机器人相关学术速递[12.6]

cs.RO机器人相关，共计14篇

【1】 Coupling Vision and Proprioception for Navigation of Legged Robots 标题：腿式机器人导航中的视觉与视觉耦合 链接：https://arxiv.org/abs/2112.02094

作者：Zipeng Fu,Ashish Kumar,Ananye Agarwal,Haozhi Qi,Jitendra Malik,Deepak Pathak 备注：Website and videos at this https URL 摘要：我们利用视觉和本体感觉的互补优势，在腿部机器人中实现点目标导航。腿式系统比轮式机器人能够穿越更复杂的地形，但为了充分利用这一能力，我们需要导航系统中的高级路径规划人员了解低级别移动策略在不同地形上的行走能力。我们通过使用本体感知反馈来估计行走策略的安全操作极限，并感知意外障碍物和地形特性，如视觉可能错过的地面平滑度或柔软度，从而实现这一目标。导航系统使用车载摄像头生成入住地图和相应的成本地图，以实现目标。然后，FMM（快速行进法）规划器生成目标路径。速度命令生成器将此作为输入，使用来自安全顾问的意外障碍物和地形确定速度限制的附加约束作为输入，为移动策略生成所需速度。与轮式机器人（LoCoBot）基线和其他具有不相交的高层规划和底层控制的基线相比，我们显示出了优越的性能。我们还展示了我们的系统在四足机器人上的实际部署，该机器人带有机载传感器和计算机。视频在https://navigation-locomotion.github.io/camera-ready 摘要：We exploit the complementary strengths of vision and proprioception to achieve point goal navigation in a legged robot. Legged systems are capable of traversing more complex terrain than wheeled robots, but to fully exploit this capability, we need the high-level path planner in the navigation system to be aware of the walking capabilities of the low-level locomotion policy on varying terrains. We achieve this by using proprioceptive feedback to estimate the safe operating limits of the walking policy, and to sense unexpected obstacles and terrain properties like smoothness or softness of the ground that may be missed by vision. The navigation system uses onboard cameras to generate an occupancy map and a corresponding cost map to reach the goal. The FMM (Fast Marching Method) planner then generates a target path. The velocity command generator takes this as input to generate the desired velocity for the locomotion policy using as input additional constraints, from the safety advisor, of unexpected obstacles and terrain determined speed limits. We show superior performance compared to wheeled robot (LoCoBot) baselines, and other baselines which have disjoint high-level planning and low-level control. We also show the real-world deployment of our system on a quadruped robot with onboard sensors and compute. Videos at https://navigation-locomotion.github.io/camera-ready

【2】 Causal-based Time Series Domain Generalization for Vehicle Intention Prediction 标题：基于因果关系的汽车意向预测时域泛化 链接：https://arxiv.org/abs/2112.02093

作者：Yeping Hu,Xiaogang Jia,Masayoshi Tomizuka,Wei Zhan 备注：Accepted by NeurIPS 2021 Workshop on Distribution Shifts 摘要：准确预测交通参与者可能的行为是自动驾驶车辆的基本能力。由于自动驾驶车辆需要在动态变化的环境中导航，因此无论在何处以及遇到什么样的驾驶环境，都需要进行准确的预测。因此，当自动驾驶车辆部署在现实世界中时，对未知领域的泛化能力对于预测模型至关重要。本文针对车辆意图预测任务的领域泛化问题，提出了一种基于因果关系的时间序列领域泛化（CTSDG）模型。我们构建了一个车辆意图预测任务的结构因果模型，以学习输入驾驶数据的不变表示，用于领域泛化。我们进一步将递归潜在变量模型集成到我们的结构因果模型中，以更好地从时间序列输入数据中捕获时间潜在依赖性。我们的方法的有效性通过真实驾驶数据进行评估。我们证明，与其他最先进的领域泛化和行为预测方法相比，我们提出的方法在预测精度上有一致的提高。 摘要：Accurately predicting possible behaviors of traffic participants is an essential capability for autonomous vehicles. Since autonomous vehicles need to navigate in dynamically changing environments, they are expected to make accurate predictions regardless of where they are and what driving circumstances they encountered. Therefore, generalization capability to unseen domains is crucial for prediction models when autonomous vehicles are deployed in the real world. In this paper, we aim to address the domain generalization problem for vehicle intention prediction tasks and a causal-based time series domain generalization (CTSDG) model is proposed. We construct a structural causal model for vehicle intention prediction tasks to learn an invariant representation of input driving data for domain generalization. We further integrate a recurrent latent variable model into our structural causal model to better capture temporal latent dependencies from time-series input data. The effectiveness of our approach is evaluated via real-world driving data. We demonstrate that our proposed method has consistent improvement on prediction accuracy compared to other state-of-the-art domain generalization and behavior prediction methods.

【3】 Snake Robot Gait Decomposition and Gait Parameter Optimization 标题：蛇形机器人步态分解与步态参数优化 链接：https://arxiv.org/abs/2112.02057

作者：Bongsub Song,Insung Ju,Dongwon Yun 摘要：本文提出了步态分解（G.D）和步态参数梯度（GPG）两种方法，前者是对蛇形运动进行数学分解的方法，后者是对分解后的步态参数进行优化的方法。G.D是一种从曲线函数生成运动到生成蛇形机器人运动时的电机控制指令的数学简洁表达蛇形步态的方法。通过该方法，可以直观地将蛇形机器人的步态分类为一个矩阵，并灵活地调整步态生成所需的曲线函数参数。这就解决了蛇形机器人难以实际应用的参数整定问题。因此，如果将该G.D应用于蛇形机器人，只需几个参数就可以生成各种步态，因此蛇形机器人可以应用于许多领域。我们还实现了GPG算法来优化步态曲线函数，并通过G.D.定义了蛇形机器人的步态。 摘要：This paper proposes Gait Decomposition (G.D), a method of mathematically decomposing snake movements, and Gait Parameter Gradient (GPG), a method of optimizing decomposed gait parameters. G.D is a method that can express the snake gait mathematically and concisely from generating movement using the curve function to the motor control order when generating movement of snake robot. Through this method, the gait of the snake robot can be intuitively classified into a matrix, as well as flexibly adjusting the parameters of the curve function required for gait generation. This can solve the problem that parameter tuning, which is the reason why it is difficult for a snake robot to practical use, is difficult. Therefore, if this G.D is applied to snake robots, various gaits can be generated with a few of parameters, so snake robots can be used in many fields. We also implemented the GPG algorithm to optimize the gait curve function as well as define the gait of the snake robot through G.D.

【4】 Dynamic Bayesian Network Modelling of User Affect and Perceptions of a Teleoperated Robot Coach during Longitudinal Mindfulness Training 标题：纵向注意力训练期间遥操作机器人教练用户情感和感知的动态贝叶斯网络建模 链接：https://arxiv.org/abs/2112.02017

作者：Indu P. Bodala,Hatice Gunes 摘要：与社交辅助机器人的纵向互动研究对于确保机器人与长期使用相关且其感知不易产生新奇效应至关重要。在本文中，我们提出了一个动态贝叶斯网络（DBN）来捕捉参与者与遥操作机器人教练（RC）进行正念会话的纵向交互。DBN模型用于研究参与者自我报告的个性特征、每周基线幸福感得分、治疗评分以及在为期5周的纵向研究中诱发的面部AUs之间复杂的时间交互作用。DBN建模涉及学习一种图形表示法，该图形表示法有助于直观理解多个组成部分如何影响与RC感知、参与者放松和平静水平相对应的会话评分的纵向变化。所学模型涵盖了纵向互动研究的以下课内和课间方面：5个人格维度对面部非接触状态和课时评分的影响，面部非接触状态对课时评分的影响，以及课时评分项目内的影响。使用前3个时间点学习DBN结构，获得的模型用于预测5周纵向数据最后2个时间点的会话评级。使用受试者的RMSE和R2分数对预测进行量化。我们还展示了该模型的两个应用，即数据集中缺失值的插补和具有给定人格特征的新参与者的纵向会话评分的估计。因此，获得的DBN模型有助于学习纵向数据中变量之间的条件依赖结构，并提供其他回归方法无法提供的推论和概念理解。 摘要：Longitudinal interaction studies with Socially Assistive Robots are crucial to ensure that the robot is relevant for long-term use and its perceptions are not prone to the novelty effect. In this paper, we present a dynamic Bayesian network (DBN) to capture the longitudinal interactions participants had with a teleoperated robot coach (RC) delivering mindfulness sessions. The DBN model is used to study complex, temporal interactions between the participants self-reported personality traits, weekly baseline wellbeing scores, session ratings, and facial AUs elicited during the sessions in a 5-week longitudinal study. DBN modelling involves learning a graphical representation that facilitates intuitive understanding of how multiple components contribute to the longitudinal changes in session ratings corresponding to the perceptions of the RC, and participants relaxation and calm levels. The learnt model captures the following within and between sessions aspects of the longitudinal interaction study: influence of the 5 personality dimensions on the facial AU states and the session ratings, influence of facial AU states on the session ratings, and the influences within the items of the session ratings. The DBN structure is learnt using first 3 time points and the obtained model is used to predict the session ratings of the last 2 time points of the 5-week longitudinal data. The predictions are quantified using subject-wise RMSE and R2 scores. We also demonstrate two applications of the model, namely, imputation of missing values in the dataset and estimation of longitudinal session ratings of a new participant with a given personality profile. The obtained DBN model thus facilitates learning of conditional dependency structure between variables in the longitudinal data and offers inferences and conceptual understanding which are not possible through other regression methodologies.

【5】 Improving the Robustness of Reinforcement Learning Policies with \mathcal{L}_{1} Adaptive Control链接：https://arxiv.org/abs/2112.01953

作者：Y. Cheng,P. Zhao,D. J. Block,N. Hovakimyan 备注：arXiv admin note: substantial text overlap with arXiv:2106.02249 摘要：由于存在动态变化，在标称环境中训练的强化学习（RL）控制策略在新环境/扰动环境中可能会失败。对于具有连续状态空间和动作空间的控制系统，我们提出了一种通过$\mathcal{L}{1}$自适应控制器（$\mathcal{L}{1}$AC）对预先训练的RLpolicy进行鲁棒化的附加方法。利用$\mathcal{L}{1}$AC快速估计和主动补偿动态变化的能力，所提出的方法可以提高RL策略的鲁棒性，该策略在模拟器或真实世界中进行训练，而不考虑广泛的动态变化。数值和真实世界的实验证明了所提出的方法在使用无模型和基于模型的方法训练的RL策略的鲁棒性方面的有效性。一个视频的实验在一个真正的潘杜博特设置是可用的leathttps://youtu.be/xgOB9vpyUgE. 摘要：A reinforcement learning (RL) control policy trained in a nominal environment could fail in a new/perturbed environment due to the existence of dynamic variations. For controlling systems with continuous state and action spaces, we propose an add-on approach to robustifying a pre-trained RLpolicy by augmenting it with an $\mathcal{L}_{1}$ adaptive controller ($ \mathcal{L}_{1}$AC). Leveraging the capability of an $\mathcal{L}_{1}$AC for fast estimation and active compensation of dynamic variations, the proposed approach can improve the robustness of an RL policy which is trained either in a simulator or in the real world without consideration of a broad class of dynamic variations. Numerical and real-world experiments empirically demonstrate the efficacy of the proposed approach in robustifying RL policies trained using both model-free and model-based methods. A video for the experiments on a real Pendubot setup is availableathttps://youtu.be/xgOB9vpyUgE.

【6】 A Survey of Robot Manipulation in Contact 标题：机器人接触操作研究综述 链接：https://arxiv.org/abs/2112.01942

作者：Markku Suomalainen,Yiannis Karayiannidis,Ville Kyrki 备注：Submitted to Robotics and Autonomous Systems 摘要：在这项调查中，我们介绍了机器人执行需要与环境进行不同接触的操作任务的现状，因此机器人必须隐式或显式地控制与环境的接触力才能完成任务。机器人可以执行越来越多的仍由人类完成的操纵任务，关于以下主题的出版物越来越多：1）执行总是需要接触的任务；2）利用环境减轻不确定性，在完美信息下，可以不接触地执行任务。最近的趋势是，机器人执行的任务比人类更早，比如按摩，而在经典任务中，比如钉孔，对其他类似任务有更有效的概括，更好的容错能力，以及更快的任务规划或学习。因此，在本次调查中，我们涵盖了机器人执行此类任务的当前阶段，从调查机器人可以执行的所有不同接触任务开始，观察这些任务是如何控制和表示的，最后介绍完成这些任务所需技能的学习和规划。 摘要：In this survey we present the current status on robots performing manipulation tasks that require varying contact with the environment, such that the robot must either implicitly or explicitly control the contact force with the environment to complete the task. Robots can perform more and more manipulation tasks that are still done by humans, and there is a growing number of publications on the topics of 1) performing tasks that always require contact and 2) mitigating uncertainty by leveraging the environment in tasks that, under perfect information, could be performed without contact. The recent trends have seen robots perform tasks earlier left for humans, such as massage, and in the classical tasks, such as peg-in-hole, there is more efficient generalization to other similar tasks, better error tolerance, and faster planning or learning of the tasks. Thus, in this survey we cover the current stage of robots performing such tasks, starting from surveying all the different in-contact tasks robots can perform, observing how these tasks are controlled and represented, and finally presenting the learning and planning of the skills required to complete these tasks.

【7】 Fast Direct Stereo Visual SLAM 标题：快速直接立体声视觉SLAM 链接：https://arxiv.org/abs/2112.01890

作者：Jiawei Mo,Md Jahidul Islam,Junaed Sattar 摘要：提出了一种不依赖于特征检测和匹配的快速、准确的立体视觉同步定位和映射（SLAM）方法。我们将单目直接稀疏里程计（DSO）扩展到立体系统，通过优化3D点的比例来最小化立体配置的光度误差，与传统立体匹配相比，该方法具有计算效率高、鲁棒性强的特点。我们进一步将其扩展到具有环路闭合的全SLAM系统，以减少累积误差。在假设摄像机向前运动的情况下，我们使用从视觉里程计获得的3D点模拟激光雷达扫描，并采用激光雷达描述符进行位置识别，以便更有效地检测环路闭合。然后，我们通过最小化潜在环路闭合的光度误差，使用直接对齐来估计相对姿态。可选地，通过使用迭代最近点（ICP）算法实现对直接对准的进一步改进。最后，我们优化了一个姿势图，以提高全局SLAM精度。通过避免SLAM系统中的特征检测或匹配，我们确保了较高的计算效率和鲁棒性。对公共数据集的彻底实验验证表明，与最先进的方法相比，它是有效的。 摘要：We propose a novel approach for fast and accurate stereo visual Simultaneous Localization and Mapping (SLAM) independent of feature detection and matching. We extend monocular Direct Sparse Odometry (DSO) to a stereo system by optimizing the scale of the 3D points to minimize photometric error for the stereo configuration, which yields a computationally efficient and robust method compared to conventional stereo matching. We further extend it to a full SLAM system with loop closure to reduce accumulated errors. With the assumption of forward camera motion, we imitate a LiDAR scan using the 3D points obtained from the visual odometry and adapt a LiDAR descriptor for place recognition to facilitate more efficient detection of loop closures. Afterward, we estimate the relative pose using direct alignment by minimizing the photometric error for potential loop closures. Optionally, further improvement over direct alignment is achieved by using the Iterative Closest Point (ICP) algorithm. Lastly, we optimize a pose graph to improve SLAM accuracy globally. By avoiding feature detection or matching in our SLAM system, we ensure high computational efficiency and robustness. Thorough experimental validations on public datasets demonstrate its effectiveness compared to the state-of-the-art approaches.

【8】 Active Inference in Robotics and Artificial Agents: Survey and Challenges 标题：机器人和人工智能体中的主动推理：综述和挑战 链接：https://arxiv.org/abs/2112.01871

作者：Pablo Lanillos,Cristian Meo,Corrado Pezzato,Ajith Anil Meera,Mohamed Baioumy,Wataru Ohata,Alexander Tschantz,Beren Millidge,Martijn Wisse,Christopher L. Buckley,Jun Tani 备注：This manuscript is under review in a IEEE journal 摘要：主动推理是一种数学框架，起源于计算神经科学，是一种关于大脑如何执行动作、感知和学习的理论。最近，它被证明是一种很有前途的方法，在不确定状态下的状态估计和控制问题，以及基础建设的目标驱动行为在机器人和人工智能体一般。在这里，我们回顾了用于状态估计、控制、规划和学习的主动推理的最新理论和实现；描述当前的成就，特别关注机器人技术。我们展示了相关的实验，展示了它在适应性、泛化和鲁棒性方面的潜力。此外，我们将此方法与其他框架联系起来，并讨论其预期的好处和挑战：使用变分贝叶斯推理的具有功能生物学合理性的统一框架。 摘要：Active inference is a mathematical framework which originated in computational neuroscience as a theory of how the brain implements action, perception and learning. Recently, it has been shown to be a promising approach to the problems of state-estimation and control under uncertainty, as well as a foundation for the construction of goal-driven behaviours in robotics and artificial agents in general. Here, we review the state-of-the-art theory and implementations of active inference for state-estimation, control, planning and learning; describing current achievements with a particular focus on robotics. We showcase relevant experiments that illustrate its potential in terms of adaptation, generalization and robustness. Furthermore, we connect this approach with other frameworks and discuss its expected benefits and challenges: a unified framework with functional biological plausibility using variational Bayesian inference.

【9】 Graph-Guided Deformation for Point Cloud Completion 标题：基于图形引导的点云补全变形算法 链接：https://arxiv.org/abs/2112.01840

作者：Jieqi Shi,Lingyun Xu,Liang Heng,Shaojie Shen 备注：RAL with IROS 2021 摘要：长期以来，点云完成任务一直被视为纯生成任务。在通过编码器获得全局形状代码后，使用网络预先学习的形状生成完整的点云。然而，这样的模型不希望偏向于先前的平均对象，并且固有地局限于适合几何细节。本文提出了一种以输入数据和中间生成为控制点和支撑点的图引导变形网络，并对点云完成任务的图卷积网络（GCN）引导的优化建模。我们的主要见解是通过网格变形方法模拟最小二乘拉普拉斯变形过程，这为建模几何细节的变化带来了适应性。通过这种方法，我们还减少了完成任务和网格变形算法之间的差距。据我们所知，我们是第一个通过使用GCN引导变形模拟传统图形算法来优化点云完成任务的人。我们在模拟的室内数据集ShapeNet、室外数据集KITTI和我们自行收集的自主驾驶数据集Pandar40上进行了广泛的实验。结果表明，在三维点云完成任务中，我们的方法优于现有的最新算法。 摘要：For a long time, the point cloud completion task has been regarded as a pure generation task. After obtaining the global shape code through the encoder, a complete point cloud is generated using the shape priorly learnt by the networks. However, such models are undesirably biased towards prior average objects and inherently limited to fit geometry details. In this paper, we propose a Graph-Guided Deformation Network, which respectively regards the input data and intermediate generation as controlling and supporting points, and models the optimization guided by a graph convolutional network(GCN) for the point cloud completion task. Our key insight is to simulate the least square Laplacian deformation process via mesh deformation methods, which brings adaptivity for modeling variation in geometry details. By this means, we also reduce the gap between the completion task and the mesh deformation algorithms. As far as we know, we are the first to refine the point cloud completion task by mimicing traditional graphics algorithms with GCN-guided deformation. We have conducted extensive experiments on both the simulated indoor dataset ShapeNet, outdoor dataset KITTI, and our self-collected autonomous driving dataset Pandar40. The results show that our method outperforms the existing state-of-the-art algorithms in the 3D point cloud completion task.

【10】 GelTip Tactile Sensor for Dexterous Manipulation in Clutter 标题：用于杂波中灵巧操作的GelTip触觉传感器 链接：https://arxiv.org/abs/2112.01834

作者：Daniel Fernandes Gomes,Shan Luo 备注：20 pages, 8 figures 摘要：触觉感知是执行灵巧操作任务的机器人的基本能力。虽然相机、激光雷达和其他遥感器可以对场景进行全局和即时评估，但触觉传感器可以减少其测量不确定性，并获得关于接触对象和机器人之间的局部物理交互的信息，这些信息通常无法通过遥感获取。触觉传感器可分为两大类：电子触觉皮肤和基于摄像头的光学触觉传感器。前者细长，可安装在不同的身体部位，而后者则呈棱柱状，具有更高的传感分辨率，为用作机器人手指或指尖提供了良好的优势。这种光学触觉传感器之一是我们的GelTip传感器，形状像手指，可以感应其表面任何位置的接触。因此，GelTip传感器能够检测来自各个方向的接触，就像人的手指一样。为了捕捉这些接触，它使用安装在底座上的摄像机来跟踪覆盖其中空、刚性和透明体的不透明弹性体的变形。由于这种设计，配备GelTip传感器的抓取器能够同时监测抓取闭合内外发生的接触。使用该传感器进行的实验证明了如何定位触点，更重要的是，在手指的任何位置都可能发生触点的杂乱环境中，在灵巧操作任务中利用全方位触摸感应的优势，甚至可能是必要的。制作GelTip传感器的所有材料可在https://danfergo.github.io/geltip/ 摘要：Tactile sensing is an essential capability for robots that carry out dexterous manipulation tasks. While cameras, Lidars and other remote sensors can assess a scene globally and instantly, tactile sensors can reduce their measurement uncertainties and gain information about the local physical interactions between the in-contact objects and the robot, that are often not accessible via remote sensing. Tactile sensors can be grouped into two main categories: electronic tactile skins and camera based optical tactile sensors. The former are slim and can be fitted to different body parts, whereas the latter assume a more prismatic shape and have much higher sensing resolutions, offering a good advantage for being used as robotic fingers or fingertips. One of such optical tactile sensors is our GelTip sensor that is shaped as a finger and can sense contacts on any location of its surface. As such, the GelTip sensor is able to detect contacts from all the directions, like a human finger. To capture these contacts, it uses a camera installed at its base to track the deformations of the opaque elastomer that covers its hollow, rigid and transparent body. Thanks to this design, a gripper equipped with GelTip sensors is capable of simultaneously monitoring contacts happening inside and outside its grasp closure. Experiments carried out using this sensor demonstrate how contacts can be localised, and more importantly, the advantages, and even possibly a necessity, of leveraging all-around touch sensing in dexterous manipulation tasks in clutter where contacts may happen at any location of the finger. All the materials for the fabrication of the GelTip sensor can be found at https://danfergo.github.io/geltip/

【11】 Reducing Tactile Sim2Real Domain Gaps via Deep Texture Generation Networks 标题：利用深度纹理生成网络缩小触觉Sim2Real域间隙 链接：https://arxiv.org/abs/2112.01807

作者：Tudor Jianu,Daniel Fernandes Gomes,Shan Luo 备注：7 pages, 4 figures 摘要：最近，针对光学触觉传感器开发了仿真方法，以实现Sim2Real学习，即首先在仿真中训练模型，然后将其部署到实际机器人上。然而，真实对象中的某些人工制品是不可预测的，例如制造过程造成的缺陷，或自然磨损造成的划痕，因此无法在模拟中表示，导致模拟和真实触觉图像之间存在显著差异。为了解决这个Sim2Real缺口，我们提出了一种新的纹理生成网络，该网络将模拟图像映射为真实感触觉图像，类似于真实传感器接触真实不完美对象。每个模拟触觉图像首先分为两类区域：与物体接触的区域和不接触的区域。前者应用于从真实触觉图像中的真实纹理学习生成的纹理，而后者则在传感器未接触任何对象时保持其外观。这可确保人工制品仅应用于传感器的变形区域。我们的大量实验表明，所提出的纹理生成网络可以在传感器的变形区域生成这些真实的人工制品，同时避免将纹理泄漏到不接触的区域。定量实验进一步表明，当将我们提出的网络生成的自适应图像用于Sim2Real分类任务时，Sim2Real间隙导致的精度下降从38.43%降低到仅0.81%。因此，这项工作有可能加速需要触觉感知的机器人任务的Sim2Real学习。 摘要：Recently simulation methods have been developed for optical tactile sensors to enable the Sim2Real learning, i.e., firstly training models in simulation before deploying them on the real robot. However, some artefacts in the real objects are unpredictable, such as imperfections caused by fabrication processes, or scratches by the natural wear and tear, and thus cannot be represented in the simulation, resulting in a significant gap between the simulated and real tactile images. To address this Sim2Real gap, we propose a novel texture generation network that maps the simulated images into photorealistic tactile images that resemble a real sensor contacting a real imperfect object. Each simulated tactile image is first divided into two types of regions: areas that are in contact with the object and areas that are not. The former is applied with generated textures learned from real textures in the real tactile images, whereas the latter maintains its appearance as when the sensor is not in contact with any object. This makes sure that the artefacts are only applied to the deformed regions of the sensor. Our extensive experiments show that the proposed texture generation network can generate these realistic artefacts on the deformed regions of the sensor, while avoiding leaking the textures into areas of no contact. Quantitative experiments further reveal that when using the adapted images generated by our proposed network for a Sim2Real classification task, the drop in accuracy caused by the Sim2Real gap is reduced from 38.43% to merely 0.81%. As such, this work has the potential to accelerate the Sim2Real learning for robotic tasks requiring tactile sensing.

【12】 Emergency-braking Distance Prediction using Deep Learning 标题：基于深度学习的紧急制动距离预测 链接：https://arxiv.org/abs/2112.01708

作者：Ruisi Zhang,Ashkan Pourkand 摘要：预测紧急制动距离对于避碰相关功能非常重要，避碰相关功能是车辆最基本和最常用的安全功能。在本研究中，我们首先收集了一个大型数据集，包括三维加速度数据和相应的紧急制动距离。利用该数据集，我们提出了一个深度学习模型来预测紧急制动距离，该模型只需要在制动前0.25秒的三维车辆加速度数据作为输入。我们考虑两个路面，我们的深入学习方法是强大的两个路面，并在3英尺内的准确性。 摘要：Predicting emergency-braking distance is important for the collision avoidance related features, which are the most essential and popular safety features for vehicles. In this study, we first gathered a large data set including a three-dimensional acceleration data and the corresponding emergency-braking distance. Using this data set, we propose a deep-learning model to predict emergency-braking distance, which only requires 0.25 seconds three-dimensional vehicle acceleration data before the break as input. We consider two road surfaces, our deep learning approach is robust to both road surfaces and have accuracy within 3 feet.

【13】 Machine Learning Subsystem for Autonomous Collision Avoidance on a small UAS with Embedded GPU 标题：基于嵌入式GPU的小型无人机自主避碰机器学习子系统 链接：https://arxiv.org/abs/2112.01688

作者：Nicholas Polosky,Tyler Gwin,Sean Furman,Parth Barhanpurkar,Jithin Jagannath 备注：IEEE International Workshop on Communication and Networking for Swarms Robotics 摘要：随着基于机器学习的自治模块和嵌入式图形处理单元（GPU）的广泛使用，人们对6G通信网络的无人机系统（UAS）供电解决方案的兴趣大大增加。虽然这些技术彻底改变了无人机解决方案的可能性，但为无人机设计一个可操作、健壮的自治框架仍然是一个多方面的难题。在这项工作中，我们提出了我们新颖的、模块化的UAS自治框架，名为MR iFLY，并讨论了如何将其扩展以支持6G swarm解决方案。我们首先详细介绍了资源受限设备上基于机器学习的UAS自主性所面临的挑战。接下来，我们将深入描述iFLY先生的新型深度估计和碰撞避免技术如何应对这些挑战。最后，我们描述了我们用来衡量性能的各种评估标准，展示了我们优化的机器视觉组件如何比基线模型提供高达15倍的加速，并展示了iFLY先生基于视觉的防撞技术的飞行演示视频。我们认为，这些实证结果证实了iFLY先生可以通过提供独立的碰撞避免和导航功能来减少6G通信群中节点之间的通信开销。 摘要：Interest in unmanned aerial system (UAS) powered solutions for 6G communication networks has grown immensely with the widespread availability of machine learning based autonomy modules and embedded graphical processing units (GPUs). While these technologies have revolutionized the possibilities of UAS solutions, designing an operable, robust autonomy framework for UAS remains a multi-faceted and difficult problem. In this work, we present our novel, modular framework for UAS autonomy, entitled MR-iFLY, and discuss how it may be extended to enable 6G swarm solutions. We begin by detailing the challenges associated with machine learning based UAS autonomy on resource constrained devices. Next, we describe in depth, how MR-iFLY's novel depth estimation and collision avoidance technology meets these challenges. Lastly, we describe the various evaluation criteria we have used to measure performance, show how our optimized machine vision components provide up to 15X speedup over baseline models and present a flight demonstration video of MR-iFLY's vision-based collision avoidance technology. We argue that these empirical results substantiate MR-iFLY as a candidate for use in reducing communication overhead between nodes in 6G communication swarms by providing standalone collision avoidance and navigation capabilities.

【14】 Scale up to infinity: the UWB Indoor Global Positioning System 标题：无限大的尺度：超宽带室内全球定位系统 链接：https://arxiv.org/abs/2112.01950

作者：Luca Santoro,Matteo Nardello,Davide Brunelli,Daniele Fontanelli 摘要：以高精度和可扩展性确定资产位置是市场上研究最多的技术之一。当需要分米级精度或需要定位在室内环境中运行的实体时，基于卫星的定位系统（即GLONASS或伽利略）提供的精度并不总是足够的。在处理室内定位系统时，可伸缩性也是一个经常出现的问题。本文提出了一种创新的超宽带室内GPS本地定位系统，能够在不降低测量更新率的情况下跟踪任意数量的资产。为了提高系统的精度，对数学模型和不确定性来源进行了研究。结果强调了建议的实现如何在绝对最大误差低于20cm的情况下提供定位信息。由于DTDoA传输机制不需要跟踪资产中的活动角色，可伸缩性也得以解决。 摘要：Determining assets position with high accuracy and scalability is one of the most investigated technology on the market. The accuracy provided by satellites-based positioning systems (i.e., GLONASS or Galileo) is not always sufficient when a decimeter-level accuracy is required or when there is the need of localising entities that operate inside indoor environments. Scalability is also a recurrent problem when dealing with indoor positioning systems. This paper presents an innovative UWB Indoor GPS-Like local positioning system able to tracks any number of assets without decreasing measurements update rate. To increase the system's accuracy the mathematical model and the sources of uncertainties are investigated. Results highlight how the proposed implementation provides positioning information with an absolute maximum error below 20 cm. Scalability is also resolved thanks to DTDoA transmission mechanisms not requiring an active role from the asset to be tracked.

机器翻译，仅供参考