机器人相关学术速递[7.8]

cs.RO机器人相关，共计19篇

【1】 RRL: Resnet as representation for Reinforcement Learning 标题：RRL：RESNET作为强化学习的表示

作者：Rutav Shah,Vikash Kumar 机构：Equal contribution 1Department of Computer Science andEngineering, Indian Institute of Technology, In-dia 2Department of Computer Science, University of Washing-ton 备注：17 pages, 12 figures, 5 tables, 1 pseudo-code 链接：https://arxiv.org/abs/2107.03380 摘要：在没有仪器的环境中，通过直接交互自主学习行为的能力可以导致多面手机器人能够提高生产力或在非结构化环境（如家庭）中提供护理。这种非仪器设置只保证使用机器人的本体感知传感器（如车载摄像机、关节编码器等）进行操作，由于高维性和部分可观测性问题，这对策略学习是一个挑战。我们提出RRL:Resnet作为强化学习的表示，这是一种直接而有效的方法，可以直接从本体感觉输入中学习复杂的行为。RRL将从预先训练的Resnet中提取的特征融合到标准的强化学习管道中，并提供与直接从状态学习相当的结果。在一个模拟的灵巧操作基准中，最先进的方法没有取得显著的进展，RRL提供了丰富的接触行为。RRL的吸引力在于它能简单地将表征学习、模仿学习和强化学习等领域的进展结合起来。它在直接从视觉输入学习行为和直接从状态匹配学习的绩效和样本效率方面的有效性，即使在复杂的高维领域，也远不明显。 摘要：The ability to autonomously learn behaviors via direct interactions in uninstrumented environments can lead to generalist robots capable of enhancing productivity or providing care in unstructured settings like homes. Such uninstrumented settings warrant operations only using the robot's proprioceptive sensor such as onboard cameras, joint encoders, etc which can be challenging for policy learning owing to the high dimensionality and partial observability issues. We propose RRL: Resnet as representation for Reinforcement Learning -- a straightforward yet effective approach that can learn complex behaviors directly from proprioceptive inputs. RRL fuses features extracted from pre-trained Resnet into the standard reinforcement learning pipeline and delivers results comparable to learning directly from the state. In a simulated dexterous manipulation benchmark, where the state of the art methods fail to make significant progress, RRL delivers contact rich behaviors. The appeal of RRL lies in its simplicity in bringing together progress from the fields of Representation Learning, Imitation Learning, and Reinforcement Learning. Its effectiveness in learning behaviors directly from visual inputs with performance and sample efficiency matching learning directly from the state, even in complex high dimensional domains, is far from obvious.

【2】 On the Robot Assisted Movement in Wireless Mobile Sensor Networks 标题：无线移动传感器网络中机器人辅助移动的研究

作者：Sajal K. Das,Rafał Kapelko 机构：Department of Fundamentals of Computer Science, Wrocław University of Science and Technology, Poland, Missouri University of Science and Technology, USA 链接：https://arxiv.org/abs/2107.03201 摘要：本文讨论了按一般随机过程在线路上随机布置的随机传感器和按两个独立的一般随机过程在平面上随机布置的随机传感器。将承载能力为$k$的移动机器人放置在原点，移动传感器以实现覆盖、连通性等一般调度要求，从而满足网络中所需的通信性能。我们研究了机器人运动中的能量消耗、传感器数量、传感器范围、干扰距离和机器人容量之间的折衷，直到完成干扰调度任务的同时完成覆盖。在这项工作中，我们得到了机器人运动能量消耗的上界，并得到了机器人总运动成本的急剧下降，从而在满足干扰要求的同时提供覆盖。 摘要：This paper deals with random sensors initially randomly deployed on the line according to general random process and on the plane according to two independent general random processes. The mobile robot with carrying capacity $k$ placed at the origin point is to move the sensors to achieve the general scheduling requirement such as coverage, connectivity and thus to satisfy the desired communication property in the network. We study tradeoffs between the energy consumption in robot's movement, the numbers of sensors $n$, the sensor range $r$, the interference distance $s$, and the robot capacity $k$ until completion of the coverage simultaneously with interference scheduling task. In this work, we obtain upper bounds for the energy consumption in robot's movement and obtain the sharp decrease in the total movement cost of the robot so as to provide the coverage simultaneously with interference requirement.

【3】 Learning Time-Invariant Reward Functions through Model-Based Inverse Reinforcement Learning 标题：基于模型的逆强化学习学习时不变奖励函数

作者：Todor Davchev,Sarah Bechtle,Subramanian Ramamoorthy,Franziska Meier 机构：School of Informatics, University of Edinburgh, MPI for Intelligent Systems, Facebook AI Research, Menlo Park, CA 链接：https://arxiv.org/abs/2107.03186 摘要：逆强化学习是一种范式，其目标是从已证明的行为中学习一般的奖励函数。然而，学习成本的一般性概念通常仅根据对各种空间扰动的鲁棒性进行评估，假设以固定的执行速度部署。然而，这在机器人学的背景下是不切实际的，构建时不变的解决方案是至关重要的。在这项工作中，我们提出了一个公式，允许我们1）通过学习时不变的成本来改变执行的长度，2）放宽从演示学习的时间对齐要求。我们将我们的方法应用于两种不同类型的成本公式，并在模拟放置和钉孔任务的学习奖励函数的上下文中评估它们的性能。我们的研究结果显示，我们的方法可以学习时间不变的奖励，从错位示范，也可以推广到空间分布外的任务。 摘要：Inverse reinforcement learning is a paradigm motivated by the goal of learning general reward functions from demonstrated behaviours. Yet the notion of generality for learnt costs is often evaluated in terms of robustness to various spatial perturbations only, assuming deployment at fixed speeds of execution. However, this is impractical in the context of robotics and building time-invariant solutions is of crucial importance. In this work, we propose a formulation that allows us to 1) vary the length of execution by learning time-invariant costs, and 2) relax the temporal alignment requirements for learning from demonstration. We apply our method to two different types of cost formulations and evaluate their performance in the context of learning reward functions for simulated placement and peg in hole tasks. Our results show that our approach enables learning temporally invariant rewards from misaligned demonstration that can also generalise spatially to out of distribution tasks.

【4】 HIDA: Towards Holistic Indoor Understanding for the Visually Impaired via Semantic Instance Segmentation with a Wearable Solid-State LiDAR Sensor 标题：HIDA：通过使用可穿戴固态LiDAR传感器进行语义实例分割，为视障人士提供全面的室内理解

作者：Huayao Liu,Ruiping Liu,Kailun Yang,Jiaming Zhang,Kunyu Peng,Rainer Stiefelhagen 机构：Karlsruhe Institute of Technology 备注：10 figures, 5 tables 链接：https://arxiv.org/abs/2107.03180 摘要：对于视障人士来说，独立探索未知空间或在室内环境中寻找物体是一项日常但富有挑战性的任务。然而，常见的二维辅助系统缺乏物体之间的深度关系，难以获得精确的空间布局和物体的相对位置。为了解决这些问题，我们提出了HIDA，一个基于固态LiDAR传感器的三维点云实例分割的轻量级辅助系统，用于室内整体探测和回避。整个系统由三个硬件组成，两个交互功能（避障和目标定位）和一个语音用户界面。基于语音引导，用户通过现场扫描获取室内环境变化的最新状态的点云。此外，我们还设计了一个点云分割模型，该模型采用了双轻量译码器进行语义和偏移量预测，满足了整个系统的效率要求。在三维实例分割之后，我们通过去除孤立点并将所有点投影到顶视图二维地图上的方法对分割后的点云进行后处理。该系统综合了上述信息，通过声反馈与用户进行直观的交互。提出的三维实例分割模型在scannetv2数据集上取得了最先进的性能。用户研究中的各种任务的综合现场测试验证了我们的系统的可用性和有效性，帮助视障人士在室内进行整体理解、避障和物体搜索。 摘要：Independently exploring unknown spaces or finding objects in an indoor environment is a daily but challenging task for visually impaired people. However, common 2D assistive systems lack depth relationships between various objects, resulting in difficulty to obtain accurate spatial layout and relative positions of objects. To tackle these issues, we propose HIDA, a lightweight assistive system based on 3D point cloud instance segmentation with a solid-state LiDAR sensor, for holistic indoor detection and avoidance. Our entire system consists of three hardware components, two interactive functions~(obstacle avoidance and object finding) and a voice user interface. Based on voice guidance, the point cloud from the most recent state of the changing indoor environment is captured through an on-site scanning performed by the user. In addition, we design a point cloud segmentation model with dual lightweight decoders for semantic and offset predictions, which satisfies the efficiency of the whole system. After the 3D instance segmentation, we post-process the segmented point cloud by removing outliers and projecting all points onto a top-view 2D map representation. The system integrates the information above and interacts with users intuitively by acoustic feedback. The proposed 3D instance segmentation model has achieved state-of-the-art performance on ScanNet v2 dataset. Comprehensive field tests with various tasks in a user study verify the usability and effectiveness of our system for assisting visually impaired people in holistic indoor understanding, obstacle avoidance and object search.

【5】 Trans4Trans: Efficient Transformer for Transparent Object Segmentation to Help Visually Impaired People Navigate in the Real World 标题：Trans4Trans：用于透明对象分割的高效转换器，帮助视障人士在现实世界中导航

作者：Jiaming Zhang,Kailun Yang,Angela Constantinescu,Kunyu Peng,Karin Müller,Rainer Stiefelhagen 机构：Karlsruhe Institute of Technology, "Obstacle", "Forward", CNN model, Trans,Trans 备注：8 figures, 6 tables 链接：https://arxiv.org/abs/2107.03172 摘要：普通的全玻璃外墙和透明物体构成了建筑障碍，阻碍了视力低下或失明的人的行动，例如，除非正确感知和反应，否则在玻璃门后面检测到的路径是不可接近的。然而，传统的辅助技术很少涉及这些安全关键对象的分割。为了解决这一问题，我们构建了一个可穿戴系统，该系统采用了一种新型的透明双头Transformer（Trans4Trans）模型，能够分割普通和透明的物体，并进行实时寻路，以帮助人们更安全地独自行走。特别是，两个解码器所创建的Transformer解析模块（TPM）实现了有效的联合学习，从不同的数据集。此外，由基于对称Transformer的编码器和解码器组成的高效Trans4Trans模型，计算量小，易于部署在便携式gpu上。我们的Trans4Trans模型在Stanford2D3D和Trans10K-v2数据集的测试集上优于最新的方法，获得的mIoU分别为45.13%和75.14%。通过各种预测试和在室内外场景中进行的用户研究，我们的辅助系统的可用性和可靠性得到了广泛的验证。 摘要：Common fully glazed facades and transparent objects present architectural barriers and impede the mobility of people with low vision or blindness, for instance, a path detected behind a glass door is inaccessible unless it is correctly perceived and reacted. However, segmenting these safety-critical objects is rarely covered by conventional assistive technologies. To tackle this issue, we construct a wearable system with a novel dual-head Transformer for Transparency (Trans4Trans) model, which is capable of segmenting general and transparent objects and performing real-time wayfinding to assist people walking alone more safely. Especially, both decoders created by our proposed Transformer Parsing Module (TPM) enable effective joint learning from different datasets. Besides, the efficient Trans4Trans model composed of symmetric transformer-based encoder and decoder, requires little computational expenses and is readily deployed on portable GPUs. Our Trans4Trans model outperforms state-of-the-art methods on the test sets of Stanford2D3D and Trans10K-v2 datasets and obtains mIoU of 45.13% and 75.14%, respectively. Through various pre-tests and a user study conducted in indoor and outdoor scenarios, the usability and reliability of our assistive system have been extensively verified.

【6】 Minimum Constraint Removal Problem for Line Segments is NP-hard 标题：线段最小约束消除问题是NP-hard问题

作者：Bahram Sadeghi Bigham 机构：Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS), Gava Zang, Zanjan, Iran. 链接：https://arxiv.org/abs/2107.03140 摘要：在最小约束删除（$MCR$）中，没有可行的路径从起点向目标移动，为了找到无碰撞的路径，必须删除最小约束。证明了当约束具有任意形状甚至是凸多边形形状时，$MCR$问题是$NP-hard$。然而，当约束是直线时，它有一个简单的线性解，并且问题对其他情况是开放的。本文利用子集和问题的约简，分三步证明了该问题对于加权线段和非加权线段都是NP困难的。 摘要：In the minimum constraint removal ($MCR$), there is no feasible path to move from the starting point towards the goal and, the minimum constraints should be removed in order to find a collision-free path. It has been proved that $MCR$ problem is $NP-hard$ when constraints have arbitrary shapes or even they are in shape of convex polygons. However, it has a simple linear solution when constraints are lines and the problem is open for other cases yet. In this paper, using a reduction from Subset Sum problem, in three steps, we show that the problem is NP-hard for both weighted and unweighted line segments.

【7】 Humans as Path-Finders for Safe Navigation 标题：人是安全导航的寻路人

作者：Alessandro Antonucci,Paolo Bevilacqua,Stefano Leonardi,Luigi Palopoli,Daniele Fontanelli 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 链接：https://arxiv.org/abs/2107.03079 摘要：移动机器人在非结构化和人类居住的工作环境中广泛应用的最重要障碍之一是规划安全路径的能力。在本文中，我们建议将此活动委派给一个在机器人前面行走的人类操作员，该操作员用她/他的足迹标记要跟随的路径。这种方法的实现要求在定位要跟踪的特定人员（领导者）时具有高度的稳健性。我们提出了一种三阶段的方法来实现这一目标：1.在图像空间中对人的识别和跟踪，2.摄像机数据和激光传感器之间的传感器融合，3.具有连续曲率曲线的点插值。文中介绍了该方法，并用实验结果进行了验证。 摘要：One of the most important barriers toward a widespread use of mobile robots in unstructured and human populated work environments is the ability to plan a safe path. In this paper, we propose to delegate this activity to a human operator that walks in front of the robot marking with her/his footsteps the path to be followed. The implementation of this approach requires a high degree of robustness in locating the specific person to be followed (the leader). We propose a three phase approach to fulfil this goal: 1. identification and tracking of the person in the image space, 2. sensor fusion between camera data and laser sensors, 3. point interpolation with continuous curvature curves. The approach is described in the paper and extensively validated with experimental results.

【8】 Maintaining a Reliable World Model using Action-aware Perceptual Anchoring 标题：使用动作感知感知锚定维护可靠的世界模型

作者：Ying Siu Liang,Dongkyu Choi,Kenneth Kwok 机构： Institute of High Performance Com-puting 备注：None 链接：https://arxiv.org/abs/2107.03038 摘要：可靠的感知对于与世界互动的机器人至关重要。但是传感器本身往往不足以提供这种能力，而且由于环境中的各种条件，它们很容易出错。此外，机器人需要保持其周围环境的模型，即使当物体消失在视野之外，不再可见。这需要将感知信息锚定到表示环境中对象的符号上。在本文中，我们提出了一个动作感知锚定模型，使机器人能够以持久的方式跟踪对象。基于规则的方法考虑了诱导偏差，对低层目标检测的结果进行高层推理，提高了机器人对复杂任务的感知能力。我们评估了我们的模型与现有的基线模型的对象持久性，并表明它优于这些飞贼定位任务使用1371个视频数据集。我们还将我们的动作感知锚定整合到认知架构的背景中，并在通用机器人上的真实变速箱装配任务中展示其优势。 摘要：Reliable perception is essential for robots that interact with the world. But sensors alone are often insufficient to provide this capability, and they are prone to errors due to various conditions in the environment. Furthermore, there is a need for robots to maintain a model of its surroundings even when objects go out of view and are no longer visible. This requires anchoring perceptual information onto symbols that represent the objects in the environment. In this paper, we present a model for action-aware perceptual anchoring that enables robots to track objects in a persistent manner. Our rule-based approach considers inductive biases to perform high-level reasoning over the results from low-level object detection, and it improves the robot's perceptual capability for complex tasks. We evaluate our model against existing baseline models for object permanence and show that it outperforms these on a snitch localisation task using a dataset of 1,371 videos. We also integrate our action-aware perceptual anchoring in the context of a cognitive architecture and demonstrate its benefits in a realistic gearbox assembly task on a Universal Robot.

【9】 WaspL: Design of a Reconfigurable Logistic Robot for Hospital Settings 标题：WaspL：一种可重构的医院后勤机器人设计

作者：Yuyao Shi,Aamir Abdullah Hayat,Sivanantham Vinu,Mohan Rajesh Elara 机构： Singapore University of Technology and Design (SUTD) 链接：https://arxiv.org/abs/2107.03002 摘要：医疗保健提出了不同的后勤需求，这导致在医院环境中部署了几个设计独特的机器人。每一个机器人都有其自身的开销，即无/有限扩展、专用充电站、编程接口、封闭结构、训练需求等。本文介绍了一种可重构物流机器人WaspL的研制。WaspL的设计满足了高机动性、开放式机器人操作系统体系结构、多功能性和可进化性的要求。它实现多种后勤模式，如牵引、提升重型有效载荷、叉式提升低离地净空物体、两个WaspL的嵌套等，满足医院环境所需的不同应用。详细讨论了设计要求、机械布局和系统结构。有限元建模，基于属性的比较与其他标准机器人，以及实验结果支持WaspL的设计能力。 摘要：Healthcare poses diverse logistic requirements, which resulted in the deployment of several distinctly designed robots within a hospital setting. Each robot comes with its overheads in the form of, namely, none/limited scaling, dedicated charging stations, programming interface, closed architecture, training requirements, etc. This paper reports on developing a reconfigurable logistic robot named WaspL. The design of WaspL caters to the requirement of high mobility, open robotic operating system architecture, multi-functionality, and evolvability features. It fulfills multiple logistics modes, like towing, lifting heavy payloads, forklifting low ground clearance objects, nesting of two WaspL} etc., fulfilling different applications required in hospital settings. The design requirements, mechanical layout, and system architecture are discussed in detail. The finite element modeling, attribute-based comparison with other standard robots, are presented along with experimental results supporting the WaspL design capabilities.

【10】 Real-time Semantic 3D Dense Occupancy Mapping with Efficient Free Space Representations 标题：具有高效自由空间表示的实时语义3D密集占用映射

作者：Yuanxin Zhong,Huei Peng 机构：Mechanical Engineering, University of Michigan, Ann Arbor United States 链接：https://arxiv.org/abs/2107.02981 摘要：提出了一种实时语义三维空间占用映射框架。映射框架基于文献中的贝叶斯核推理策略。提出了两种新的自由空间表示方法来有效地构造训练数据和提高映射速度，这是实际部署的一个主要瓶颈。我们的方法甚至可以在消费级CPU上实现实时映射。另一个重要的优点是我们的方法能够处理动态场景，这得益于所提出算法的覆盖完整性。在真实的点云扫描数据集上进行了实验。 摘要：A real-time semantic 3D occupancy mapping framework is proposed in this paper. The mapping framework is based on the Bayesian kernel inference strategy from the literature. Two novel free space representations are proposed to efficiently construct training data and improve the mapping speed, which is a major bottleneck for real-world deployments. Our method achieves real-time mapping even on a consumer-grade CPU. Another important benefit is that our method can handle dynamic scenarios, thanks to the coverage completeness of the proposed algorithm. Experiments on real-world point cloud scan datasets are presented.

【11】 RoboCup@Home Education 2020 Best Performance: RoboBreizh, a modular approach 标题：RoboCup@Home Education 2020最佳表现：RoboBreizh，模块化方法

作者：Antoine Dizet,Cédric Le Bono,Amélie Legeleux,Maëlic neau,Cédric Buche 链接：https://arxiv.org/abs/2107.02978 摘要：每年Robocup@Home比赛挑战团队和机器人的能力。2020年RoboCup@Home教育挑战赛是在网上组织的，改变了通常的竞赛规则。在本文中，我们介绍了最新的发展，导致RoboBreizh队赢得比赛。这些发展包括几个相互连接的模块，使胡椒机器人能够理解、行动并适应当地环境。最新的可用技术已用于导航和对话。第一个贡献包括结合目标检测和姿态估计技术来检测用户的意图。第二个贡献是通过演示学习来轻松学习新动作，从而提高胡椒机器人的技能。该提案荣获2020年度最佳绩效奖RoboCup@Home教育挑战。 摘要：Every year, the Robocup@Home competition challenges teams and robots' abilities. In 2020, the RoboCup@Home Education challenge was organized online, altering the usual competition rules. In this paper, we present the latest developments that lead the RoboBreizh team to win the contest. These developments include several modules linked to each other allowing the Pepper robot to understand, act and adapt itself to a local environment. Up-to-date available technologies have been used for navigation and dialogue. First contribution includes combining object detection and pose estimation techniques to detect user's intention. Second contribution involves using Learning by Demonstrations to easily learn new movements that improve the Pepper robot's skills. This proposal won the best performance award of the 2020 RoboCup@Home Education challenge.

【12】 RAM-VO: Less is more in Visual Odometry 标题：RAM-VO：视觉里程计中的少即是多

作者：Iury Cleveston,Esther L. Colombini 机构：Laboratory of Robotics and Cognitive Systems (LaRoCS), Institute of Computing, University of Campinas, Campinas, S˜ao Paulo, Brazil 链接：https://arxiv.org/abs/2107.02974 摘要：建造能够在没有人监督的情况下运行的车辆需要确定代理人的姿势。视觉里程计（VO）算法仅利用输入图像的视觉变化来估计自我运动。最新的VO方法广泛使用卷积神经网络（CNN）来实现深度学习，这在处理高分辨率图像时增加了大量的成本。此外，在VO任务中，输入数据越多并不意味着预测效果越好；相反，架构可能会过滤掉无用的信息。因此，实现计算效率高、轻量级的体系结构至关重要。在这项工作中，我们提出了RAM-VO，一个扩展的经常性注意模型（RAM）的视觉里程计任务。RAM-VO改进了信息的视觉和时间表示，实现了近端策略优化（PPO）算法来学习鲁棒策略。结果表明，RAM-VO可以用大约300万个参数对单目输入图像进行6个自由度的回归。此外，在KITTI数据集上的实验表明，RAM-VO只使用了5.7%的可用视觉信息就获得了具有竞争力的结果。 摘要：Building vehicles capable of operating without human supervision requires the determination of the agent's pose. Visual Odometry (VO) algorithms estimate the egomotion using only visual changes from the input images. The most recent VO methods implement deep-learning techniques using convolutional neural networks (CNN) extensively, which add a substantial cost when dealing with high-resolution images. Furthermore, in VO tasks, more input data does not mean a better prediction; on the contrary, the architecture may filter out useless information. Therefore, the implementation of computationally efficient and lightweight architectures is essential. In this work, we propose the RAM-VO, an extension of the Recurrent Attention Model (RAM) for visual odometry tasks. RAM-VO improves the visual and temporal representation of information and implements the Proximal Policy Optimization (PPO) algorithm to learn robust policies. The results indicate that RAM-VO can perform regressions with six degrees of freedom from monocular input images using approximately 3 million parameters. In addition, experiments on the KITTI dataset demonstrate that RAM-VO achieves competitive results using only 5.7% of the available visual information.

【13】 Quadruped Locomotion on Non-Rigid Terrain using Reinforcement Learning 标题：基于强化学习的非刚性地形四足行走

作者：Taehei Kim,Sung-Hee Lee 机构： Our experiments show that 1Graduate School of Cultural Technology 链接：https://arxiv.org/abs/2107.02955 摘要：腿部机器人需要能够在不同的地形条件下行走。在本文中，我们提出了一个新的强化学习框架，学习运动的非刚性动态地形。具体来说，我们的框架可以在平坦的弹性地形上产生四足动物的运动，该地形由机器人脚推动时被动上下移动的瓷砖矩阵组成。一个55厘米长的训练机器人可以在下沉5厘米的地形上行走。我们提出了一套观察和奖励条款，使这一运动；我们发现在观测中加入末端效应器历史和末端效应器速度项是至关重要的。通过对不同地形条件下的机器人进行训练，验证了该方法的有效性。 摘要：Legged robots need to be capable of walking on diverse terrain conditions. In this paper, we present a novel reinforcement learning framework for learning locomotion on non-rigid dynamic terrains. Specifically, our framework can generate quadruped locomotion on flat elastic terrain that consists of a matrix of tiles moving up and down passively when pushed by the robot's feet. A trained robot with 55cm base length can walk on terrain that can sink up to 5cm. We propose a set of observation and reward terms that enable this locomotion; in which we found that it is crucial to include the end-effector history and end-effector velocity terms into observation. We show the effectiveness of our method by training the robot with various terrain conditions.

【14】 Supervised Bayesian Specification Inference from Demonstrations 标题：基于示例的有监督贝叶斯规范推理

作者：Ankit Shah,Pritish Kamath,Shen Li,Patrick Craven,Kevin Landers,Kevin Oden,Julie Shah 机构：Massachusetts Institute of Technology 链接：https://arxiv.org/abs/2107.02912 摘要：当观察任务演示时，人类学徒能够在获得实际执行任务的专业知识之前，识别给定任务是否正确执行。先前关于从示范中学习（LfD）的研究未能抓住任务执行的可接受性这一概念；同时，时态逻辑为任务规范的表达提供了一种灵活的语言。受此启发，我们提出了贝叶斯规范推理，一种将任务规范作为时序逻辑公式进行推理的概率模型。我们结合了概率规划的方法来定义我们的先验，以及一个独立于领域的似然函数来实现基于抽样的推理。我们证明了我们的模型用于推断规范的有效性，在合成域和实际表格设置任务中，推断规范和基本事实之间的相似度超过90%。 摘要：When observing task demonstrations, human apprentices are able to identify whether a given task is executed correctly long before they gain expertise in actually performing that task. Prior research into learning from demonstrations (LfD) has failed to capture this notion of the acceptability of a task's execution; meanwhile, temporal logics provide a flexible language for expressing task specifications. Inspired by this, we present Bayesian specification inference, a probabilistic model for inferring task specification as a temporal logic formula. We incorporate methods from probabilistic programming to define our priors, along with a domain-independent likelihood function to enable sampling-based inference. We demonstrate the efficacy of our model for inferring specifications, with over 90% similarity observed between the inferred specification and the ground truth, both within a synthetic domain and during a real-world table setting task.

【15】 Learning Latent Actions to Control Assistive Robots 标题：学习潜在动作控制辅助机器人

作者：Dylan P. Losey,Hong Jun Jeon,Mengxi Li,Krishnan Srinivasan,Ajay Mandlekar,Animesh Garg,Jeannette Bohg,Dorsa Sadigh 机构： Stanford UniversityA, University of Torontomarshmallows 链接：https://arxiv.org/abs/2107.02907 摘要：辅助机器人手臂使残疾人能够独立完成日常任务。这些手臂是灵巧和高维的；然而，人们用来控制机器人的接口是低维的。考虑用2-DOF操纵杆遥操作7自由度机器人手臂。机器人正在帮你吃晚饭，现在你想切一块豆腐。今天的机器人假设操纵杆输入和机器人动作之间有一个预定义的映射：在一种模式下操纵杆控制机器人在x-y平面上的运动，在另一种模式下操纵杆控制机器人的z-yaw运动，等等。但是这个映射错过了你要执行的任务！理想情况下，一个操纵杆轴应控制机器人如何刺豆腐，另一个轴应控制不同的切割运动。我们的见解是，我们可以通过将机器人的高维动作嵌入低维和人类可控的潜在动作中，实现对辅助机器人的直观、用户友好的控制。我们把这个过程分为三个部分。首先，我们探讨了从离线任务示范中学习潜在动作的模型，并形式化了潜在动作应该满足的属性。接下来，我们将学习到的潜在动作与自主机器人辅助相结合，帮助用户达到并维持他们的高层次目标。最后，我们学习了操纵杆输入和潜在动作之间的个性化对齐模型。我们在四个用户研究中评估了我们的方法，其中非残疾参与者吃棉花糖、做苹果派、切豆腐和组装甜点。然后我们用两个每天使用辅助设备的残疾成年人来测试我们的方法。 摘要：Assistive robot arms enable people with disabilities to conduct everyday tasks on their own. These arms are dexterous and high-dimensional; however, the interfaces people must use to control their robots are low-dimensional. Consider teleoperating a 7-DoF robot arm with a 2-DoF joystick. The robot is helping you eat dinner, and currently you want to cut a piece of tofu. Today's robots assume a pre-defined mapping between joystick inputs and robot actions: in one mode the joystick controls the robot's motion in the x-y plane, in another mode the joystick controls the robot's z-yaw motion, and so on. But this mapping misses out on the task you are trying to perform! Ideally, one joystick axis should control how the robot stabs the tofu and the other axis should control different cutting motions. Our insight is that we can achieve intuitive, user-friendly control of assistive robots by embedding the robot's high-dimensional actions into low-dimensional and human-controllable latent actions. We divide this process into three parts. First, we explore models for learning latent actions from offline task demonstrations, and formalize the properties that latent actions should satisfy. Next, we combine learned latent actions with autonomous robot assistance to help the user reach and maintain their high-level goals. Finally, we learn a personalized alignment model between joystick inputs and latent actions. We evaluate our resulting approach in four user studies where non-disabled participants reach marshmallows, cook apple pie, cut tofu, and assemble dessert. We then test our approach with two disabled adults who leverage assistive devices on a daily basis.

【16】 SiMPLeR: A Series-Elastic Manipulator with Passive Variable Stiffness for Legged Robots 标题：SIMPLE：一种用于腿部机器人的被动变刚度串联弹性机械手

作者：Sajiv Shah,Brad Saund 机构：Saratoga High School, Saratoga, CA. USA, Robotics Department, University of Michigan, Ann Arbor, MI. USA 备注：8 pages, 13 figures 链接：https://arxiv.org/abs/2107.02892 摘要：提出了一种机械简单、成本低廉、刚度可控的串联弹性作动器的设计方法。这些特性是动物奔跑、跳跃、投掷和操纵所必需的，然而在机器人中，可变刚度执行器要么复杂，要么通过反馈控制器在低带宽下模仿。一个健壮和简单的设计是需要建立可靠和廉价的机器人与动物的能力。我们设计的关键是将扭簧连接到正时皮带上，以形成一个可变刚度的线性弹簧。在一对对立的，改变电机和关节之间的距离，然后改变驱动器的刚度。我们建立了我们提出的驱动器的原型，显示了理论行为与实验特征相匹配，并演示了在机器人单足跳跃中的应用。 摘要：We propose a mechanically simple and cheap design for a series elastic actuator with controllable stiffness. Such characteristics are necessary for animals for running, jumping, throwing, and manipulation, yet in robots, variable stiffness actuators are either complicated or mimicked at low bandwidth through feedback controllers. A robust and simple design is needed to build reliable and cheap robots with animal capabilities. The key insight of our design is attaching torsional springs to timing belts to create a variable stiffness linear spring. In an antagonistic pair, varying the distance between motor and joint then varies the actuator stiffness. We build a prototype of our proposed actuator, show the theoretical behavior matches the experimental characterization, and demonstrate an application to robotic one-legged hopping.

【17】 Toward Robotically Automated Femoral Vascular Access 标题：走向自动化的股血管通路

作者：Nico Zevallos,Evan Harber,Abhimanyu,Kirtan Patel,Yizhu Gu,Kenny Sladick,Francis Guyette,Leonard Weiss,Michael R. Pinsky,Hernando Gomez,John Galeotti,Howie Choset 机构：edu 2Department of Mechanical Engineering, Carnegie Mellon University, USA 3Department of Emergency Medicine, University of Pittsburgh 备注：6 pages, 5 figures, 1 table, submitted (but not accepted yet) to ISMR 链接：https://arxiv.org/abs/2107.02839 摘要：先进的复苏技术，如体外膜氧合（ECMO）插管或复苏性血管内球囊闭塞主动脉（REBOA），即使是技术熟练的医务人员在技术上也是困难的。本文介绍了构成远程操作系统的核心技术，该系统能够提供股骨血管通路，这是这两个程序中的重要一步，也是它们在该领域广泛应用的主要障碍。这些技术包括运动学操纵器、各种传感模式和用户界面。此外，我们评估了我们的系统上的手术模型以及在活体猪实验。据我们所知，这导致了第一次机器人辅助动脉插管；这是我们通过Seldinger技术实现导管自动插入的最终目标的重要一步。 摘要：Advanced resuscitative technologies, such as Extra Corporeal Membrane Oxygenation (ECMO) cannulation or Resuscitative Endovascular Balloon Occlusion of the Aorta (REBOA), are technically difficult even for skilled medical personnel. This paper describes the core technologies that comprise a teleoperated system capable of granting femoral vascular access, which is an important step in both of these procedures and a major roadblock in their wider use in the field. These technologies include a kinematic manipulator, various sensing modalities, and a user interface. In addition, we evaluate our system on a surgical phantom as well as in-vivo porcine experiments. These resulted in, to the best of our knowledge, the first robot-assisted arterial catheterizations; a major step towards our eventual goal of automatic catheter insertion through the Seldinger technique.

【18】 Search-based Path Planning for a High Dimensional Manipulator in Cluttered Environments Using Optimization-based Primitives 标题：基于优化基元的高维机械臂在杂波环境中的搜索路径规划

作者：Muhammad Suhail Saleem,Raghav Sood,Sho Onodera,Rohit Arora,Hiroyuki Kanazawa,Maxim Likhachev 机构： Likhachev are with the RoboticsInstitute, Carnegie Mellon University 备注：Accepted for presentation at 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 链接：https://arxiv.org/abs/2107.02829 摘要：在这项工作中，我们处理的路径规划问题的21维蛇机器人般的机械手，导航混乱的燃气轮机的目的是检查。基于启发式搜索的方法是常见操作领域的有效规划策略。然而，它们在高维系统上的性能在很大程度上依赖于动作空间的有效性和所选择的启发式算法。我们系统的复杂性、可达性约束和高度混乱的涡轮环境使得行动空间和启发式的天真选择变得无效。在这种程度上，我们开发了i）一种基于在线优化的动态生成动作的方法，帮助机器人在狭窄的空间中导航；ii）一种惰性生成这些计算昂贵的优化动作的技术，以有效利用资源，以及iii）在机器人工作空间中推理由涡轮叶片诱导的同伦类的启发式算法和引导沿相关类搜索的多启发式框架。我们的贡献的影响是通过模拟实验研究，其中21自由度机械手导航到汽轮机内的检查区域。 摘要：In this work we tackle the path planning problem for a 21-dimensional snake robot-like manipulator, navigating a cluttered gas turbine for the purposes of inspection. Heuristic search based approaches are effective planning strategies for common manipulation domains. However, their performance on high dimensional systems is heavily reliant on the effectiveness of the action space and the heuristics chosen. The complex nature of our system, reachability constraints, and highly cluttered turbine environment renders naive choices of action spaces and heuristics ineffective. To this extent we have developed i) a methodology for dynamically generating actions based on online optimization that help the robot navigate narrow spaces, ii) a technique for lazily generating these computationally expensive optimization actions to effectively utilize resources, and iii) heuristics that reason about the homotopy classes induced by the blades of the turbine in the robot workspace and a Multi-Heuristic framework which guides the search along the relevant classes. The impact of our contributions is presented through an experimental study in simulation, where the 21 DOF manipulator navigates towards regions of inspection within a turbine.

【19】 MPLP: Massively Parallelized Lazy Planning 标题：MPLP：大规模并行懒惰计划

作者：Shohin Mukherjee,Sandip Aine,Maxim Likhachev 机构： The benefit of these methods is that they are moretime-efficient in domains where the cost of edge evaluationThe authors are with the Robotics Institute 链接：https://arxiv.org/abs/2107.02826 摘要：在计算量主要由边缘计算量所决定的领域，惰性搜索算法已经被开发出来以有效地解决规划问题。当前的方法是通过在搜索图和计算边之间智能地平衡计算工作量。但是，这些算法被设计为作为单个进程运行，并且没有利用现代处理器的多线程功能。在这项工作中，我们提出了一个大规模并行化，有界次优，懒惰搜索算法（MPLP），利用现代多核处理器。在MPLP中，图的搜索和边的计算是完全异步并行执行的，这导致了规划时间的极大改进。我们在两个不同的规划领域验证了所提出的算法：仿人导航任务的运动规划和机器人装配任务的运动规划。我们证明了MPLP的性能优于现有的延迟搜索和并行搜索算法。 摘要：Lazy search algorithms have been developed to efficiently solve planning problems in domains where the computational effort is dominated by the cost of edge evaluation. The current approaches operate by intelligently balancing computational effort between searching the graph and evaluating edges. However these algorithms are designed to run as a single process and do not leverage the multi-threading capability of modern processors. In this work we propose a massively parallelized, bounded suboptimal, lazy search algorithm (MPLP) that harnesses modern multi-core processors. In MPLP, searching of the graph and edge evaluations are performed completely asynchronously in parallel, leading to a drastic improvement in planning time. We validate the proposed algorithm in two different planning domains: motion planning for a humanoid navigation and task and motion planning for a robotic assembly task. We show that MPLP outperforms the state of the art lazy search as well as parallel search algorithms.