Cartesian impedance control is a type of motion control strategy for robots that improves safety in partially unknown environments by achieving a compliant behavior of the robot with respect to its external forces. This compliant robot behavior has the added benefit of allowing physical human guidance of the robot. In this paper, we propose a C++ implementation of compliance control valid for any torque-commanded robotic manipulator. The proposed controller implements Cartesian impedance control to track a desired end-effector pose. Additionally, joint impedance is projected in the nullspace of the Cartesian robot motion to track a desired robot joint configuration without perturbing the Cartesian motion of the robot. The proposed implementation also allows the robot to apply desired forces and torques to its environment. Several safety features such as filtering, rate limiting, and saturation are included in the proposed implementation. The core functionalities are in a re-usable base library and a Robot Operating System (ROS) ros_control integration is provided on top of that. The implementation was tested with the KUKA LBR iiwa robot and the Franka Emika Robot (Panda) both in simulation and with the physical robots.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Computational fluid dynamics (CFD) is a valuable asset for patient-specific cardiovascular-disease diagnosis and prognosis, but its high computational demands hamper its adoption in practice. Machine-learning methods that estimate blood flow in individual patients could accelerate or replace CFD simulation to overcome these limitations. In this work, we consider the estimation of vector-valued quantities on the wall of three-dimensional geometric artery models. We employ group-equivariant graph convolution in an end-to-end SE(3)-equivariant neural network that operates directly on triangular surface meshes and makes efficient use of training data. We run experiments on a large dataset of synthetic coronary arteries and find that our method estimates directional wall shear stress (WSS) with an approximation error of 7.6% and normalised mean absolute error (NMAE) of 0.4% while up to two orders of magnitude faster than CFD. Furthermore, we show that our method is powerful enough to accurately predict transient, vector-valued WSS over the cardiac cycle while conditioned on a range of different inflow boundary conditions. These results demonstrate the potential of our proposed method as a plugin replacement for CFD in the personalised prediction of hemodynamic vector and scalar fields.

Everting, soft growing vine robots benefit from reduced friction with their environment, which allows them to navigate challenging terrain. Vine robots can use air pouches attached to their sides for lateral steering. However, when all pouches are serially connected, the whole robot can only perform one constant curvature in free space. It must contact the environment to navigate through obstacles along paths with multiple turns. This work presents a multi-segment vine robot that can navigate complex paths without interacting with its environment. This is achieved by a new steering method that selectively actuates each single pouch at the tip, providing high degrees of freedom with few control inputs. A small magnetic valve connects each pouch to a pressure supply line. A motorized tip mount uses an interlocking mechanism and motorized rollers on the outer material of the vine robot. As each valve passes through the tip mount, a permanent magnet inside the tip mount opens the valve so the corresponding pouch is connected to the pressure supply line at the same moment. Novel cylindrical pneumatic artificial muscles (cPAMs) are integrated into the vine robot and inflate to a cylindrical shape for improved bending characteristics compared to other state-of-the art vine robots. The motorized tip mount controls a continuous eversion speed and enables controlled retraction. A final prototype was able to repeatably grow into different shapes and hold these shapes. We predict the path using a model that assumes a piecewise constant curvature along the outside of the multi-segment vine robot. The proposed multi-segment steering method can be extended to other soft continuum robot designs.

Keke AI竞赛介绍了游戏Baba的人造代理竞赛是您 - 像索托班一样的益智游戏，玩家可以创建影响游戏机制的规则。更改规则可能会导致可能是解决方案空间的一部分的其余级别的暂时或永久效应。这些动态规则的性质和游戏的确定性方面为AI构成了一个挑战，即适应各种机械组合以解决一个水平。本文介绍了用于对提交代理进行排名的框架和评估指标，以及样本搜索剂的基线结果。

这项工作扩展了遗传指纹欺骗的先前进步，并引入了多样性和新颖的大师。该系统使用质量多样性进化算法来生成人造印刷的字典，重点是增加数据集对用户的覆盖范围。多样性大师图的重点是生成与以前发现的印刷品未涵盖的用户匹配的解决方案印刷品，而新颖的主版印刷明确地搜索了与以前的印刷品相比，在用户空间中更多的印刷品。我们的多印刷搜索方法在覆盖范围和概括方面都优于奇异的深层印刷，同时保持指纹图像输出的质量。

本文介绍了Aesthetic Bot的实现，这是一个自动化的Twitter帐户，该帐户发布了用户制造或从进化系统生成的小型游戏地图的图像。然后，该机器人提示用户通过图像线程中发布的民意调查进行投票，以获取最令人愉悦的地图。这创建了一个评级系统，该系统允许以无缝集成到用户定期更新的Twitter内容fef中的方式直接与机器人进行交互。在每次投票回合结束时，该机器人从每张地图的投票分布中学习，以模仿设计和视觉美学的用户偏好，以生成将赢得未来投票配对的地图。我们讨论了自机器人生成游戏地图和参与的Twitter用户发布以来发生的持续结果和新兴行为。

这项工作介绍了使用伪层作为费米子决定因素的随机估计量的费米子晶状体理论中基于流动采样的量规均值架构。这是最先进的晶格场理论计算中的默认方法，这使得对流向模型在QCD等理论的实际应用至关重要。还概述了通过标准技术（例如/奇数预处理和HasenBusch分解）来改进基于流的采样方法的方法。提供了二维U（1）和SU（3）具有$ n_f = 2 $ FERMIONS的量规理论的数值演示。

允许合成现实细胞形状的方法可以帮助生成训练数据集，以改善生物医学图像中的细胞跟踪和分割。细胞形状合成的深层生成模型需要对细胞形状进行轻巧和柔性表示。但是，通常使用体素的表示不适合高分辨率形状合成，而多边形网格在建模拓扑变化（例如细胞生长或有丝分裂）时具有局限性。在这项工作中，我们建议使用符号距离功能（SDF）的级别集来表示细胞形状。我们将神经网络优化为3D+时域中任何点的SDF值的隐式神经表示。该模型以潜在代码为条件，从而允许合成新的和看不见的形状序列。我们在生长和分裂的秀丽隐杆线虫细胞上进行定量和质量验证方法，并具有生长的复杂丝虫突起的肺癌细胞。我们的结果表明，合成细胞的形状描述符类似于真实细胞的形状，并且我们的模型能够在3D+时间内生成复杂细胞形状的拓扑合理序列。

我们研究了如何根据PlayTraces有效预测游戏角色。可以通过计算玩家与游戏行为的生成模型（所谓的程序角色）之间的动作协议比率来计算游戏角色。但这在计算上很昂贵，并假设很容易获得适当的程序性格。我们提出了两种用于估计玩家角色的方法，一种是使用定期监督的学习和启动游戏机制的汇总度量的方法，另一种是基于序列学习的序列学习的另一种方法。尽管这两种方法在预测与程序角色一致定义的游戏角色时都具有很高的精度，但它们完全无法预测玩家使用问卷的玩家本身定义的游戏风格。这个有趣的结果突出了使用计算方法定义游戏角色的价值。