We study algorithms for detecting and including glass objects in an optimization-based Simultaneous Localization and Mapping (SLAM) algorithm in this work. When LiDAR data is the primary exteroceptive sensory input, glass objects are not correctly registered. This occurs as the incident light primarily passes through the glass objects or reflects away from the source, resulting in inaccurate range measurements for glass surfaces. Consequently, the localization and mapping performance is impacted, thereby rendering navigation in such environments unreliable. Optimization-based SLAM solutions, which are also referred to as Graph SLAM, are widely regarded as state of the art. In this paper, we utilize a simple and computationally inexpensive glass detection scheme for detecting glass objects and present the methodology to incorporate the identified objects into the occupancy grid maintained by such an algorithm (Google Cartographer). We develop both local (submap level) and global algorithms for achieving the objective mentioned above and compare the maps produced by our method with those produced by an existing algorithm that utilizes particle filter based SLAM.

By transferring knowledge from large, diverse, task-agnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or with small task-specific datasets to a high level of performance. While this capability has been demonstrated in other fields such as computer vision, natural language processing or speech recognition, it remains to be shown in robotics, where the generalization capabilities of the models are particularly critical due to the difficulty of collecting real-world robotic data. We argue that one of the keys to the success of such general robotic models lies with open-ended task-agnostic training, combined with high-capacity architectures that can absorb all of the diverse, robotic data. In this paper, we present a model class, dubbed Robotics Transformer, that exhibits promising scalable model properties. We verify our conclusions in a study of different model classes and their ability to generalize as a function of the data size, model size, and data diversity based on a large-scale data collection on real robots performing real-world tasks. The project's website and videos can be found at robotics-transformer.github.io

Deep Reinforcement Learning (DRL) has the potential to be used for synthesizing feedback controllers (agents) for various complex systems with unknown dynamics. These systems are expected to satisfy diverse safety and liveness properties best captured using temporal logic. In RL, the reward function plays a crucial role in specifying the desired behaviour of these agents. However, the problem of designing the reward function for an RL agent to satisfy complex temporal logic specifications has received limited attention in the literature. To address this, we provide a systematic way of generating rewards in real-time by using the quantitative semantics of Signal Temporal Logic (STL), a widely used temporal logic to specify the behaviour of cyber-physical systems. We propose a new quantitative semantics for STL having several desirable properties, making it suitable for reward generation. We evaluate our STL-based reinforcement learning mechanism on several complex continuous control benchmarks and compare our STL semantics with those available in the literature in terms of their efficacy in synthesizing the controller agent. Experimental results establish our new semantics to be the most suitable for synthesizing feedback controllers for complex continuous dynamical systems through reinforcement learning.

个性化联合学习允许分布式系统中的客户培训针对其独特本地数据量身定制的神经网络，同时利用其他客户的信息。但是，客户的模型在培训和测试阶段都容易受到攻击。在本文中，我们解决了对抗性客户在测试时间制定逃避攻击的问题，以欺骗其他客户。例如，对手可能旨在欺骗垃圾邮件过滤器和推荐系统，并接受了个性化联合学习培训以获得金钱收益。根据分布式学习的方法，对抗客户具有不同程度的个性化，从而导致“灰色盒子”情况。我们是第一个表征这种内部逃避攻击对不同学习方法的可转移性，并根据客户数据的个性化程度和相似性分析模型准确性和鲁棒性之间的权衡。我们介绍了一种防御机制PFEDDEF，该机制进行了个性化的联合对手培训，同时尊重抑制对抗性培训的客户的资源限制。总体而言，与联邦对抗训练相比，PFEDDEF将相对灰色的对抗鲁棒性提高62％，即使在有限的系统资源下也表现良好。

来自给定时间序列数据的相关矩阵的预测有多种针对一系列问题的应用程序，例如从峰值数据推断神经元连接，从表达数据中推论基因之间的因果关系，并发现气候变化的长空间范围影响。预测相关矩阵的传统方法利用了基础网络所有节点的时间序列数据。在这里，我们使用监督的机器学习技术来预测一些随机选择节点的有限时间序列信息的整个系统的相关矩阵。来自模型的预测的准确性证实，整个系统的一个子集的有限时间序列足以做出良好的相关矩阵预测。此外，使用无监督的学习算法，我们提供了对模型预测成功的见解。最后，我们将此处开发的机器学习模型应用于现实世界数据集。

本文介绍了INLG 2022的Hinglisheval挑战的系统描述。该任务的目的是研究影响代码混合文本生成系统质量的因素。该任务分为两个子任务，质量评级预测和注释者的分歧预测预测。我们尝试使用句子级嵌入来解决这些任务，这些任务是通过平均汇总我们文本中所有输入令牌的上下文化词嵌入而获得的。我们在为各自任务生成的嵌入式外面尝试了各种分类器。我们表现最好的系统在子任务B上排名第一，在子任务A上排名第三。

我们展示了在文本上预先培训的神经网络，并在代码上进行微调解决数学问题，通过程序合成解决了数学问题。我们将问题转化为编程任务，自动生成程序，然后从MIT的大型数学课程（单变微积分18.01，多变量计算18.02，微分方程18.03，概率和统计介绍18.05，概率和统计概要和统计概要和统计概要和统计概要和统计概要和统计概要和统计概要和统计概况概要和统计概要和统计概要和统计概率概述的大学级问题。 18.06，以及计算机科学的数学6.042）以及数学数据集的问题（在预先发生的地板，代数，计数和概率，数字理论和前进的问题上），最新数学问题的基准专门用于评估数学推理。我们探索提示生成方法，使变形金刚能够为这些主题生成问题解决程序，包括具有图的解决方案。我们在每个主题中的随机问题上生成正确的答案。我们量化了原始和转型问题之间的差距，并进行了调查以评估所产生的问题的质量和难度。这是在规模上自动解决，等级和生成大学数学课程问题的第一项工作，这代表了高等教育的里程碑。

我们通过使用Openai的Codex进行计划综合解决大学级概率和统计问题，一个在文本上培训并在代码上进行微调的变压器。我们从MIT 18.05的课程问题转换为概率和统计信息和哈佛的Stat110概率转换为编程任务。然后，我们执行生成的代码以获得解决方案。由于这些课程问题在概率地基础上，我们往往的目标是具有Codex生成概率的程序，以模拟大量概率依赖项来计算其解决方案。我们的方法需要提示工程将问题从其原始表格转换为明确的，贸易的表格，导致正确的程序和解决方案。为了估计将原始问题转化为此易易表单所需的工作量，我们衡量了原始和转型问题之间的相似性。我们的工作是第一个推出大学级概率和统计问题的新数据集，并使用大型语言模型的程序综合能力以可扩展方式解决这些问题。

知识依赖任务通常使用两个知识来源：参数，在培训时间和上下文中学到的，作为推理时间的段落给出。要了解模型如何使用这些来源，我们正式化知识冲突问题，其中上下文信息与学到的信息相矛盾。分析流行模型的行为，我们衡量其过度依赖记忆信息（幻觉的原因），并揭示加剧这种行为的重要因素。最后，我们提出了一种简单的方法来减轻对参数知识的过度依赖，这最大限度地减少了幻觉，并提高了分配的推广4％-7％。我们的调查结果表明了从业者评估模型倾向于幻觉而不是阅读的重要性，并表明我们的缓解战略鼓励向不断发展的信息（即时间依赖查询）概括。为鼓励这些做法，我们发布了我们的框架，以产生知识冲突。

We introduce Argoverse 2 (AV2) - a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequences of multimodal data, encompassing high-resolution imagery from seven ring cameras, and two stereo cameras in addition to lidar point clouds, and 6-DOF map-aligned pose. Sequences contain 3D cuboid annotations for 26 object categories, all of which are sufficiently-sampled to support training and evaluation of 3D perception models. The Lidar Dataset contains 20,000 sequences of unlabeled lidar point clouds and map-aligned pose. This dataset is the largest ever collection of lidar sensor data and supports self-supervised learning and the emerging task of point cloud forecasting. Finally, the Motion Forecasting Dataset contains 250,000 scenarios mined for interesting and challenging interactions between the autonomous vehicle and other actors in each local scene. Models are tasked with the prediction of future motion for "scored actors" in each scenario and are provided with track histories that capture object location, heading, velocity, and category. In all three datasets, each scenario contains its own HD Map with 3D lane and crosswalk geometry - sourced from data captured in six distinct cities. We believe these datasets will support new and existing machine learning research problems in ways that existing datasets do not. All datasets are released under the CC BY-NC-SA 4.0 license.