下一代物理科学涉及机器人科学家 - 自主物理科学系统，能够在封闭环中实验设计，执行和分析。这样的系统已显示出对科学探索和发现的现实成功，包括首次发现一流的材料。为了构建和使用这些系统，下一代劳动力需要在不同领域的专业知识，包括ML，控制系统，测量科学，材料合成，决策理论等。但是，教育滞后。教育工作者需要一个低成本，易于使用的平台来教授所需的技能。行业还可以使用这样的平台来开发和评估自主物理科学方法论。我们介绍了科学教育的下一代，这是建立低成本自治科学家的套件。该套件在马里兰州大学的两门课程中用于教授本科和研究生自治物理科学。我们以自主模型探索，优化和确定的双重任务来讨论其在课程中的用途及其更大的能力，并以自主实验的“发现”为例。

Autonomous vehicles are suited for continuous area patrolling problems. However, finding an optimal patrolling strategy can be challenging for many reasons. Firstly, patrolling environments are often complex and can include unknown and evolving environmental factors. Secondly, autonomous vehicles can have failures or hardware constraints such as limited battery lives. Importantly, patrolling large areas often requires multiple agents that need to collectively coordinate their actions. In this work, we consider these limitations and propose an approach based on a distributed, model-free deep reinforcement learning based multi-agent patrolling strategy. In this approach, agents make decisions locally based on their own environmental observations and on shared information. In addition, agents are trained to automatically recharge themselves when required to support continuous collective patrolling. A homogeneous multi-agent architecture is proposed, where all patrolling agents have an identical policy. This architecture provides a robust patrolling system that can tolerate agent failures and allow supplementary agents to be added to replace failed agents or to increase the overall patrol performance. This performance is validated through experiments from multiple perspectives, including the overall patrol performance, the efficiency of the battery recharging strategy, the overall robustness of the system, and the agents' ability to adapt to environment dynamics.

Plastic shopping bags that get carried away from the side of roads and tangled on cotton plants can end up at cotton gins if not removed before the harvest. Such bags may not only cause problem in the ginning process but might also get embodied in cotton fibers reducing its quality and marketable value. Therefore, it is required to detect, locate, and remove the bags before cotton is harvested. Manually detecting and locating these bags in cotton fields is labor intensive, time-consuming and a costly process. To solve these challenges, we present application of four variants of YOLOv5 (YOLOv5s, YOLOv5m, YOLOv5l and YOLOv5x) for detecting plastic shopping bags using Unmanned Aircraft Systems (UAS)-acquired RGB (Red, Green, and Blue) images. We also show fixed effect model tests of color of plastic bags as well as YOLOv5-variant on average precision (AP), mean average precision (mAP@50) and accuracy. In addition, we also demonstrate the effect of height of plastic bags on the detection accuracy. It was found that color of bags had significant effect (p < 0.001) on accuracy across all the four variants while it did not show any significant effect on the AP with YOLOv5m (p = 0.10) and YOLOv5x (p = 0.35) at 95% confidence level. Similarly, YOLOv5-variant did not show any significant effect on the AP (p = 0.11) and accuracy (p = 0.73) of white bags, but it had significant effects on the AP (p = 0.03) and accuracy (p = 0.02) of brown bags including on the mAP@50 (p = 0.01) and inference speed (p < 0.0001). Additionally, height of plastic bags had significant effect (p < 0.0001) on overall detection accuracy. The findings reported in this paper can be useful in speeding up removal of plastic bags from cotton fields before harvest and thereby reducing the amount of contaminants that end up at cotton gins.

Continual Learning, also known as Lifelong or Incremental Learning, has recently gained renewed interest among the Artificial Intelligence research community. Recent research efforts have quickly led to the design of novel algorithms able to reduce the impact of the catastrophic forgetting phenomenon in deep neural networks. Due to this surge of interest in the field, many competitions have been held in recent years, as they are an excellent opportunity to stimulate research in promising directions. This paper summarizes the ideas, design choices, rules, and results of the challenge held at the 3rd Continual Learning in Computer Vision (CLVision) Workshop at CVPR 2022. The focus of this competition is the complex continual object detection task, which is still underexplored in literature compared to classification tasks. The challenge is based on the challenge version of the novel EgoObjects dataset, a large-scale egocentric object dataset explicitly designed to benchmark continual learning algorithms for egocentric category-/instance-level object understanding, which covers more than 1k unique main objects and 250+ categories in around 100k video frames.

Humans form mental images of 3D scenes to support counterfactual imagination, planning, and motor control. Our abilities to predict the appearance and affordance of the scene from previously unobserved viewpoints aid us in performing manipulation tasks (e.g., 6-DoF kitting) with a level of ease that is currently out of reach for existing robot learning frameworks. In this work, we aim to build artificial systems that can analogously plan actions on top of imagined images. To this end, we introduce Mental Imagery for Robotic Affordances (MIRA), an action reasoning framework that optimizes actions with novel-view synthesis and affordance prediction in the loop. Given a set of 2D RGB images, MIRA builds a consistent 3D scene representation, through which we synthesize novel orthographic views amenable to pixel-wise affordances prediction for action optimization. We illustrate how this optimization process enables us to generalize to unseen out-of-plane rotations for 6-DoF robotic manipulation tasks given a limited number of demonstrations, paving the way toward machines that autonomously learn to understand the world around them for planning actions.

Cloth in the real world is often crumpled, self-occluded, or folded in on itself such that key regions, such as corners, are not directly graspable, making manipulation difficult. We propose a system that leverages visual and tactile perception to unfold the cloth via grasping and sliding on edges. By doing so, the robot is able to grasp two adjacent corners, enabling subsequent manipulation tasks like folding or hanging. As components of this system, we develop tactile perception networks that classify whether an edge is grasped and estimate the pose of the edge. We use the edge classification network to supervise a visuotactile edge grasp affordance network that can grasp edges with a 90% success rate. Once an edge is grasped, we demonstrate that the robot can slide along the cloth to the adjacent corner using tactile pose estimation/control in real time. See http://nehasunil.com/visuotactile/visuotactile.html for videos.

Diffusion models have shown great promise for image generation, beating GANs in terms of generation diversity, with comparable image quality. However, their application to 3D shapes has been limited to point or voxel representations that can in practice not accurately represent a 3D surface. We propose a diffusion model for neural implicit representations of 3D shapes that operates in the latent space of an auto-decoder. This allows us to generate diverse and high quality 3D surfaces. We additionally show that we can condition our model on images or text to enable image-to-3D generation and text-to-3D generation using CLIP embeddings. Furthermore, adding noise to the latent codes of existing shapes allows us to explore shape variations.

这项研究提供了一个新颖的框架，以根据开源数据估算全球城市的公共交通巴士的经济，环境和社会价值。电动巴士是替代柴油巴士以获得环境和社会利益的引人注目的候选人。但是，评估总线电气化价值的最先进模型的适用性受到限制，因为它们需要可能难以购买的总线运营数据的细粒和定制数据。我们的估值工具使用通用过境饲料规范，这是全球运输机构使用的标准数据格式，为制定优先级排序策略提供了高级指导，以使总线机队电气化。我们开发了物理知识的机器学习模型，以评估每种运输途径的能耗，碳排放，健康影响以及总拥有成本。我们通过对大波士顿和米兰大都会地区的公交线路进行案例研究来证明我们的工具的可扩展性。

最近对反向传播的近似（BP）减轻了BP的许多计算效率低下和与生物学的不兼容性，但仍然存在重要的局限性。此外，近似值显着降低了基准的准确性，这表明完全不同的方法可能更富有成果。在这里，基于在软冠军全网络中Hebbian学习的最新理论基础上，我们介绍了多层softhebb，即一种训练深神经网络的算法，没有任何反馈，目标或错误信号。结果，它通过避免重量传输，非本地可塑性，层更新的时间锁定，迭代平衡以及（自我）监督或其他反馈信号来实现效率，这在其他方法中是必不可少的。与最先进的生物学知识学习相比，它提高的效率和生物兼容性不能取得准确性的折衷，而是改善了准确性。 MNIST，CIFAR-10，STL-10和IMAGENET上最多五个隐藏层和添加的线性分类器，分别达到99.4％，80.3％，76.2％和27.3％。总之，SOFTHEBB显示出与BP的截然不同的方法，即对几层的深度学习在大脑中可能是合理的，并提高了生物学上的机器学习的准确性。

自我定位是一种基本功能，移动机器人导航系统集成到使用地图从一个点转移到另一点。因此，任何提高本地化精度的增强对于执行精致的灵活性任务至关重要。本文描述了一个新的位置，该位置使用Monte Carlo定位（MCL）算法维护几个颗粒人群，始终选择最佳的粒子作为系统的输出。作为新颖性，我们的工作包括一种多尺度匹配匹配算法，以创建新的MCL群体和一个确定最可靠的指标。它还贡献了最新的实现，从错误的估计或未知的初始位置增加了恢复时间。在与NAV2完全集成的模块中评估了所提出的方法，并与当前的最新自适应ACML溶液进行了比较，从而获得了良好的精度和恢复时间。