invariant visual features through interpolation, invariance through inference turns adaptation at deployment-time into an unsupervised learning problem. This is achieved in practice by deploying a straightforward algorithm that tries to match the distribution of latent features to the agent's prior experience, without relying on paired data. Although simple, we show that this idea leads to surprising improvements on a variety of adaptation scenarios without access to deployment-time rewards, including changes in camera poses and lighting conditions. Results are presented on challenging distractor control suite, a robotics environment with image-based observations.
【15】 Safety-Aware Preference-Based Learning for Safety-Critical Control 标题：基于安全意识偏好的安全关键控制学习 链接：https://arxiv.org/abs/2112.08516
作者：Ryan K. Cosner,Maegan Tucker,Andrew J. Taylor,Kejun Li,Tamás G. Molnár,Wyatt Ubellacker,Anil Alan,Gábor Orosz,Yisong Yue,Aaron D. Ames 摘要：将动态机器人引入野外需要在性能和安全之间取得微妙的平衡。然而，设计用于提供稳健安全保证的控制器通常会导致保守行为，调整这些控制器以找到性能和安全之间的理想平衡通常需要领域专家或精心构建的奖励函数。这项工作提出了一种设计范式，通过集成安全感知偏好学习（PBL）和控制屏障功能（CBF），系统地实现平衡性能和鲁棒安全的行为。融合这些概念——安全意识学习和安全关键控制——提供了一种在实践中实现复杂机器人系统安全行为的强大方法。我们通过仿真和硬件实验证明了这种设计模式能够实现四足机器人基于感知的安全和高性能自主操作。 摘要：Bringing dynamic robots into the wild requires a tenuous balance between performance and safety. Yet controllers designed to provide robust safety guarantees often result in conservative behavior, and tuning these controllers to find the ideal trade-off between performance and safety typically requires domain expertise or a carefully constructed reward function. This work presents a design paradigm for systematically achieving behaviors that balance performance and robust safety by integrating safety-aware Preference-Based Learning (PBL) with Control Barrier Functions (CBFs). Fusing these concepts -- safety-aware learning and safety-critical control -- gives a robust means to achieve safe behaviors on complex robotic systems in practice. We demonstrate the capability of this design paradigm to achieve safe and performant perception-based autonomous operation of a quadrupedal robot both in simulation and experimentally on hardware.
【16】 Safety-Critical Control with Input Delay in Dynamic Environment 标题：动态环境中具有输入时滞的安全临界控制 链接：https://arxiv.org/abs/2112.08445
作者：Tamas G. Molnar,Adam K. Kiss,Aaron D. Ames,Gábor Orosz 备注：Submitted to the IEEE Transactions on Control Systems Technology (TCST). 14 pages, 7 figures 摘要：赋予非线性系统安全行为在现代控制中越来越重要。对于必须在动态变化环境中安全运行的实际控制系统来说，这项任务尤其具有挑战性。本文通过建立环境控制屏障功能（ECBFs）的概念，建立了动态环境中安全关键控制的框架。通过考虑系统延迟响应期间环境的演变，该框架能够保证即使在存在输入延迟的情况下也能安全。基础控制综合依赖于预测系统和环境在延迟间隔内的未来状态，并具有针对预测错误的鲁棒安全保证。通过一个简单的自适应巡航控制问题和一个更复杂的机器人在赛格威平台上的应用，证明了该方法的有效性。 摘要：Endowing nonlinear systems with safe behavior is increasingly important in modern control. This task is particularly challenging for real-life control systems that must operate safely in dynamically changing environments. This paper develops a framework for safety-critical control in dynamic environments, by establishing the notion of environmental control barrier functions (ECBFs). The framework is able to guarantee safety even in the presence of input delay, by accounting for the evolution of the environment during the delayed response of the system. The underlying control synthesis relies on predicting the future state of the system and the environment over the delay interval, with robust safety guarantees against prediction errors. The efficacy of the proposed method is demonstrated by a simple adaptive cruise control problem and a more complex robotics application on a Segway platform.
【17】 Contact simulation of a 2D Bipedal Robot kicking a ball 标题：二维两足机器人踢球的接触仿真 链接：https://arxiv.org/abs/2112.08426
作者：Alphonsus Adu-Bredu 摘要：本报告描述了一种模拟主动控制系统多体接触的方法。在这项工作中，我们重点研究了二维两足机器人踢圆球的控制和接触仿真。 摘要：This report describes an approach for simulating multi-body contacts of actively-controlled systems. In this work, we focus on the controls and contact simulation of a 2-dimensional bipedal robot kicking a circular ball.
