Convolutional Neural Networks (CNNs) have proven very effective in image classification and show promise for audio. We use various CNN architectures to classify the soundtracks of a dataset of 70M training videos (5.24 million hours) with 30,871 video-level labels. We examine fully connected Deep Neural Networks (DNNs), AlexNet [1], VGG [2], Inception [3], and ResNet [4]. We investigate varying the size of both training set and label vocabulary, finding that analogs of the CNNs used in image classification do well on our audio classification task, and larger training and label sets help up to a point. A model using embeddings from these classifiers does much better than raw features on the Audio Set [5] Acoustic Event Detection (AED) classification task.

Our earlier research built a virtual shake robot in simulation to study the dynamics of precariously balanced rocks (PBR), which are negative indicators of earthquakes in nature. The simulation studies need validation through physical experiments. For this purpose, we developed Shakebot, a low-cost (under $2,000), open-source shake table to validate simulations of PBR dynamics and facilitate other ground motion experiments. The Shakebot is a custom one-dimensional prismatic robotic system with perception and motion software developed using the Robot Operating System (ROS). We adapted affordable and high-accuracy components from 3D printers, particularly a closed-loop stepper motor for actuation and a toothed belt for transmission. The stepper motor enables the bed to reach a maximum horizontal acceleration of 11.8 m/s^2 (1.2 g), and velocity of 0.5 m/s, when loaded with a 2 kg scale-model PBR. The perception system of the Shakebot consists of an accelerometer and a high frame-rate camera. By fusing camera-based displacements with acceleration measurements, the Shakebot is able to carry out accurate bed velocity estimation. The ROS-based perception and motion software simplifies the transition of code from our previous virtual shake robot to the physical Shakebot. The reuse of the control programs ensures that the implemented ground motions are consistent for both the simulation and physical experiments, which is critical to validate our simulation experiments.

Artificial intelligence (AI) has enormous potential to improve Air Force pilot training by providing actionable feedback to pilot trainees on the quality of their maneuvers and enabling instructor-less flying familiarization for early-stage trainees in low-cost simulators. Historically, AI challenges consisting of data, problem descriptions, and example code have been critical to fueling AI breakthroughs. The Department of the Air Force-Massachusetts Institute of Technology AI Accelerator (DAF-MIT AI Accelerator) developed such an AI challenge using real-world Air Force flight simulator data. The Maneuver ID challenge assembled thousands of virtual reality simulator flight recordings collected by actual Air Force student pilots at Pilot Training Next (PTN). This dataset has been publicly released at Maneuver-ID.mit.edu and represents the first of its kind public release of USAF flight training data. Using this dataset, we have applied a variety of AI methods to separate "good" vs "bad" simulator data and categorize and characterize maneuvers. These data, algorithms, and software are being released as baselines of model performance for others to build upon to enable the AI ecosystem for flight simulator training.

Grasp learning has become an exciting and important topic in robotics. Just a few years ago, the problem of grasping novel objects from unstructured piles of clutter was considered a serious research challenge. Now, it is a capability that is quickly becoming incorporated into industrial supply chain automation. How did that happen? What is the current state of the art in robotic grasp learning, what are the different methodological approaches, and what machine learning models are used? This review attempts to give an overview of the current state of the art of grasp learning research.

Reinforcement learning in partially observable domains is challenging due to the lack of observable state information. Thankfully, learning offline in a simulator with such state information is often possible. In particular, we propose a method for partially observable reinforcement learning that uses a fully observable policy (which we call a state expert) during offline training to improve online performance. Based on Soft Actor-Critic (SAC), our agent balances performing actions similar to the state expert and getting high returns under partial observability. Our approach can leverage the fully-observable policy for exploration and parts of the domain that are fully observable while still being able to learn under partial observability. On six robotics domains, our method outperforms pure imitation, pure reinforcement learning, the sequential or parallel combination of both types, and a recent state-of-the-art method in the same setting. A successful policy transfer to a physical robot in a manipulation task from pixels shows our approach's practicality in learning interesting policies under partial observability.

在这项工作中，我们研究了生成图像模型的性能和评估如何受到其培训数据集的种族组成的影响。通过检查和控制各种培训数据集中的种族分布，我们能够观察不同培训分布对生成的图像质量和生成图像的种族分布的影响。我们的结果表明，生成的图像的种族组成成功地保留了培训数据。但是，我们观察到截断是一种用于在推断过程中生成更高质量图像的技术，加剧了数据中的种族失衡。最后，在检查图像质量与种族之间的关系时，我们发现给定种族的最高可感知的视觉质量图像来自该种族代表性很好的分布，并且注释者始终偏爱白人的生成图像，而不是黑人。

用于机器人操纵的多进球政策学习具有挑战性。先前的成功使用了对象的基于状态的表示或提供了演示数据来促进学习。在本文中，通过对域的高级离散表示形式进行手工编码，我们表明，可以使用来自像素的Q学习来学习达到数十个目标的策略。代理商将学习重点放在更简单的本地政策上，这些政策是通过在抽象空间中进行计划来对其进行测序的。我们将我们的方法与标准的多目标RL基线以及在具有挑战性的块构造域上利用离散表示的其他方法进行了比较。我们发现我们的方法可以构建一百多个不同的块结构，并证明具有新物体的结构向前转移。最后，我们将所学的政策部署在真正的机器人上的模拟中。

基于2D图像的3D对象的推理由于从不同方向查看对象引起的外观差异很大，因此具有挑战性。理想情况下，我们的模型将是对物体姿势变化的不变或等效的。不幸的是，对于2D图像输入，这通常是不可能的，因为我们没有一个先验模型，即在平面外对象旋转下如何改变图像。唯一的$ \ mathrm {so}（3）$ - 当前存在的模型需要点云输入而不是2D图像。在本文中，我们提出了一种基于Icosahedral群卷积的新型模型体系结构，即通过将输入图像投影到iCosahedron上，以$ \ mathrm {so（3）} $中的理由。由于此投影，该模型大致与$ \ mathrm {so}（3）$中的旋转大致相当。我们将此模型应用于对象构成估计任务，并发现它的表现优于合理的基准。

树皮甲虫暴发会极大地影响世界各地的森林生态系统和服务。为了制定有效的森林政策和管理计划，至关重要的是对树木的早期发现至关重要。尽管树皮甲虫的侵扰存在视觉症状，但考虑到冠状叶子变色的树冠和非同质性，这项任务仍然具有挑战性。在这项工作中，提出了一种基于深度学习的方法，以有效地对单个树级别的树皮甲虫攻击的不同阶段进行分类。所提出的方法使用视网膜架构（利用预萃取良好的特征提取主链进行树冠检测）来训练浅子网络，以对无人机（无人驾驶汽车）捕获的图像的不同攻击阶段进行分类。此外，检查了各种数据增强策略以解决类不平衡问题，因此，选择仿射转换是为此目的最有效的。实验评估通过达到98.95％的平均准确性来证明该方法的有效性，使基线方法的表现高约10％。

在自然界中，动物的集体行为（例如飞鸟）由同一物种的个体之间的相互作用主导。但是，对鸟类物种中这种行为的研究是一个复杂的过程，即人类无法使用常规的视觉观察技术（例如自然界的焦点采样）进行。对于鸟类等社会动物，群体形成的机制可以帮助生态学家了解社交线索及其视觉特征随着时间的流逝（例如姿势和形状）之间的关系。但是，恢复飞行鸟类的不同姿势和形状是一个极具挑战性的问题。解决此瓶颈的一种广泛的解决方案是将姿势和形状从2D图像提取到3D对应关系。 3D视觉的最新进展导致了关于3D形状和姿势估计的许多令人印象深刻的作品，每项作品都有不同的利弊。据我们所知，这项工作是首次尝试概述基于单眼视觉的3D鸟重建的最新进展，使计算机视觉和生物学研究人员概述了现有方法，并比较其特征。