机器人相关学术速递[12.9]

cs.RO机器人相关，共计9篇

【1】 CoMPS: Continual Meta Policy Search 标题：COMPS：连续元策略搜索 链接：https://arxiv.org/abs/2112.04467

作者：Glen Berseth,Zhiwei Zhang,Grace Zhang,Chelsea Finn,Sergey Levine 备注：23 pages, under review 摘要：我们开发了一种新的持续元学习方法来应对顺序多任务学习中的挑战。在此设置中，代理的目标是在任何任务序列中快速获得高回报。先前的元强化学习算法在加速新任务的获取方面已显示出良好的效果。但是，它们需要在训练期间访问所有任务。除了简单地将过去的经验转移到新任务之外，我们的目标是设计出能够学会学习的持续强化学习算法，利用他们在以前任务中的经验更快地学习新任务。我们引入了一种新的方法，即连续元策略搜索（CoMPS），该方法通过以增量方式对每个任务进行元训练来消除这一限制，而无需重新访问以前的任务。CoMPS不断重复两个子例程：使用RL学习新任务，使用RL的经验执行完全离线元学习，为后续任务学习做好准备。我们发现，在几个具有挑战性的连续控制任务序列上，CoMPS优于先前的连续学习和非策略元强化方法。 摘要：We develop a new continual meta-learning method to address challenges in sequential multi-task learning. In this setting, the agent's goal is to achieve high reward over any sequence of tasks quickly. Prior meta-reinforcement learning algorithms have demonstrated promising results in accelerating the acquisition of new tasks. However, they require access to all tasks during training. Beyond simply transferring past experience to new tasks, our goal is to devise continual reinforcement learning algorithms that learn to learn, using their experience on previous tasks to learn new tasks more quickly. We introduce a new method, continual meta-policy search (CoMPS), that removes this limitation by meta-training in an incremental fashion, over each task in a sequence, without revisiting prior tasks. CoMPS continuously repeats two subroutines: learning a new task using RL and using the experience from RL to perform completely offline meta-learning to prepare for subsequent task learning. We find that CoMPS outperforms prior continual learning and off-policy meta-reinforcement methods on several sequences of challenging continuous control tasks.

【2】 Geometry-Aware Fruit Grasping Estimation for Robotic Harvesting in Orchards 标题：果园机器人采摘中基于几何感知的果实抓取估计 链接：https://arxiv.org/abs/2112.04363

作者：Hanwen Kang,Xing Wang,Chao Chen 摘要：田间机器人收获是近年来农业发展中一项很有前途的技术。在自然果园收获水果之前，机器人识别和定位水果是至关重要的。然而，果园采摘机器人的工作空间是复杂的：许多水果被树枝和树叶遮挡。在进行操作之前，估计每个水果的正确抓取姿势是很重要的。在这项研究中，提出了一种几何感知网络A3N，用于使用来自RGB-D相机的颜色和几何感知数据执行端到端实例分割和抓取估计。此外，利用工作空间几何建模辅助机器人操作。此外，我们实施了一种全局到局部的扫描策略，该策略使机器人能够使用两个消费者级RGB-D摄像头在田间环境中准确识别和检索水果。在实验中，我们还对该网络的准确性和鲁棒性进行了综合评估。实验结果表明，A3N的实例分割精度为0.873，平均计算时间为35ms，抓取估计的中心和方向平均精度分别为0.61cm和4.8$^{\circ}$。总体而言，该机器人系统利用全球到本地扫描和A3N，在田间收割试验中获得了70%-85%的收割成功率。 摘要：Field robotic harvesting is a promising technique in recent development of agricultural industry. It is vital for robots to recognise and localise fruits before the harvesting in natural orchards. However, the workspace of harvesting robots in orchards is complex: many fruits are occluded by branches and leaves. It is important to estimate a proper grasping pose for each fruit before performing the manipulation. In this study, a geometry-aware network, A3N, is proposed to perform end-to-end instance segmentation and grasping estimation using both color and geometry sensory data from a RGB-D camera. Besides, workspace geometry modelling is applied to assist the robotic manipulation. Moreover, we implement a global-to-local scanning strategy, which enables robots to accurately recognise and retrieve fruits in field environments with two consumer-level RGB-D cameras. We also evaluate the accuracy and robustness of proposed network comprehensively in experiments. The experimental results show that A3N achieves 0.873 on instance segmentation accuracy, with an average computation time of 35 ms. The average accuracy of grasping estimation is 0.61 cm and 4.8$^{\circ}$ in centre and orientation, respectively. Overall, the robotic system that utilizes the global-to-local scanning and A3N, achieves success rate of harvesting ranging from 70\% - 85\% in field harvesting experiments.

【3】 Transformer based trajectory prediction 标题：基于Transformer的轨迹预测 链接：https://arxiv.org/abs/2112.04350

作者：Aleksey Postnikov,Aleksander Gamayunov,Gonzalo Ferrer 摘要：为了规划一条安全有效的路线，一辆自动驾驶汽车应该预测周围其他代理的未来运动。运动预测是一项极具挑战性的任务，近年来受到了研究界的广泛关注。在这项工作中，我们提出了一个简单而强大的基于Transformer神经网络的不确定性感知运动预测基线，它在域变化的情况下显示了其有效性。虽然易于实施，所提出的方法实现了竞争性能，排名1美元^ ^ {St} $ 2021班车辆运动预测竞争。 摘要：To plan a safe and efficient route, an autonomous vehicle should anticipate future motions of other agents around it. Motion prediction is an extremely challenging task which recently gained significant attention of the research community. In this work, we present a simple and yet strong baseline for uncertainty aware motion prediction based purely on transformer neural networks, which has shown its effectiveness in conditions of domain change. While being easy-to-implement, the proposed approach achieves competitive performance and ranks 1$^{st}$ on the 2021 Shifts Vehicle Motion Prediction Competition.

【4】 iRoPro: An interactive Robot Programming Framework 标题：iRoPro：一种交互式机器人编程框架 链接：https://arxiv.org/abs/2112.04289

作者：Ying Siu Liang,Damien Pellier,Humbert Fiorino,Sylvie Pesty 备注：None 摘要：从制造环境到个人住宅，终端用户任务的巨大多样性使得用于通用应用的预编程机器人极具挑战性。事实上，从头开始教机器人新的动作，这些动作可以重复用于以前看不见的任务，这仍然是一项艰巨的挑战，通常留给机器人专家来完成。在这项工作中，我们介绍了iRoPro，一个交互式机器人编程框架，它允许几乎没有技术背景的最终用户教授机器人新的可重用动作。我们将演示编程和自动规划技术相结合，允许用户通过动觉演示教授新动作来构建机器人的知识库。这些操作被概括并与任务计划器一起重用，以解决用户定义的以前未发现的问题。我们将iRoPro作为一个端到端系统在巴克斯特研究机器人上实现，通过演示用户可以通过图形用户界面进行定制以适应其特定用例，从而同时教授低级和高级动作。为了评估我们的方法的可行性，我们首先进行了预设计实验，以更好地理解用户对相关概念的采用以及建议的机器人编程过程。我们将结果与后设计实验进行比较，在后设计实验中，我们进行了一项用户研究，以验证我们的方法在实际最终用户中的可用性。总的来说，我们展示了具有不同编程水平和教育背景的用户可以轻松地学习和使用iRoPro及其机器人编程过程。 摘要：The great diversity of end-user tasks ranging from manufacturing environments to personal homes makes pre-programming robots for general purpose applications extremely challenging. In fact, teaching robots new actions from scratch that can be reused for previously unseen tasks remains a difficult challenge and is generally left up to robotics experts. In this work, we present iRoPro, an interactive Robot Programming framework that allows end-users with little to no technical background to teach a robot new reusable actions. We combine Programming by Demonstration and Automated Planning techniques to allow the user to construct the robot's knowledge base by teaching new actions by kinesthetic demonstration. The actions are generalised and reused with a task planner to solve previously unseen problems defined by the user. We implement iRoPro as an end-to-end system on a Baxter Research Robot to simultaneously teach low- and high-level actions by demonstration that the user can customise via a Graphical User Interface to adapt to their specific use case. To evaluate the feasibility of our approach, we first conducted pre-design experiments to better understand the user's adoption of involved concepts and the proposed robot programming process. We compare results with post-design experiments, where we conducted a user study to validate the usability of our approach with real end-users. Overall, we showed that users with different programming levels and educational backgrounds can easily learn and use iRoPro and its robot programming process.

【5】 Radar Occupancy Prediction with Lidar Supervision while Preserving Long-Range Sensing and Penetrating Capabilities 标题：保持远程感知和穿透能力的激光雷达监视雷达占有率预测 链接：https://arxiv.org/abs/2112.04282

作者：Pou-Chun Kung,Chieh-Chih Wang,Wen-Chieh Lin 摘要：雷达通过在不同天气条件下实现远程传感，显示了自主驾驶的巨大潜力。但由于雷达噪声的存在，雷达也是一种极具挑战性的传感方式。最近的工作在利用激光雷达标签监督对雷达图像中的空闲空间和占用空间进行分类方面取得了巨大进展。然而，还有几个问题没有解决。首先，激光雷达的探测范围限制了探测结果的探测距离。其次，由于两个传感器之间的物理传感差异，激光雷达会降低结果的性能。例如，一些激光雷达可见的物体对雷达是不可见的，而由于雷达的穿透能力，激光雷达扫描中遮挡的一些物体在雷达图像中是可见的。这些感知差异分别导致假阳性和穿透能力退化。针对这一问题，本文提出了训练数据预处理和极轴滑动窗口推理的方法。数据预处理旨在减少激光雷达扫描中雷达不可见测量的影响。极性滑动窗口推理旨在通过将近距离训练网络应用于远程区域来解决有限的传感范围问题。我们建议使用极坐标表示来减少远距离和近距离数据之间的形状差异，而不是使用普通的笛卡尔表示。我们发现，将近距离训练网络扩展到极坐标空间中的长距离区域推理，其IoU比笛卡尔空间中的IoU好4.2倍。此外，极滑动窗口推理通过改变推理区域的视点来保持雷达的穿透能力，这使得一些被遮挡的测量对于预训练网络来说似乎是不被遮挡的。 摘要：Radar shows great potential for autonomous driving by accomplishing long-range sensing under diverse weather conditions. But radar is also a particularly challenging sensing modality due to the radar noises. Recent works have made enormous progress in classifying free and occupied spaces in radar images by leveraging lidar label supervision. However, there are still several unsolved issues. Firstly, the sensing distance of the results is limited by the sensing range of lidar. Secondly, the performance of the results is degenerated by lidar due to the physical sensing discrepancies between the two sensors. For example, some objects visible to lidar are invisible to radar, and some objects occluded in lidar scans are visible in radar images because of the radar's penetrating capability. These sensing differences cause false positive and penetrating capability degeneration, respectively. In this paper, we propose training data preprocessing and polar sliding window inference to solve the issues. The data preprocessing aims to reduce the effect caused by radar-invisible measurements in lidar scans. The polar sliding window inference aims to solve the limited sensing range issue by applying a near-range trained network to the long-range region. Instead of using common Cartesian representation, we propose to use polar representation to reduce the shape dissimilarity between long-range and near-range data. We find that extending a near-range trained network to long-range region inference in the polar space has 4.2 times better IoU than in Cartesian space. Besides, the polar sliding window inference can preserve the radar penetrating capability by changing the viewpoint of the inference region, which makes some occluded measurements seem non-occluded for a pretrained network.

【6】 Specializing Versatile Skill Libraries using Local Mixture of Experts 标题：利用本地混合专家实现多功能技能库的专业化 链接：https://arxiv.org/abs/2112.04216

作者：Onur Celik,Dongzhuoran Zhou,Ge Li,Philipp Becker,Gerhard Neumann 备注：published at CoRL 2021 London 摘要：机器人学的一个长期梦想是为机器人配备与人类的多功能性和精确性相匹配的技能。例如，在打乒乓球时，机器人应该能够以各种方式将球返回，同时精确地将球放置在所需位置。对这种多功能行为建模的常用方法是使用混合专家（MoE）模型，其中每个专家都是一个上下文运动原语。然而，学习这样的MOE是一个挑战，因为大多数目标迫使模型覆盖整个上下文空间，这会阻止原语的专门化，从而导致相当低质量的组件。从最大熵强化学习（RL）开始，我们将目标分解为优化每个混合成分的单个下限。此外，我们引入了一个课程，允许组件关注本地上下文区域，使模型能够学习高度准确的技能表示。为此，我们使用与专家原语联合适应的局部上下文分布。我们的下限提倡迭代添加新组件，其中新组件将集中在当前MoE未涵盖的本地上下文区域。这种局部和增量学习产生了高精度和多功能性的模块化MoE模型，在该模型中，可以通过动态添加更多组件来扩展这两种特性。我们通过广泛的消融和两个具有挑战性的模拟机器人技能学习任务来证明这一点。我们将我们取得的绩效与LaDiPS和HIREP进行比较，这是一种用于学习不同技能的已知分层策略搜索方法。 摘要：A long-cherished vision in robotics is to equip robots with skills that match the versatility and precision of humans. For example, when playing table tennis, a robot should be capable of returning the ball in various ways while precisely placing it at the desired location. A common approach to model such versatile behavior is to use a Mixture of Experts (MoE) model, where each expert is a contextual motion primitive. However, learning such MoEs is challenging as most objectives force the model to cover the entire context space, which prevents specialization of the primitives resulting in rather low-quality components. Starting from maximum entropy reinforcement learning (RL), we decompose the objective into optimizing an individual lower bound per mixture component. Further, we introduce a curriculum by allowing the components to focus on a local context region, enabling the model to learn highly accurate skill representations. To this end, we use local context distributions that are adapted jointly with the expert primitives. Our lower bound advocates an iterative addition of new components, where new components will concentrate on local context regions not covered by the current MoE. This local and incremental learning results in a modular MoE model of high accuracy and versatility, where both properties can be scaled by adding more components on the fly. We demonstrate this by an extensive ablation and on two challenging simulated robot skill learning tasks. We compare our achieved performance to LaDiPS and HiREPS, a known hierarchical policy search method for learning diverse skills.

【7】 An Investigation of Drivers' Dynamic Situational Trust in Conditionally Automated Driving 标题：条件自动驾驶中驾驶员动态情境信任的研究 链接：https://arxiv.org/abs/2112.04095

作者：Jackie Ayoub,Lilit Avetisyan,Mustapha Makki,Feng Zhou 摘要：了解信任是如何随着时间的推移而建立的至关重要，因为信任在自动化车辆（AV）的接受和采用中起着重要作用。本研究旨在探讨在接管过渡期间，系统性能和参与者的信任前提对动态情境信任的影响。我们使用自我报告和行为测量评估了42名参与者的动态情境信任，同时观看了30段带有接管场景的视频。该研究采用3×2混合受试者设计，受试者内变量为系统性能（即95%、80%和70%的准确度水平），受试者间变量为参与者信任的前提条件（即过度信任和不信任）。我们的结果表明，参与者迅速调整了他们的自我报告情境信任（SST）水平，这与两种信任前提下系统性能的不同准确性水平一致。然而，参与者的行为情境信任（BST）受到不同准确度水平的信任前提的影响。例如，与信任不足前提相比，过度信任前提显著增加了协议分数。与过度信任前提相比，欠信任前提显著降低了切换分数。这些结果对设计条件AVs的车内信任校准系统具有重要意义。 摘要：Understanding how trust is built over time is essential, as trust plays an important role in the acceptance and adoption of automated vehicles (AVs). This study aimed to investigate the effects of system performance and participants' trust preconditions on dynamic situational trust during takeover transitions. We evaluated the dynamic situational trust of 42 participants using both self-reported and behavioral measures while watching 30 videos with takeover scenarios. The study was a 3 by 2 mixed-subjects design, where the within-subjects variable was the system performance (i.e., accuracy levels of 95\%, 80\%, and 70\%) and the between-subjects variable was the preconditions of the participants' trust (i.e., overtrust and undertrust). Our results showed that participants quickly adjusted their self-reported situational trust (SST) levels which were consistent with different accuracy levels of system performance in both trust preconditions. However, participants' behavioral situational trust (BST) was affected by their trust preconditions across different accuracy levels. For instance, the overtrust precondition significantly increased the agreement fraction compared to the undertrust precondition. The undertrust precondition significantly decreased the switch fraction compared to the overtrust precondition. These results have important implications for designing an in-vehicle trust calibration system for conditional AVs.

【8】 Learning to Localize, Grasp, and Hand Over Unmodified Surgical Needles 标题：学习定位、抓取和交出未改装的外科针头 链接：https://arxiv.org/abs/2112.04071

作者：Albert Wilcox,Justin Kerr,Brijen Thananjeyan,Jeffrey Ichnowski,Minho Hwang,Samuel Paradis,Danyal Fer,Ken Goldberg 备注：8 pages, 7 figures. First two authors contributed equally 摘要：机器人手术助手（RSA）通常由专家外科医生执行微创手术。然而，长时间的手术充满了繁琐和重复的任务，如缝合，可能会导致外科医生疲劳，促使缝合自动化。由于薄反射针的视觉跟踪非常具有挑战性，先前的工作已经用非反射对比漆对针进行了修改。作为在不修改针的情况下实现缝合子任务自动化的一步，我们提出了休斯顿：未经修改的、外科手术的、工具阻塞的针的移交，这是一个问题和算法，该问题和算法使用学习到的主动感测策略和立体相机将针定位并对齐到另一只手臂的可见和可接近姿势。为了补偿机器人定位和针感知误差，该算法随后执行使用多个摄像头的高精度抓取运动。在使用达芬奇研究工具包（dVRK）的物理实验中，休斯顿成功地通过了未经修改的手术针，成功率为96.7%，并且能够在失败前平均32.4次按顺序在手臂之间进行切换。在训练中从未见过的针头上，休斯顿取得了75-92.9%的成功率。据我们所知，这项工作是第一次研究未经修改的手术针的移交。看见https://tinyurl.com/houston-surgery 其他材料。 摘要：Robotic Surgical Assistants (RSAs) are commonly used to perform minimally invasive surgeries by expert surgeons. However, long procedures filled with tedious and repetitive tasks such as suturing can lead to surgeon fatigue, motivating the automation of suturing. As visual tracking of a thin reflective needle is extremely challenging, prior work has modified the needle with nonreflective contrasting paint. As a step towards automation of a suturing subtask without modifying the needle, we propose HOUSTON: Handoff of Unmodified, Surgical, Tool-Obstructed Needles, a problem and algorithm that uses a learned active sensing policy with a stereo camera to localize and align the needle into a visible and accessible pose for the other arm. To compensate for robot positioning and needle perception errors, the algorithm then executes a high-precision grasping motion that uses multiple cameras. In physical experiments using the da Vinci Research Kit (dVRK), HOUSTON successfully passes unmodified surgical needles with a success rate of 96.7% and is able to perform handover sequentially between the arms 32.4 times on average before failure. On needles unseen in training, HOUSTON achieves a success rate of 75 - 92.9%. To our knowledge, this work is the first to study handover of unmodified surgical needles. See https://tinyurl.com/houston-surgery for additional materials.

【9】 Active Bayesian Multi-class Mapping from Range and Semantic Segmentation Observations 标题：基于范围和语义分割观测的主动贝叶斯多类映射 链接：https://arxiv.org/abs/2112.04063

作者：Arash Asgharivaskasi,Nikolay Atanasov 备注：arXiv admin note: substantial text overlap with arXiv:2101.01831 摘要：由于大量廉价的传感和边缘计算解决方案，在非结构化和未知环境中对机器人探索的需求最近大幅增长。为了更接近完全自主，机器人需要实时处理测量流，这就需要有效的探索策略。基于信息的勘探技术，如Cauchy-Schwarz二次互信息（CSQMI）和快速Shannon互信息（FSMI），已经成功地实现了具有距离测量的主动二进制占用映射。然而，当我们设想机器人执行语义上有意义的对象指定的复杂任务时，有必要在测量、地图表示和探索目标中捕获语义类别。在这项工作中，我们提出了一种利用距离类别度量的贝叶斯多类映射算法，以及多类映射和度量之间香农互信息的一个封闭形式的有效可计算下界。该界限允许快速评估许多潜在的机器人轨迹，以便进行自主探索和测绘。此外，我们开发了一种基于八叉树数据结构的具有语义标签的三维环境的压缩表示，其中每个体素在对象类上保持分类分布。提出的三维表示有助于使用距离类别观测射线的游程编码（RLE）快速计算语义八达图和测量值之间的香农互信息。我们将我们的方法与基于前沿和FSMI的探索进行了比较，并将其应用于各种模拟和真实实验中。 摘要：The demand for robot exploration in unstructured and unknown environments has recently grown substantially thanks to the host of inexpensive sensing and edge-computing solutions. In order to come closer to full autonomy, robots need to process the measurement stream in real-time, which calls for efficient exploration strategies. Information-based exploration techniques, such as Cauchy-Schwarz quadratic mutual information (CSQMI) and fast Shannon mutual information (FSMI), have successfully achieved active binary occupancy mapping with range measurements. However, as we envision robots performing complex tasks specified with semantically meaningful objects, it is necessary to capture semantic categories in the measurements, map representation, and exploration objective. In this work we propose a Bayesian multi-class mapping algorithm utilizing range-category measurements, as well as a closed-form efficiently computable lower bound for the Shannon mutual information between the multi-class map and the measurements. The bound allows rapid evaluation of many potential robot trajectories for autonomous exploration and mapping. Furthermore, we develop a compressed representation of 3-D environments with semantic labels based on OcTree data structure, where each voxel maintains a categorical distribution over object classes. The proposed 3-D representation facilitates fast computation of Shannon mutual information between the semantic Octomap and the measurements using Run-Length Encoding (RLE) of range-category observation rays. We compare our method against frontier-based and FSMI exploration and apply it in a variety of simulated and real-world experiments.

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