先前创建了以最小消息长度（MML）原理为指导的用于归纳推理的软件库。它包含统计模型的各种（面向对象的）类和子类，可用于从机器学习问题中给定的数据集中推断模型。在这里，在库中考虑并实现了统计模型的转换，以便从面向对象的编程和数学观点具有理想的属性。定义了进行此类转换所需的类功能的子类。

用于评估人工数据的因果模型发现的两个最常用的标准是从真实模型到学习模型的编辑距离和kullback-Leibler分歧。这两个度量都最大衡奖励真实模型。但是，我们认为他们既不充分辨别判断虚假模型的相对优点。例如，编辑距离未能区分强大和弱概率依赖关系。另一方面，kl发散同样地奖励所有统计上等同的模型，无论其不同的因果索赔如何。我们提出了一种增强的KL发散，我们称之为因果KL（CKL），这考虑了区分了观测到等效模型的因果关系。结果显示为CKL的三种变体，显示出在实践中运作良好的因果kl。

Current Virtual Reality (VR) environments lack the rich haptic signals that humans experience during real-life interactions, such as the sensation of texture during lateral movement on a surface. Adding realistic haptic textures to VR environments requires a model that generalizes to variations of a user's interaction and to the wide variety of existing textures in the world. Current methodologies for haptic texture rendering exist, but they usually develop one model per texture, resulting in low scalability. We present a deep learning-based action-conditional model for haptic texture rendering and evaluate its perceptual performance in rendering realistic texture vibrations through a multi part human user study. This model is unified over all materials and uses data from a vision-based tactile sensor (GelSight) to render the appropriate surface conditioned on the user's action in real time. For rendering texture, we use a high-bandwidth vibrotactile transducer attached to a 3D Systems Touch device. The result of our user study shows that our learning-based method creates high-frequency texture renderings with comparable or better quality than state-of-the-art methods without the need for learning a separate model per texture. Furthermore, we show that the method is capable of rendering previously unseen textures using a single GelSight image of their surface.

Robotics software is pushing the limits of software engineering practice. The 3rd International Workshop on Robotics Software Engineering held a panel on "the best practices for robotic software engineering". This article shares the key takeaways that emerged from the discussion among the panelists and the workshop, ranging from architecting practices at the NASA/Caltech Jet Propulsion Laboratory, model-driven development at Bosch, development and testing of autonomous driving systems at Waymo, and testing of robotics software at XITASO. Researchers and practitioners can build on the contents of this paper to gain a fresh perspective on their activities and focus on the most pressing practices and challenges in developing robotics software today.

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.

We present a method for controlling a swarm using its spectral decomposition -- that is, by describing the set of trajectories of a swarm in terms of a spatial distribution throughout the operational domain -- guaranteeing scale invariance with respect to the number of agents both for computation and for the operator tasked with controlling the swarm. We use ergodic control, decentralized across the network, for implementation. In the DARPA OFFSET program field setting, we test this interface design for the operator using the STOMP interface -- the same interface used by Raytheon BBN throughout the duration of the OFFSET program. In these tests, we demonstrate that our approach is scale-invariant -- the user specification does not depend on the number of agents; it is persistent -- the specification remains active until the user specifies a new command; and it is real-time -- the user can interact with and interrupt the swarm at any time. Moreover, we show that the spectral/ergodic specification of swarm behavior degrades gracefully as the number of agents goes down, enabling the operator to maintain the same approach as agents become disabled or are added to the network. We demonstrate the scale-invariance and dynamic response of our system in a field relevant simulator on a variety of tactical scenarios with up to 50 agents. We also demonstrate the dynamic response of our system in the field with a smaller team of agents. Lastly, we make the code for our system available.

When comparing approximate Gaussian process (GP) models, it can be helpful to be able to generate data from any GP. If we are interested in how approximate methods perform at scale, we may wish to generate very large synthetic datasets to evaluate them. Na\"{i}vely doing so would cost \(\mathcal{O}(n^3)\) flops and \(\mathcal{O}(n^2)\) memory to generate a size \(n\) sample. We demonstrate how to scale such data generation to large \(n\) whilst still providing guarantees that, with high probability, the sample is indistinguishable from a sample from the desired GP.

Effective force modulation during tissue manipulation is important for ensuring safe robot-assisted minimally invasive surgery (RMIS). Strict requirements for in-vivo distal force sensing have led to prior sensor designs that trade off ease of manufacture and integration against force measurement accuracy along the tool axis. These limitations have made collecting high-quality 3-degree-of-freedom (3-DoF) bimanual force data in RMIS inaccessible to researchers. We present a modular and manufacturable 3-DoF force sensor that integrates easily with an existing RMIS tool. We achieve this by relaxing biocompatibility and sterilizability requirements while utilizing commercial load cells and common electromechanical fabrication techniques. The sensor has a range of +-5 N axially and +-3 N laterally with average root mean square errors(RMSEs) of below 0.15 N in all directions. During teleoperated mock tissue manipulation tasks, a pair of jaw-mounted sensors achieved average RMSEs of below 0.15 N in all directions. For grip force, it achieved an RMSE of 0.156 N. The sensor has sufficient accuracy within the range of forces found in delicate manipulation tasks, with potential use in bimanual haptic feedback and robotic force control. As an open-source design, the sensors can be adapted to suit additional robotic applications outside of RMIS.

Learning from demonstration (LfD) is a proven technique to teach robots new skills. Data quality and quantity play a critical role in LfD trained model performance. In this paper we analyze the effect of enhancing an existing teleoperation data collection system with real-time haptic feedback; we observe improvements in the collected data throughput and its quality for model training. Our experiment testbed was a mobile manipulator robot that opened doors with latch handles. Evaluation of teleoperated data collection on eight real world conference room doors found that adding the haptic feedback improved the data throughput by 6%. We additionally used the collected data to train six image-based deep imitation learning models, three with haptic feedback and three without it. These models were used to implement autonomous door-opening with the same type of robot used during data collection. Our results show that a policy from a behavior cloning model trained with haptic data performed on average 11% better than its counterpart with no haptic feedback data, indicating that haptic feedback resulted in collection of a higher quality dataset.

功能配准算法表示点云为函数（例如，空间占用场），避免了常规最小二乘Quares注册算法中不可靠的对应估计。但是，现有的功能注册算法在计算上很昂贵。此外，在基于CAD模型的对象本地化等任务中，必须使用未知量表的注册能力，但是功能注册中没有这种支持。在这项工作中，我们提出了一种比例不变的线性时间复杂性功能配准算法。我们通过使用正顺序基函数在功能之间的L2距离之间有效地近似实现线性时间复杂性。正统基函数的使用导致与最小二乘配准兼容的公式。受益于最小二乘的公式，我们使用翻译反转不变测量的理论来解除尺度估计，从而实现规模不变的注册。我们在标准的3D注册基准上评估了所提出的算法，称为FLS（功能最小二乘），显示FLS的数量级比最先进的功能配准算法快，而无需损害准确性和鲁棒性。 FLS还胜过基于最小二乘的最小二乘注册算法，其精度和鲁棒性具有已知和未知量表。最后，我们证明将FLS应用于具有不同密度和部分重叠的寄存点云，同一类别中不同对象的点云以及带有嘈杂RGB-D测量值的真实世界对象的点云。