Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

预测，预测了大量的机器人和人为辅助任务。 NASA为了解这些天体的地质和构成的努力在很大程度上取决于机器人臂的使用。当人类与机器人探险家一起工作时，安全性和冗余方面至关重要。此外，机器人臂对于卫星维修和计划的轨道碎片缓解任务至关重要。这项工作的目的是创建一个基于自定义的计算机视觉（CV）的人工神经网络（ANN），该神经网络将能够快速识别从单个（RGB-D）的7度自由（DOF）机器人组的姿势图像 - 就像人类可以轻松识别手臂是否指向一定方向一样。 Sawyer机器人臂用于开发和培训这种智能算法。由于Sawyer的关节空间涵盖了7个维度，因此覆盖整个联合配置空间是一项无法克服的任务。在这项工作中，使用类似于Taguchi方法的正交阵列，以有效地跨越关节空间，以最少的训练图像数量。该生成的数据库用于训练自定义ANN，其准确度平均等于数据库生成使用的最小关节位移步骤的两倍。预先训练的ANN将有助于估计在太空站，航天器和流浪者作为辅助工具或应急计划上使用的机器人操纵器的姿势。

机器人和人类月球着陆是未来NASA任务的重点。精确着陆功能对于确保任务的成功以及着陆器和机组人员的安全至关重要。在进入表面的方法中，存在与危险相对导航相关的多个挑战，以确保安全着陆。本文将重点介绍被动自主危害检测和避免子系统，以对指导系统的可能着陆区进行初步评估。该系统使用单个摄像头和Mobilenetv2神经网络体系结构来检测和辨别安全的着陆点和危险，例如岩石，阴影和陨石坑。然后，来自运动的单眼结构将重新创建表面以提供斜率和粗糙度分析。

这项工作利用MobileNETV2卷积神经网络（CNN）快速，移动检测卫星和拒绝恒星，在混乱的未解决的空间图像中。首先，使用合成卫星图像程序中的图像创建自定义数据库，并在卫星上标记为“卫星阳性”图像的框架框。然后在此数据库上训练CNN，并通过在由真实望远镜图像构建的外部数据集上检查模型的准确性来验证推理。在此过程中，训练有素的CNN提供了一种快速卫星识别方法，可在基于地面的轨道估计中使用。

培训和评估语言模型越来越多地要求构建元数据 - 多样化的策划数据收集，并具有清晰的出处。自然语言提示最近通过将现有的，有监督的数据集转换为多种新颖的预处理任务，突出了元数据策划的好处，从而改善了零击的概括。尽管将这些以数据为中心的方法转化为生物医学语言建模的通用域文本成功，但由于标记的生物医学数据集在流行的数据中心中的代表性大大不足，因此仍然具有挑战性。为了应对这一挑战，我们介绍了BigBio一个由126个以上的生物医学NLP数据集的社区库，目前涵盖12个任务类别和10多种语言。 BigBio通过对数据集及其元数据进行程序化访问来促进可再现的元数据策划，并与当前的平台兼容，以及时工程和端到端的几个/零射击语言模型评估。我们讨论了我们的任务架构协调，数据审核，贡献指南的过程，并概述了两个说明性用例：生物医学提示和大规模，多任务学习的零射门评估。 BigBio是一项持续的社区努力，可在https://github.com/bigscience-workshop/biomedical上获得。

The performance of inertial navigation systems is largely dependent on the stable flow of external measurements and information to guarantee continuous filter updates and bind the inertial solution drift. Platforms in different operational environments may be prevented at some point from receiving external measurements, thus exposing their navigation solution to drift. Over the years, a wide variety of works have been proposed to overcome this shortcoming, by exploiting knowledge of the system current conditions and turning it into an applicable source of information to update the navigation filter. This paper aims to provide an extensive survey of information aided navigation, broadly classified into direct, indirect, and model aiding. Each approach is described by the notable works that implemented its concept, use cases, relevant state updates, and their corresponding measurement models. By matching the appropriate constraint to a given scenario, one will be able to improve the navigation solution accuracy, compensate for the lost information, and uncover certain internal states, that would otherwise remain unobservable.

We consider infinite horizon Markov decision processes (MDPs) with fast-slow structure, meaning that certain parts of the state space move "fast" (and in a sense, are more influential) while other parts transition more "slowly." Such structure is common in real-world problems where sequential decisions need to be made at high frequencies, yet information that varies at a slower timescale also influences the optimal policy. Examples include: (1) service allocation for a multi-class queue with (slowly varying) stochastic costs, (2) a restless multi-armed bandit with an environmental state, and (3) energy demand response, where both day-ahead and real-time prices play a role in the firm's revenue. Models that fully capture these problems often result in MDPs with large state spaces and large effective time horizons (due to frequent decisions), rendering them computationally intractable. We propose an approximate dynamic programming algorithmic framework based on the idea of "freezing" the slow states, solving a set of simpler finite-horizon MDPs (the lower-level MDPs), and applying value iteration (VI) to an auxiliary MDP that transitions on a slower timescale (the upper-level MDP). We also extend the technique to a function approximation setting, where a feature-based linear architecture is used. On the theoretical side, we analyze the regret incurred by each variant of our frozen-state approach. Finally, we give empirical evidence that the frozen-state approach generates effective policies using just a fraction of the computational cost, while illustrating that simply omitting slow states from the decision modeling is often not a viable heuristic.

In the present work we propose an unsupervised ensemble method consisting of oblique trees that can address the task of auto-encoding, namely Oblique Forest AutoEncoders (briefly OF-AE). Our method is a natural extension of the eForest encoder introduced in [1]. More precisely, by employing oblique splits consisting in multivariate linear combination of features instead of the axis-parallel ones, we will devise an auto-encoder method through the computation of a sparse solution of a set of linear inequalities consisting of feature values constraints. The code for reproducing our results is available at https://github.com/CDAlecsa/Oblique-Forest-AutoEncoders.

When robots learn reward functions using high capacity models that take raw state directly as input, they need to both learn a representation for what matters in the task -- the task ``features" -- as well as how to combine these features into a single objective. If they try to do both at once from input designed to teach the full reward function, it is easy to end up with a representation that contains spurious correlations in the data, which fails to generalize to new settings. Instead, our ultimate goal is to enable robots to identify and isolate the causal features that people actually care about and use when they represent states and behavior. Our idea is that we can tune into this representation by asking users what behaviors they consider similar: behaviors will be similar if the features that matter are similar, even if low-level behavior is different; conversely, behaviors will be different if even one of the features that matter differs. This, in turn, is what enables the robot to disambiguate between what needs to go into the representation versus what is spurious, as well as what aspects of behavior can be compressed together versus not. The notion of learning representations based on similarity has a nice parallel in contrastive learning, a self-supervised representation learning technique that maps visually similar data points to similar embeddings, where similarity is defined by a designer through data augmentation heuristics. By contrast, in order to learn the representations that people use, so we can learn their preferences and objectives, we use their definition of similarity. In simulation as well as in a user study, we show that learning through such similarity queries leads to representations that, while far from perfect, are indeed more generalizable than self-supervised and task-input alternatives.

While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Challenge aimed to fast track the development of autonomous exploration systems by evaluating their performance in real-world underground search-and-rescue scenarios. Subterranean environments present a plethora of challenges for robotic systems, such as limited communications, complex topology, visually-degraded sensing, and harsh terrain. The presented solution enables long-term autonomy with minimal human supervision by combining a powerful and independent single-agent autonomy stack, with higher level mission management operating over a flexible mesh network. The autonomy suite deployed on quadruped and wheeled robots was fully independent, freeing the human supervision to loosely supervise the mission and make high-impact strategic decisions. We also discuss lessons learned from fielding our system at the SubT Final Event, relating to vehicle versatility, system adaptability, and re-configurable communications.