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.

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。

多步兵的操纵任务（例如打开推动的儿童瓶）需要机器人来做出各种计划选择，这些选择受到在任务期间施加力量的要求所影响的各种计划。机器人必须推荐与动作顺序相关的离散和连续选择，例如是否拾取对象以及每个动作的参数，例如如何掌握对象。为了实现计划和执行有力的操纵，我们通过限制了扭矩和摩擦限制，通过拟议的有力的运动链约束来增强现有的任务和运动计划者。在三个领域，打开一个防儿童瓶，扭动螺母并切割蔬菜，我们演示了系统如何从组合组合组合中进行选择。我们还展示了如何使用成本敏感的计划来查找强大的策略和参数物理参数的不确定性。

在本文中，我们提出了TAC2POSE，这是一种特定于对象的触觉方法，从首次触摸已知对象的触觉估计。鉴于对象几何形状，我们在模拟中学习了一个量身定制的感知模型，该模型估计了给定触觉观察的可能对象姿势的概率分布。为此，我们模拟了一个密集的物体姿势将在传感器上产生的密集对象姿势的接触形状。然后，鉴于从传感器获得的新接触形状，我们使用使用对比度学习学习的对象特定于对象的嵌入式将其与预计集合进行了匹配。我们从传感器中获得接触形状，并具有对象不足的校准步骤，该步骤将RGB触觉观测值映射到二进制接触形状。该映射可以在对象和传感器实例上重复使用，是唯一接受真实传感器数据训练的步骤。这导致了一种感知模型，该模型从第一个真实的触觉观察中定位对象。重要的是，它产生姿势分布，并可以纳入来自其他感知系统，联系人或先验的其他姿势限制。我们为20个对象提供定量结果。 TAC2POSE从独特的触觉观测中提供了高精度的姿势估计，同时回归有意义的姿势分布，以说明可能由不同对象姿势产生的接触形状。我们还测试了从3D扫描仪重建的对象模型上的TAC2POSE，以评估对象模型中不确定性的鲁棒性。最后，我们证明了TAC2POSE的优势与三种基线方法进行触觉姿势估计：直接使用神经网络回归对象姿势，将观察到的接触与使用标准分类神经网络的一组可能的接触匹配，并直接的像素比较比较观察到的一组可能的接触接触。网站：http：//mcube.mit.edu/research/tac2pose.html

我们呈现神经描述符字段（NDFS），对象表示，其通过类别级别描述符在对象和目标（例如用于悬挂的机器人夹具或用于悬挂的机架）之间进行编码和相对姿势。我们使用此表示进行对象操作，在这里，在给定任务演示时，我们要在同一类别中对新对象实例重复相同的任务。我们建议通过搜索（通过优化）来实现这一目标，为演示中观察到的描述符匹配的姿势。 NDFS通过不依赖于专家标记的关键点的3D自动编码任务，方便地以自我监督的方式培训。此外，NDFS是SE（3） - 保证在所有可能的3D对象翻译和旋转中推广的性能。我们展示了在仿真和真正的机器人上的少数（5-10）示范中的操纵任务的学习。我们的性能遍历两个对象实例和6-DOF对象姿势，并且显着优于最近依赖于2D描述符的基线。项目网站：https://yilundu.github.io/ndf/。

使用视频指定任务是获取新颖和一般机器人技能的强大技术。然而，推理机械和灵巧的互动可以使其挑战规模学习接触的操纵。在这项工作中，我们专注于视觉非预先展示平面操作的问题：给定平面运动中对象的视频，找到再现相同对象运动的联系人感知机器人动作。我们提出了一种新颖的架构，可微分的操纵（\我们）的学习，它通过利用可微分优化和基于有限差分的模拟来将视频解码与接触机械的前沿的视频解码神经模型结合在一起。通过广泛的模拟实验，研究了基于模型的技术与现代深度学习方法之间的相互作用。我们发现，我们的模块化和完全可差的架构比看不见的对象和运动的学习方法更好。 \ url {https://github.com/baceituno/dlm}。

Modelling and forecasting real-life human behaviour using online social media is an active endeavour of interest in politics, government, academia, and industry. Since its creation in 2006, Twitter has been proposed as a potential laboratory that could be used to gauge and predict social behaviour. During the last decade, the user base of Twitter has been growing and becoming more representative of the general population. Here we analyse this user base in the context of the 2021 Mexican Legislative Election. To do so, we use a dataset of 15 million election-related tweets in the six months preceding election day. We explore different election models that assign political preference to either the ruling parties or the opposition. We find that models using data with geographical attributes determine the results of the election with better precision and accuracy than conventional polling methods. These results demonstrate that analysis of public online data can outperform conventional polling methods, and that political analysis and general forecasting would likely benefit from incorporating such data in the immediate future. Moreover, the same Twitter dataset with geographical attributes is positively correlated with results from official census data on population and internet usage in Mexico. These findings suggest that we have reached a period in time when online activity, appropriately curated, can provide an accurate representation of offline behaviour.

In the last years, the number of IoT devices deployed has suffered an undoubted explosion, reaching the scale of billions. However, some new cybersecurity issues have appeared together with this development. Some of these issues are the deployment of unauthorized devices, malicious code modification, malware deployment, or vulnerability exploitation. This fact has motivated the requirement for new device identification mechanisms based on behavior monitoring. Besides, these solutions have recently leveraged Machine and Deep Learning techniques due to the advances in this field and the increase in processing capabilities. In contrast, attackers do not stay stalled and have developed adversarial attacks focused on context modification and ML/DL evaluation evasion applied to IoT device identification solutions. This work explores the performance of hardware behavior-based individual device identification, how it is affected by possible context- and ML/DL-focused attacks, and how its resilience can be improved using defense techniques. In this sense, it proposes an LSTM-CNN architecture based on hardware performance behavior for individual device identification. Then, previous techniques have been compared with the proposed architecture using a hardware performance dataset collected from 45 Raspberry Pi devices running identical software. The LSTM-CNN improves previous solutions achieving a +0.96 average F1-Score and 0.8 minimum TPR for all devices. Afterward, context- and ML/DL-focused adversarial attacks were applied against the previous model to test its robustness. A temperature-based context attack was not able to disrupt the identification. However, some ML/DL state-of-the-art evasion attacks were successful. Finally, adversarial training and model distillation defense techniques are selected to improve the model resilience to evasion attacks, without degrading its performance.