对于现实和生动的着色，最近已经利用了生成先验。但是，由于其表示空间有限，因此这种生成先验的野外复杂图像通常会失败。在本文中，我们提出了BigColor，这是一种新型的着色方法，可为具有复杂结构的不同野外图像提供生动的着色。虽然先前的生成先验训练以综合图像结构和颜色，但我们在关注颜色合成之前就学会了一种生成颜色，鉴于图像的空间结构。通过这种方式，我们减轻了从生成先验中合成图像结构的负担，并扩大其表示空间以覆盖各种图像。为此，我们提出了一个以Biggan启发的编码生成网络，该网络使用空间特征映射而不是空间框架的Biggan潜在代码，从而产生了扩大的表示空间。我们的方法可以在单个正向传球中为各种输入提供强大的着色，支持任意输入分辨率，并提供多模式着色结果。我们证明，BigColor明显优于现有方法，尤其是在具有复杂结构的野外图像上。

我们提出了一个自动静态分析仪Pytea，可检测Pytorch码中的张量误差。张量误差在深度神经网络代码中是至关重要的;一旦在训练阶段中间发生张量形状不匹配，就会丢失大部分训练成本和中间结果。鉴于输入Pytorch源，Pytea静态跟踪每个可能的执行路径，收集路径的张量操作序列所需的张量形状约束，并决定如果约束是不匹配的（因此发生形状误差）。 Pytea的可扩展性和精确铰链对现实世界的Pytorch应用的特点：Pytea保守修剪后的执行路径数很少爆炸，循环简单，以通过我们的符号抽象来限制。我们测试了Pytea在官方Pytorch存储库中的项目中，以及在StackOverflow中发现的一些张计错误代码。 Pytea在几秒钟内成功地检测这些代码中的张量形状误差。

Real-world autonomous missions often require rich interaction with nearby objects, such as doors or switches, along with effective navigation. However, such complex behaviors are difficult to learn because they involve both high-level planning and low-level motor control. We present a novel framework, Cascaded Compositional Residual Learning (CCRL), which learns composite skills by recursively leveraging a library of previously learned control policies. Our framework learns multiplicative policy composition, task-specific residual actions, and synthetic goal information simultaneously while freezing the prerequisite policies. We further explicitly control the style of the motion by regularizing residual actions. We show that our framework learns joint-level control policies for a diverse set of motor skills ranging from basic locomotion to complex interactive navigation, including navigating around obstacles, pushing objects, crawling under a table, pushing a door open with its leg, and holding it open while walking through it. The proposed CCRL framework leads to policies with consistent styles and lower joint torques, which we successfully transfer to a real Unitree A1 robot without any additional fine-tuning.

快速领域适应的能力对于增加增强学习（RL）对现实世界问题的适用性很重要。RL代理的概括对于在现实世界中的成功至关重要，但是零射击政策转移是一个具有挑战性的问题，因为即使是轻微的视觉变化也可能使训练有素的代理在新任务中完全失败。我们提出了USRA：在数据增强下的统一状态表示学习，这是一个代表学习框架，通过对其观察结果进行数据增强来学习潜在的统一状态表示，以提高其推广到看不见的目标域的能力。我们在Walker环境中展示了我们的方法在DeepMind控制概括基准上的成功，并发现USRA可实现更高的样本效率，而与最佳基线结果相比，USRA可以提高样品效率和14.3％的适应性性能。

深度强化学习（Deep RL）已成为开发腿部机器人控制器的有效工具。但是，香草深RL通常需要大量的训练样本，并且对于实现强大的行为不可行。取而代之的是，研究人员通过合并人类专家的知识来调查一种新颖的政策架构，例如调节轨迹发生器（PMTG）的政策。该体系结构通过组合参数轨迹生成器（TG）和反馈策略网络来构建一个经常性的控制循环，以实现更强大的行为。为了利用人类专家的知识，但消除了耗时的互动教学，研究人员调查了一种新颖的架构，策略调节轨迹发生器（PMTG），该建筑通过结合参数轨迹生成器（TG）和反馈策略来构建经常性的控制循环网络使用直观的先验知识来实现​​更强大的行为。在这项工作中，我们建议通过使用接触感知的有限状态机器（FSM）代替TG来调整有限状态机（PM-FSM），从而为每条腿提供更灵活的控制。与TGS相比，FSM在每个腿部运动生成器上提供高级管理，并实现灵活的状态安排，这使得学习的行为不那么容易受到看不见的扰动或具有挑战性的地形。本发明为政策提供了明确的联系事件的概念，以协商意外的扰动。我们证明，在模拟机器人和真实的机器人上，所提出的架构可以在各种情况下（例如具有挑战性的地形或外部扰动）实现更强大的行为。补充视频可以在以下网址找到：https：//youtu.be/78cbomqtkjq。

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.