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cs.RO机器人相关，共计11篇
【1】 Toward Designing Social Human-Robot Interactions for Deep Space Exploration 标题：面向深空探测的社会性人-机器人交互设计
作者：Huili Chen,Cynthia Breazeal 机构：MIT Media Lab 备注：spaceCHI workshop at CHI2021 链接：https://arxiv.org/abs/2105.08631 摘要：在规划未来人类太空探索时，重要的是要考虑如何设计以提升船员之间的人际沟通和社会动态。如果具体化的社交机器人能够帮助改善整个团队在太空中的互动体验呢？在地球上，社交机器人在提供友谊、缓解压力和焦虑、促进人与人之间的联系、提高团队绩效以及调解人类群体中的冲突方面已经被证明是有效的。在这篇论文中，我们介绍了一套新颖的研究问题，探讨人类与机器人在长期太空探索任务中的社会互动。 摘要：In planning for future human space exploration, it is important to consider how to design for uplifting interpersonal communications and social dynamics among crew members. What if embodied social robots could help to improve the overall team interaction experience in space? On Earth, social robots have been shown effective in providing companionship, relieving stress and anxiety, fostering connection among people, enhancing team performance, and mediating conflicts in human groups. In this paper, we introduce a set of novel research questions exploring social human-robot interactions in long-duration space exploration missions.
【2】 Graph Neural Networks for Decentralized Multi-Robot Submodular Action Selection 标题：图神经网络在分散式多机器人子模块动作选择中的应用
作者：Lifeng Zhou,Vishnu D. Sharma,Qingbiao Li,Amanda Prorok,Alejandro Ribeiro,Vijay Kumar 机构： Universityof Maryland 链接：https://arxiv.org/abs/2105.08601 摘要：本文提出了一种基于学习的分散子模最大化方法。我们专注于机器人需要联合选择动作的应用，例如，运动原语，以最大限度地利用本地通信实现团队子模块目标。这些应用对于大规模多机器人协调如区域覆盖的多机器人运动规划、环境探测和目标跟踪等都是必不可少的。但现有的分散子模最大化算法要么需要对机器人间的通信进行假设，要么失去一些次优的保证。在这项工作中，我们提出了一个通用的学习架构朝着子模块最大化的规模，分散通信。特别地，我们的学习架构利用图形神经网络（GNN）来捕捉机器人的局部交互，并学习机器人的分散决策。我们通过模仿专家解决方案来训练学习模型，并实现了只涉及局部观察和通信的分散行动选择模型。我们展示了我们的GNN为基础的学习方法的性能在一个大的机器人网络的主动目标覆盖的场景。仿真结果表明，该方法的覆盖性能与专家算法相当，但在30多个机器人的情况下运行速度加快了几个数量级。实验结果也显示了我们的方法在先前未知的场景中的泛化能力，例如，更大的环境和更大的机器人网络。 摘要：In this paper, we develop a learning-based approach for decentralized submodular maximization. We focus on applications where robots are required to jointly select actions, e.g., motion primitives, to maximize team submodular objectives with local communications only. Such applications are essential for large-scale multi-robot coordination such as multi-robot motion planning for area coverage, environment exploration, and target tracking. But the current decentralized submodular maximization algorithms either require assumptions on the inter-robot communication or lose some suboptimal guarantees. In this work, we propose a general-purpose learning architecture towards submodular maximization at scale, with decentralized communications. Particularly, our learning architecture leverages a graph neural network (GNN) to capture local interactions of the robots and learns decentralized decision-making for the robots. We train the learning model by imitating an expert solution and implement the resulting model for decentralized action selection involving local observations and communications only. We demonstrate the performance of our GNN-based learning approach in a scenario of active target coverage with large networks of robots. The simulation results show our approach nearly matches the coverage performance of the expert algorithm, and yet runs several orders faster with more than 30 robots. The results also exhibit our approach's generalization capability in previously unseen scenarios, e.g., larger environments and larger networks of robots.
【3】 GPR: Grasp Pose Refinement Network for Cluttered Scenes 标题：GPR：针对杂乱场景的抓取姿态优化网络
作者：Wei Wei,Yongkang Luo,Fuyu Li,Guangyun Xu,Jun Zhong,Wanyi Li,Peng Wang 机构： 1 Institute of Automation, 2 School of Artificial Intelligence, University of Chinese Academy ofSciences 备注：7 pages, accepted to ICRA2021 链接：https://arxiv.org/abs/2105.08502 摘要：在杂乱的场景中抓取物体是机器人操作的一个广泛研究领域。目前的研究主要集中在基于单镜头抓取检测网络的点云抓取姿态估计上。然而，由于缺乏对局部抓取区域的几何感知，可能导致严重碰撞和抓取构型不稳定。本文提出了一种两阶段抓取姿态优化网络，在全局检测抓取的同时，对低质量抓取进行微调，对噪声抓取进行局部滤波。此外，我们扩展了六自由度抓取，增加了抓取宽度作为抓取宽度，这对于杂乱场景中的无碰撞抓取至关重要。它以单个视点云作为输入，预测密集而精确的抓取配置。为了提高泛化能力，我们构建了一个包含150种不同形状商品的合成单目标抓取数据集，以及一个包含100k点云的多目标杂乱场景数据集，该数据集具有健壮、密集的抓取姿势和遮罩注释。在yumiirb-1400机器人上进行的实验表明，在我们的数据集上训练的模型在实际环境中表现良好，大大优于以前的方法。 摘要：Object grasping in cluttered scenes is a widely investigated field of robot manipulation. Most of the current works focus on estimating grasp pose from point clouds based on an efficient single-shot grasp detection network. However, due to the lack of geometry awareness of the local grasping area, it may cause severe collisions and unstable grasp configurations. In this paper, we propose a two-stage grasp pose refinement network which detects grasps globally while fine-tuning low-quality grasps and filtering noisy grasps locally. Furthermore, we extend the 6-DoF grasp with an extra dimension as grasp width which is critical for collisionless grasping in cluttered scenes. It takes a single-view point cloud as input and predicts dense and precise grasp configurations. To enhance the generalization ability, we build a synthetic single-object grasp dataset including 150 commodities of various shapes, and a multi-object cluttered scene dataset including 100k point clouds with robust, dense grasp poses and mask annotations. Experiments conducted on Yumi IRB-1400 Robot demonstrate that the model trained on our dataset performs well in real environments and outperforms previous methods by a large margin.
【4】 Camera Frame Misalignment in a Teleoperated Eye-in-Hand Robot: Effects and a Simple Correction Method 标题：遥操作手眼机器人中摄像机帧错位的影响及简单校正方法
作者：Liao Wu,Fangwen Yu,Jiaole Wang,Thanh Nho Do 机构： Wang is with the School of Mechanical Engineering and Au-tomation 备注：Submitted to IEEE Transactions on Human-Machine Systems 链接：https://arxiv.org/abs/2105.08466 摘要：在遥控操作系统中，摄像机框架和操作员框架之间的不对中是常见的，并且通常会降低操作性能。这种错位的影响还没有充分研究的眼-手系统-系统的相机（眼睛）安装到末端执行器（手），以获得紧凑的密闭空间，如内窥镜手术。本文系统地研究了遥操作手眼机器人中摄像机帧不对中的影响，提出了一种简单的视图显示校正方法。设计了一个仿真来比较不同条件下的失准效应。要求用户通过遥操作将刚体从初始位置移动到指定的目标位置，并模拟不同程度的失准。研究发现，在正交方向（~40s）上输入运动与输出视图之间的失准比在相反方向（~20s）上的失准更难被算子补偿。在一个具有手眼结构的同心圆管机器人上进行了实验。用户被要求对机器人进行远程操作以完成挑选和放置任务。结果显示，在矫正后，手术完成时间（平均40.6%，中位数38.6%）、轨迹长度（平均34.3%，中位数28.1%）、难度（50.5%）、不稳定性（49.4%）和精神压力（60.9%）有显著改善。 摘要：Misalignment between the camera frame and the operator frame is commonly seen in a teleoperated system and usually degrades the operation performance. The effects of such misalignment have not been fully investigated for eye-in-hand systems - systems that have the camera (eye) mounted to the end-effector (hand) to gain compactness in confined spaces such as in endoscopic surgery. This paper provides a systematic study on the effects of the camera frame misalignment in a teleoperated eye-in-hand robot and proposes a simple correction method in the view display. A simulation is designed to compare the effects of the misalignment under different conditions. Users are asked to move a rigid body from its initial position to the specified target position via teleoperation, with different levels of misalignment simulated. It is found that misalignment between the input motion and the output view is much more difficult to compensate by the operators when it is in the orthogonal direction (~40s) compared with the opposite direction (~20s). An experiment on a real concentric tube robot with an eye-in-hand configuration is also conducted. Users are asked to telemanipulate the robot to complete a pick-and-place task. Results show that with the correction enabled, there is a significant improvement in the operation performance in terms of completion time (mean 40.6%, median 38.6%), trajectory length (mean 34.3%, median 28.1%), difficulty (50.5%), unsteadiness (49.4%), and mental stress (60.9%).
【5】 Blind Bipedal Stair Traversal via Sim-to-Real Reinforcement Learning 标题：基于仿实强化学习的两足动物盲目楼梯遍历
作者：Jonah Siekmann,Kevin Green,John Warila,Alan Fern,Jonathan Hurst 机构：CoRIS Institute, Oregon State University, †Agility Robotics 备注：Accepted to RSS 2021. Submission video available at this https URL and video of a supplemental robustness test at this https URL 链接：https://arxiv.org/abs/2105.08328 摘要：准确而精确的地形估计是机器人在现实环境中运动的一个难题。因此，使系统不依赖于对脆弱点的准确估计是有用的。在本文中，我们探讨了这种方法的局限性，通过研究在没有任何外部感知或地形模型的情况下，双足机器人穿越阶梯状地形的问题。对于这样的盲双足平台，由于海拔的突然变化，这个问题显得很困难（甚至对人类也是如此）。我们的主要贡献是证明了sim-to-real-reinforcement learning（RL）可以在双足机器人Cassie上仅使用本体感知反馈就可以实现在阶梯状地形上的鲁棒运动。重要的是，这只需要修改现有的平坦地形训练RL框架，以包括阶梯状地形随机化，而不需要改变任何奖励函数。据我们所知，这是第一个控制器的双足，人类规模的机器人能够可靠地穿越各种真实世界的楼梯和其他楼梯样干扰只用本体感觉。 摘要：Accurate and precise terrain estimation is a difficult problem for robot locomotion in real-world environments. Thus, it is useful to have systems that do not depend on accurate estimation to the point of fragility. In this paper, we explore the limits of such an approach by investigating the problem of traversing stair-like terrain without any external perception or terrain models on a bipedal robot. For such blind bipedal platforms, the problem appears difficult (even for humans) due to the surprise elevation changes. Our main contribution is to show that sim-to-real reinforcement learning (RL) can achieve robust locomotion over stair-like terrain on the bipedal robot Cassie using only proprioceptive feedback. Importantly, this only requires modifying an existing flat-terrain training RL framework to include stair-like terrain randomization, without any changes in reward function. To our knowledge, this is the first controller for a bipedal, human-scale robot capable of reliably traversing a variety of real-world stairs and other stair-like disturbances using only proprioception.
【6】 Robust Physics-Based Manipulation by Interleaving Open and Closed-Loop Execution 标题：通过交错执行开环和闭环实现健壮的基于物理的操作
作者：Wisdom C. Agboh,Mehmet R. Dogar 机构：School of Computing, University of Leeds 链接：https://arxiv.org/abs/2105.08325 摘要：我们提出了一个不确定条件下基于物理操作的计划和控制框架。其核心思想是将鲁棒开环执行与闭环控制交织。我们通过收缩理论导出鲁棒性度量。我们使用这些度量来规划轨迹，这些轨迹对状态不确定性和模型不精确性都具有鲁棒性。然而，对于许多多接触操作问题，很难找到或不存在完全鲁棒的轨迹。通过鲁棒图上的最小代价路径搜索，将轨迹分为鲁棒段和非鲁棒段。鲁棒段采用开环执行，非鲁棒段采用模型预测控制。我们在一个真实的机器人系统上进行实验，以便在杂乱的环境中达到目的。我们的结果表明，与开环基线相比，开环和闭环方法的实际成功率高达35%，与模型预测控制相比，执行时间减少了40%。我们首次证明了用我们的方法生成的部分开环操作方案达到了与模型预测控制相似的成功率，同时实现了更流畅/实时的执行。一个显示真实机器人执行的视频可以在https://youtu.be/rPOPCwHfV4g. 摘要：We present a planning and control framework for physics-based manipulation under uncertainty. The key idea is to interleave robust open-loop execution with closed-loop control. We derive robustness metrics through contraction theory. We use these metrics to plan trajectories that are robust to both state uncertainty and model inaccuracies. However, fully robust trajectories are extremely difficult to find or may not exist for many multi-contact manipulation problems. We separate a trajectory into robust and non-robust segments through a minimum cost path search on a robustness graph. Robust segments are executed open-loop and non-robust segments are executed with model-predictive control. We conduct experiments on a real robotic system for reaching in clutter. Our results suggest that the open and closed-loop approach results in up to 35% more real-world success compared to open-loop baselines and a 40% reduction in execution time compared to model-predictive control. We show for the first time that partially open-loop manipulation plans generated with our approach reach similar success rates to model-predictive control, while achieving a more fluent/real-time execution. A video showing real-robot executions can be found at https://youtu.be/rPOPCwHfV4g.
【7】 Differentiable Factor Graph Optimization for Learning Smoothers 标题：学习斯摩尔的可微因素图优化
作者：Brent Yi,Michelle Lee,Alina Kloss,Roberto Martín-Martín,Jeannette Bohg 机构：(,) MAP Inference, Linearize, Solve 链接：https://arxiv.org/abs/2105.08257 摘要：最近的一项工作表明，贝叶斯滤波器的端到端优化可用于学习底层模型难以手工设计或调整的系统的状态估计器，同时保留概率状态估计的核心优势。作为这种情况下状态估计的另一种方法，我们提出了一种端到端的学习方法，将状态估计建模为基于因子图的平滑器。通过展开这些概率图形模型中用于最大后验推理的优化器，该方法能够在整个状态估计器的上下文中学习概率系统模型，同时还利用了平滑器相对于递归滤波器所提供的独特精度和运行时优势。我们研究我们的方法使用两个基本的状态估计问题，目标跟踪和视觉里程计，其中我们证明了一个显着的改善现有的基线。我们的工作附带了广泛的代码版本，其中包括可微李论和因子图优化的评估模型和库：https://sites.google.com/view/diffsmoothing/ 摘要：A recent line of work has shown that end-to-end optimization of Bayesian filters can be used to learn state estimators for systems whose underlying models are difficult to hand-design or tune, while retaining the core advantages of probabilistic state estimation. As an alternative approach for state estimation in these settings, we present an end-to-end approach for learning state estimators modeled as factor graph-based smoothers. By unrolling the optimizer we use for maximum a posteriori inference in these probabilistic graphical models, this method is able to learn probabilistic system models in the full context of an overall state estimator, while also taking advantage of the distinct accuracy and runtime advantages that smoothers offer over recursive filters. We study our approach using two fundamental state estimation problems, object tracking and visual odometry, where we demonstrate a significant improvement over existing baselines. Our work comes with an extensive code release, which includes the evaluated models and libraries for differentiable Lie theory and factor graph optimization: https://sites.google.com/view/diffsmoothing/
【8】 Zero Dynamics, Pendulum Models, and Angular Momentum in Feedback Control of Bipedal Locomotion 标题：两足运动反馈控制中的零动力学、摆模型和角动量
作者：Yukai Gong,Jessy Grizzle 机构： This latter condition can be met byFunding for this work was provided in part by the Toyota Research Institute(TRI) under award number No, The authors are with the College of Engineering and the RoboticsInstitute, UniversityofMichigan 备注：20 pages, 14 figures. arXiv admin note: text overlap with arXiv:2008.10763 链接：https://arxiv.org/abs/2105.08170 摘要：低维模型普遍存在于两足机器人的文献中。一方面，选择简化的摆模型来捕捉质心动力学。另一方面是虚拟约束诱导的被动低维模型。在第一种情况下，低维模型因其物理洞察力和分析可处理性而受到重视。在第二种情况下，低维模型是对机器人全维模型中行走步态稳定性进行严格分析的一部分。本文将这两种方法结合起来，阐明它们的共同点和区别。在此过程中，我们认为接触点的角动量比线速度更能反映机器人的状态。具体地说，我们证明了用角动量参数化的近似（单摆和零动力学）模型比用线速度参数化的近似模型更精确。我们在三维机器人Cassie上实现了一个基于角动量的控制器，并通过实验证明了该控制器具有很高的灵活性和鲁棒性。 摘要：Low-dimensional models are ubiquitous in the bipedal robotics literature. On the one hand, are the simplified pendulum models selected to capture the center of mass dynamics. On the other hand, is the passive low-dimensional model induced by virtual constraints. In the first case, the low-dimensional model is valued for its physical insight and analytical tractability. In the second case, the low-dimensional model is integral to a rigorous analysis of the stability of walking gaits in the full-dimensional model of the robot. This paper brings these two approaches together, clarifying their commonalities and differences. In the process of doing so, we argue that angular momentum about the contact point is a better indicator of robot state than linear velocity. Concretely, we show that an approximate (pendulum and zero dynamics) model parameterized by angular momentum is more accurate on a physical robot (e.g., legs with mass) than is a related approximate model parameterized in terms of linear velocity. We implement an associated angular-momentum-based controller on Cassie, a 3D robot, and demonstrate high agility and robustness in experiments.
【9】 Safe Occlusion-aware Autonomous Driving via Game-Theoretic Active Perception 标题：基于博弈论主动感知的安全遮挡自主驾驶
作者：Zixu Zhang,Jaime F. Fisac 机构：Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 备注：To be appeared in Robotics: Science and Systems (RSS), 2021 链接：https://arxiv.org/abs/2105.08169 摘要：与其他交通参与者交互的自主车辆在很大程度上依赖于对其他主体行为的感知和预测来规划安全的轨迹。然而，由于遮挡限制了车辆的感知能力，因此在开发自动驾驶系统时，对视野之外的潜在危险进行推理是最具挑战性的问题之一。提出了一种新的分析方法，将闭塞条件下的安全轨迹规划问题归结为自主车辆（回避者）与初始隐藏交通参与者（追踪器）之间的混合零和动态博弈。由于遮挡，追击者的状态最初对躲避者来说是未知的，随后可能会被车辆的传感器发现。分析得出了两个参与者的最优策略，以及保证自主车辆避免碰撞的初始条件集。我们利用这一理论结果开发了一个新的自主驾驶轨迹规划框架，该框架通过考虑车辆在未来观测中一旦发现其他道路使用者就主动避开他们的能力，提供最坏情况下的安全保证，同时最小化保守性。我们的框架是不可知的驾驶环境，适合各种运动规划。我们使用开源的CARLA模拟器演示了我们的算法在具有挑战性的城市和高速公路驾驶场景中的应用。实验结果见https://youtu.be/Cdm1T6Iv7GI. 摘要：Autonomous vehicles interacting with other traffic participants heavily rely on the perception and prediction of other agents' behaviors to plan safe trajectories. However, as occlusions limit the vehicle's perception ability, reasoning about potential hazards beyond the field-of-view is one of the most challenging issues in developing autonomous driving systems. This paper introduces a novel analytical approach that poses the problem of safe trajectory planning under occlusions as a hybrid zero-sum dynamic game between the autonomous vehicle (evader), and an initially hidden traffic participant (pursuer). Due to occlusions, the pursuer's state is initially unknown to the evader and may later be discovered by the vehicle's sensors. The analysis yields optimal strategies for both players as well as the set of initial conditions from which the autonomous vehicle is guaranteed to avoid collisions. We leverage this theoretical result to develop a novel trajectory planning framework for autonomous driving that provides worst-case safety guarantees while minimizing conservativeness by accounting for the vehicle's ability to actively avoid other road users as soon as they are detected in future observations. Our framework is agnostic to the driving environment and suitable for various motion planners. We demonstrate our algorithm on challenging urban and highway driving scenarios using the open-source CARLA simulator. The experimental results can be found in https://youtu.be/Cdm1T6Iv7GI.
【10】 StRETcH: a Soft to Resistive Elastic Tactile Hand 标题：伸展：一种柔软到抗阻的弹性触觉之手
作者：Carolyn Matl,Josephine Koe,Ruzena Bajcsy 机构： was later applied as anAll authors are affiliated with the Department of Electrical Engineeringand Computer Science, University of California 备注：8 pages, 8 figures, To be published in Proc. 2020 IEEE International Conference on Robotics and Automation (ICRA), expanded figures, added clarification for stiffness estimation (Sec. VI-B, paragraph 4) 链接：https://arxiv.org/abs/2105.08154 摘要：软光学触觉传感器通过捕捉高分辨率接触几何和物体柔顺性估计等重要特征，使机器人能够操纵可变形物体。这项工作提出了一个变刚度软触觉末端执行器称为拉伸，软电阻弹性触觉手，这是很容易制造和集成的机械臂。弹性膜悬挂在两个机械手手指之间，深度传感器捕捉弹性膜的变形，能够精确估计亚毫米级的接触几何形状。平行钳口夹持器通过单向拉伸改变膜的刚度，可控制地将拉伸的有效模量从约4kPa调节到9kPa。这项工作使用拉伸来重建刚性和可变形物体的接触几何，估计四个装满不同物质的气球的刚度，并将面团操纵成所需的形状。 摘要：Soft optical tactile sensors enable robots to manipulate deformable objects by capturing important features such as high-resolution contact geometry and estimations of object compliance. This work presents a variable stiffness soft tactile end-effector called StRETcH, a Soft to Resistive Elastic Tactile Hand, that is easily manufactured and integrated with a robotic arm. An elastic membrane is suspended between two robotic fingers, and a depth sensor capturing the deformations of the elastic membrane enables sub-millimeter accurate estimates of contact geometries. The parallel-jaw gripper varies the stiffness of the membrane by uni-axially stretching it, which controllably modulates StRETcH's effective modulus from approximately 4kPa to 9kPa. This work uses StRETcH to reconstruct the contact geometry of rigid and deformable objects, estimate the stiffness of four balloons filled with different substances, and manipulate dough into a desired shape.
【11】 Reactive Navigation Framework for Mobile Robots by Heuristically Evaluated Pre-sampled Trajectories 标题：基于启发式预采样轨迹评估的移动机器人反应式导航框架
作者：Neşet Ünver Akmandor,Taşkın Padır 机构： Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA, Institute for Experiential Robotics, Received (xxxxxx), Revised (xxxxxx), Accepted (xxxxxx) 备注：This paper is accepted for publication in International Journal of Robotic Computing (IJRC). arXiv admin note: substantial text overlap with arXiv:2001.09199 链接：https://arxiv.org/abs/2105.08145 摘要：本文描述并分析了一种未知环境下移动机器人的反应式导航框架。该方法不依赖于全局地图，只考虑以机器人为中心的三维网格结构中的局部占用。该算法通过对预采样轨迹的动态启发式估计，实现了快速导航。在每一个循环中，这些路径由一个基于启发式特征的加权代价函数来评估，例如接近目标、先前选择的轨迹和附近的障碍物。本文介绍了一种在选定轨迹上计算可行位姿的系统方法，然后将其发送给控制器执行运动。定义了该框架的结构，给出了实现细节，并说明了如何调整其离线和在线参数。为了证明该算法在未知环境下的通用性和适应性，在各种地图上进行了物理仿真。基准测试表明，该算法比以前的迭代算法和另一种先进的方法具有更高的性能。算法和基准数据的开源实现可以在url中找到{https://github.com/RIVeR-Lab/tentabot}. 摘要：This paper describes and analyzes a reactive navigation framework for mobile robots in unknown environments. The approach does not rely on a global map and only considers the local occupancy in its robot-centered 3D grid structure. The proposed algorithm enables fast navigation by heuristic evaluations of pre-sampled trajectories on-the-fly. At each cycle, these paths are evaluated by a weighted cost function, based on heuristic features such as closeness to the goal, previously selected trajectories, and nearby obstacles. This paper introduces a systematic method to calculate a feasible pose on the selected trajectory, before sending it to the controller for the motion execution. Defining the structures in the framework and providing the implementation details, the paper also explains how to adjust its offline and online parameters. To demonstrate the versatility and adaptability of the algorithm in unknown environments, physics-based simulations on various maps are presented. Benchmark tests show the superior performance of the proposed algorithm over its previous iteration and another state-of-art method. The open-source implementation of the algorithm and the benchmark data can be found at url{https://github.com/RIVeR-Lab/tentabot}.
机器翻译，仅供参考
