本文介绍了具有深度Q网（DQN）的移动机器人的导航技术与门控复发单元（GRU）相结合。与GRU集成的DQN允许动作跳过以改善导航性能。该技术旨在实现自动停车机器人等移动机器人的有效导航。增强学习的框架可以应用于实际环境中的GRU的DQN，可以由部分可观察到的马尔可夫决策过程（POMDP）建模。通过允许动作跳过，可以提高DQN与GRU在学习密钥动作中的能力可以得到改善。应用了算法以探讨ROS-Gazebo模拟器的实际环境中解决方案的可行性，并且模拟结果表明，与单独的DQN获得的结果相比，该算法在导航和碰撞中实现了改进的导航性能和避免用gru而不允许动作跳跃。

建模人行走的动力是对计算机视觉的长期兴趣的问题。许多涉及行人轨迹预测的以前的作品将一组特定的单个动作定义为隐式模型组动作。在本文中，我们介绍了一个名为GP-GRAPH的新颖架构，该架构具有集体的小组表示，用于在拥挤的环境中有效的人行道轨迹预测，并且与所有类型的现有方法兼容。 GP-GRAPH的一个关键思想是将个人和小组关系的关系作为图表表示。为此，GP-Graph首先学会将每个行人分配给最可能的行为组。然后，使用此分配信息，GP编写是图形的组内和组间相互作用，分别考虑了组和群体关系中的人类关系。要具体，对于小组内相互作用，我们掩盖了相关组中的行人图边缘。我们还建议小组合并和不致密操作，以代表一个具有多个行人作为一个图节点的小组。最后，GP-GRAPH从两个组相互作用的综合特征中渗透了一个可获得社会上可接受的未来轨迹的概率图。此外，我们介绍了一个小组潜在的矢量抽样，以确保对一系列可能的未来轨迹的集体推断。进行了广泛的实验来验证我们的体系结构的有效性，该实验证明了通过公开可用的基准测试的绩效一致。代码可在https://github.com/inhwanbae/gpgraph上公开获取。

这封信建议基于分层成本图的多个自动移动机器人（AMR）的流量管理。多个AMR通过数据分发服务（DDS）进行通信，该数据由同一DDS域中的主题共享。每一层的成本都是由主题操纵的。域中的流量管理服务器将发送或接收到AMR的主题。使用分层成本图，提出并实施了新的禁令，车道过滤器，车队层和区域过滤器的概念。禁止过滤器可以帮助用户设置禁止AMR侵入的区域。车道滤波器可以根据角度图像帮助设置单向方向。车队层可以帮助AMR通过流量管理服务器共享其位置。该区域过滤器请求或接收一个独家区域，该区域只能由一个AMR占用，该区域可以从流量管理服务器中占据。所有层通过现实世界AMR在实验上验证。每个区域都可以使用用户定义的图像或基于文本的参数文件配置。

The 3D-aware image synthesis focuses on conserving spatial consistency besides generating high-resolution images with fine details. Recently, Neural Radiance Field (NeRF) has been introduced for synthesizing novel views with low computational cost and superior performance. While several works investigate a generative NeRF and show remarkable achievement, they cannot handle conditional and continuous feature manipulation in the generation procedure. In this work, we introduce a novel model, called Class-Continuous Conditional Generative NeRF ($\text{C}^{3}$G-NeRF), which can synthesize conditionally manipulated photorealistic 3D-consistent images by projecting conditional features to the generator and the discriminator. The proposed $\text{C}^{3}$G-NeRF is evaluated with three image datasets, AFHQ, CelebA, and Cars. As a result, our model shows strong 3D-consistency with fine details and smooth interpolation in conditional feature manipulation. For instance, $\text{C}^{3}$G-NeRF exhibits a Fr\'echet Inception Distance (FID) of 7.64 in 3D-aware face image synthesis with a $\text{128}^{2}$ resolution. Additionally, we provide FIDs of generated 3D-aware images of each class of the datasets as it is possible to synthesize class-conditional images with $\text{C}^{3}$G-NeRF.

In both terrestrial and marine ecology, physical tagging is a frequently used method to study population dynamics and behavior. However, such tagging techniques are increasingly being replaced by individual re-identification using image analysis. This paper introduces a contrastive learning-based model for identifying individuals. The model uses the first parts of the Inception v3 network, supported by a projection head, and we use contrastive learning to find similar or dissimilar image pairs from a collection of uniform photographs. We apply this technique for corkwing wrasse, Symphodus melops, an ecologically and commercially important fish species. Photos are taken during repeated catches of the same individuals from a wild population, where the intervals between individual sightings might range from a few days to several years. Our model achieves a one-shot accuracy of 0.35, a 5-shot accuracy of 0.56, and a 100-shot accuracy of 0.88, on our dataset.

Feature selection helps reduce data acquisition costs in ML, but the standard approach is to train models with static feature subsets. Here, we consider the dynamic feature selection (DFS) problem where a model sequentially queries features based on the presently available information. DFS is often addressed with reinforcement learning (RL), but we explore a simpler approach of greedily selecting features based on their conditional mutual information. This method is theoretically appealing but requires oracle access to the data distribution, so we develop a learning approach based on amortized optimization. The proposed method is shown to recover the greedy policy when trained to optimality and outperforms numerous existing feature selection methods in our experiments, thus validating it as a simple but powerful approach for this problem.

The purpose of this work was to tackle practical issues which arise when using a tendon-driven robotic manipulator with a long, passive, flexible proximal section in medical applications. A separable robot which overcomes difficulties in actuation and sterilization is introduced, in which the body containing the electronics is reusable and the remainder is disposable. A control input which resolves the redundancy in the kinematics and a physical interpretation of this redundancy are provided. The effect of a static change in the proximal section angle on bending angle error was explored under four testing conditions for a sinusoidal input. Bending angle error increased for increasing proximal section angle for all testing conditions with an average error reduction of 41.48% for retension, 4.28% for hysteresis, and 52.35% for re-tension + hysteresis compensation relative to the baseline case. Two major sources of error in tracking the bending angle were identified: time delay from hysteresis and DC offset from the proximal section angle. Examination of these error sources revealed that the simple hysteresis compensation was most effective for removing time delay and re-tension compensation for removing DC offset, which was the primary source of increasing error. The re-tension compensation was also tested for dynamic changes in the proximal section and reduced error in the final configuration of the tip by 89.14% relative to the baseline case.

According to the rapid development of drone technologies, drones are widely used in many applications including military domains. In this paper, a novel situation-aware DRL- based autonomous nonlinear drone mobility control algorithm in cyber-physical loitering munition applications. On the battlefield, the design of DRL-based autonomous control algorithm is not straightforward because real-world data gathering is generally not available. Therefore, the approach in this paper is that cyber-physical virtual environment is constructed with Unity environment. Based on the virtual cyber-physical battlefield scenarios, a DRL-based automated nonlinear drone mobility control algorithm can be designed, evaluated, and visualized. Moreover, many obstacles exist which is harmful for linear trajectory control in real-world battlefield scenarios. Thus, our proposed autonomous nonlinear drone mobility control algorithm utilizes situation-aware components those are implemented with a Raycast function in Unity virtual scenarios. Based on the gathered situation-aware information, the drone can autonomously and nonlinearly adjust its trajectory during flight. Therefore, this approach is obviously beneficial for avoiding obstacles in obstacle-deployed battlefields. Our visualization-based performance evaluation shows that the proposed algorithm is superior from the other linear mobility control algorithms.

In robotics and computer vision communities, extensive studies have been widely conducted regarding surveillance tasks, including human detection, tracking, and motion recognition with a camera. Additionally, deep learning algorithms are widely utilized in the aforementioned tasks as in other computer vision tasks. Existing public datasets are insufficient to develop learning-based methods that handle various surveillance for outdoor and extreme situations such as harsh weather and low illuminance conditions. Therefore, we introduce a new large-scale outdoor surveillance dataset named eXtremely large-scale Multi-modAl Sensor dataset (X-MAS) containing more than 500,000 image pairs and the first-person view data annotated by well-trained annotators. Moreover, a single pair contains multi-modal data (e.g. an IR image, an RGB image, a thermal image, a depth image, and a LiDAR scan). This is the first large-scale first-person view outdoor multi-modal dataset focusing on surveillance tasks to the best of our knowledge. We present an overview of the proposed dataset with statistics and present methods of exploiting our dataset with deep learning-based algorithms. The latest information on the dataset and our study are available at https://github.com/lge-robot-navi, and the dataset will be available for download through a server.

Springs are efficient in storing and returning elastic potential energy but are unable to hold the energy they store in the absence of an external load. Lockable springs use clutches to hold elastic potential energy in the absence of an external load but have not yet been widely adopted in applications, partly because clutches introduce design complexity, reduce energy efficiency, and typically do not afford high-fidelity control over the energy stored by the spring. Here, we present the design of a novel lockable compression spring that uses a small capstan clutch to passively lock a mechanical spring. The capstan clutch can lock up to 1000 N force at any arbitrary deflection, unlock the spring in less than 10 ms with a control force less than 1 % of the maximal spring force, and provide an 80 % energy storage and return efficiency (comparable to a highly efficient electric motor operated at constant nominal speed). By retaining the form factor of a regular spring while providing high-fidelity locking capability even under large spring forces, the proposed design could facilitate the development of energy-efficient spring-based actuators and robots.