当使用基于视觉的方法对被占用和空的空地之间的单个停车位进行分类时，人类专家通常需要注释位置，并标记包含目标停车场中收集的图像的训练集，以微调系统。我们建议研究三种注释类型（多边形，边界框和固定尺寸的正方形），提供停车位的不同数据表示。理由是阐明手工艺注释精度和模型性能之间的最佳权衡。我们还调查了在目标停车场微调预训练型号所需的带注释的停车位数。使用PKLOT数据集使用的实验表明，使用低精度注释（例如固定尺寸的正方形），可以将模型用少于1,000个标记的样品微调到目标停车场。
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由于存在浓烟或阴霾，从室外视觉环境收集的图像通常会降解。在这些退化的视觉环境（DVE）中，在场景理解中进行研究的关键挑战是缺乏代表性的基准数据集。这些数据集需要评估降级设置中的最新对象识别和其他计算机视觉算法。在本文中，我们通过引入带有朦胧和无雾图像的第一个配对的真实图像基准数据集以及原位的雾化密度测量来解决其中的一些限制。该数据集是在受控的环境中生产的，其专业烟雾产生机器覆盖了整个场景，并由从无人机（UAV）（UAV）和无人接地车（UGV）的角度捕获的图像组成。我们还评估了一组代表性的最先进的飞行方法以及数据集中的对象探测器。本文介绍的完整数据集，包括地面真相对象分类框和雾密度测量值，为社区提供了以下网址评估其算法的信息：https：//a2i2-archangel.vision。该数据集的一个子集已用于在CVPR UG2 2022挑战的雾痕中进行对象检测。
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我们解决了与行业相关的尺度上的机器人轨迹计划问题。我们的端到端解决方案将高度通用的随机键算法与模型堆叠和集成技术集成在一起，以及用于溶液细化的路径重新链接。核心优化模块由偏置的随机基遗传算法组成。通过与问题依赖性和问题相关模块的独特分离，我们通过约束的天然编码实现了有效的问题表示。我们表明，对替代算法范式（例如模拟退火）的概括是直接的。我们为行业规模的数据集提供数值基准结果。发现我们的方法始终超过贪婪的基线结果。为了评估当今量子硬件的功能，我们使用Amazon Braket上的QBSOLV在量子退火硬件上获得的经典方法进行了补充。最后，我们展示了如何将后者集成到我们的较大管道中，从而为问题提供了量子准备的混合解决方案。
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我们将存储系统视为任何技术认知系统的关键组成部分，这些系统可以在弥合用于推理，计划和语义场景的高级符号离散表示之间弥合差距，以了解用于控制，用于控制。在这项工作中，我们描述了概念和技术特征，其中的内存系统必须与基础数据表示一起实现。我们根据我们在开发ARMAR类人体机器人系统中获得的经验来确定这些特征，并讨论实践示例，这些例子证明了在以人为中心的环境中执行任务的类人生物机器人的记忆系统应支持，例如多模式，内态性，异性恋，Hetero关联性，可预测性或固有的发作结构。基于这些特征，我们将机器人软件框架ARMARX扩展到了统一的认知架构，该架构用于Armar Humanoid Robot家族的机器人。此外，我们描述了机器人软件的开发如何导致我们采用这种新颖的启用内存的认知体系结构，并展示了机器人如何使用内存来实现内存驱动的行为。
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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.
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Attention mechanisms form a core component of several successful deep learning architectures, and are based on one key idea: ''The output depends only on a small (but unknown) segment of the input.'' In several practical applications like image captioning and language translation, this is mostly true. In trained models with an attention mechanism, the outputs of an intermediate module that encodes the segment of input responsible for the output is often used as a way to peek into the `reasoning` of the network. We make such a notion more precise for a variant of the classification problem that we term selective dependence classification (SDC) when used with attention model architectures. Under such a setting, we demonstrate various error modes where an attention model can be accurate but fail to be interpretable, and show that such models do occur as a result of training. We illustrate various situations that can accentuate and mitigate this behaviour. Finally, we use our objective definition of interpretability for SDC tasks to evaluate a few attention model learning algorithms designed to encourage sparsity and demonstrate that these algorithms help improve interpretability.
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Recent advances in deep learning have enabled us to address the curse of dimensionality (COD) by solving problems in higher dimensions. A subset of such approaches of addressing the COD has led us to solving high-dimensional PDEs. This has resulted in opening doors to solving a variety of real-world problems ranging from mathematical finance to stochastic control for industrial applications. Although feasible, these deep learning methods are still constrained by training time and memory. Tackling these shortcomings, Tensor Neural Networks (TNN) demonstrate that they can provide significant parameter savings while attaining the same accuracy as compared to the classical Dense Neural Network (DNN). In addition, we also show how TNN can be trained faster than DNN for the same accuracy. Besides TNN, we also introduce Tensor Network Initializer (TNN Init), a weight initialization scheme that leads to faster convergence with smaller variance for an equivalent parameter count as compared to a DNN. We benchmark TNN and TNN Init by applying them to solve the parabolic PDE associated with the Heston model, which is widely used in financial pricing theory.
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Artificial neural networks can learn complex, salient data features to achieve a given task. On the opposite end of the spectrum, mathematically grounded methods such as topological data analysis allow users to design analysis pipelines fully aware of data constraints and symmetries. We introduce a class of persistence-based neural network layers. Persistence-based layers allow the users to easily inject knowledge about symmetries (equivariance) respected by the data, are equipped with learnable weights, and can be composed with state-of-the-art neural architectures.
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KL-regularized reinforcement learning from expert demonstrations has proved successful in improving the sample efficiency of deep reinforcement learning algorithms, allowing them to be applied to challenging physical real-world tasks. However, we show that KL-regularized reinforcement learning with behavioral reference policies derived from expert demonstrations can suffer from pathological training dynamics that can lead to slow, unstable, and suboptimal online learning. We show empirically that the pathology occurs for commonly chosen behavioral policy classes and demonstrate its impact on sample efficiency and online policy performance. Finally, we show that the pathology can be remedied by non-parametric behavioral reference policies and that this allows KL-regularized reinforcement learning to significantly outperform state-of-the-art approaches on a variety of challenging locomotion and dexterous hand manipulation tasks.
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Three main points: 1. Data Science (DS) will be increasingly important to heliophysics; 2. Methods of heliophysics science discovery will continually evolve, requiring the use of learning technologies [e.g., machine learning (ML)] that are applied rigorously and that are capable of supporting discovery; and 3. To grow with the pace of data, technology, and workforce changes, heliophysics requires a new approach to the representation of knowledge.
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