In this paper, we present the service robot MARLIN and its integration with
the K4R platform, a cloud system for complex AI applications in retail. At its
core, this platform contains so-called semantic digital twins, a semantically
annotated representation of the retail store. MARLIN continuously exchanges
data with the K4R platform, improving the robot's capabilities in perception,
autonomous navigation, and task planning. We exploit these capabilities in a
retail intralogistics scenario, specifically by assisting store employees in
stocking shelves. We demonstrate that MARLIN is able to update the digital
representation of the retail store by detecting and classifying obstacles,
autonomously planning and executing replenishment missions, adapting to
unforeseen changes in the environment, and interacting with store employees.
Experiments are conducted in simulation, in a laboratory environment, and in a
real store. We also describe and evaluate a novel algorithm for autonomous
navigation of articulated tractor-trailer systems. The algorithm outperforms
the manufacturer's proprietary navigation approach and improves MARLIN's
navigation capabilities in confined spaces.

该研究论文介绍了服务机器人 MARLIN 及其与零售业中的复杂人工智能应用的云系统 K4R 平台的整合；研究表明，MARLIN 利用 K4R 平台不断交换数据，从而提高其在感知、自主导航和任务规划方面的能力，并通过在零售物流场景中协助店员进行货架补货，不断更新零售店铺的数字化表示；此外，研究在模拟、实验室环境和实际店铺中进行，介绍并评估了一种用于自主导航的新算法，该算法优于制造商的专有导航方法，提高了 MARLIN 在狭小空间中的导航能力。

MARLIN：用于支持零售行业内物流的云集成机器人解决方案

MARLIN: A Cloud Integrated Robotic Solution to Support Intralogistics in  Retail

Conventional Congestion Control (CC) algorithms,such as TCP Cubic, struggle
in tactical environments as they misinterpret packet loss and fluctuating
network performance as congestion symptoms. Recent efforts, including our own
MARLIN, have explored the use of Reinforcement Learning (RL) for CC, but they
often fall short of generalization, particularly in competitive, unstable, and
unforeseen scenarios. To address these challenges, this paper proposes an RL
framework that leverages an accurate and parallelizable emulation environment
to reenact the conditions of a tactical network. We also introduce refined RL
formulation and performance evaluation methods tailored for agents operating in
such intricate scenarios. We evaluate our RL learning framework by training a
MARLIN agent in conditions replicating a bottleneck link transition between a
Satellite Communication (SATCOM) and an UHF Wide Band (UHF) radio link.
Finally, we compared its performance in file transfer tasks against
Transmission Control Protocol (TCP) Cubic and the default strategy implemented
in the Mockets tactical communication middleware. The results demonstrate that
the MARLIN RL agent outperforms both TCP and Mockets under different
perspectives and highlight the effectiveness of specialized RL solutions in
optimizing CC for tactical network environments.

本研究提出了一种利用 RL 框架来优化战术网络拥塞控制的方法，并使用 MARLIN 智能体在依托于精确的仿真环境下训练，在模拟的卫星通信和 UHF 无线电链路之间测试其性能，结果表明 MARLIN 在战术网络环境下优于传统的 TCP Cubic 和默认的 Mockets 解决方案，这突出了在优化战术网络 CC 中使用专门的 RL 方案的有效性。

动态网络航行学习：用于战术环境拥塞控制的 MARLIN 强化学习框架

Learning to Sail Dynamic Networks: The MARLIN Reinforcement Learning  Framework for Congestion Control in Tactical Environments

This paper proposes a self-supervised approach to learn universal facial
representations from videos, that can transfer across a variety of facial
analysis tasks such as Facial Attribute Recognition (FAR), Facial Expression
Recognition (FER), DeepFake Detection (DFD), and Lip Synchronization (LS). Our
proposed framework, named MARLIN, is a facial video masked autoencoder, that
learns highly robust and generic facial embeddings from abundantly available
non-annotated web crawled facial videos. As a challenging auxiliary task,
MARLIN reconstructs the spatio-temporal details of the face from the densely
masked facial regions which mainly include eyes, nose, mouth, lips, and skin to
capture local and global aspects that in turn help in encoding generic and
transferable features. Through a variety of experiments on diverse downstream
tasks, we demonstrate MARLIN to be an excellent facial video encoder as well as
feature extractor, that performs consistently well across a variety of
downstream tasks including FAR (1.13% gain over supervised benchmark), FER
(2.64% gain over unsupervised benchmark), DFD (1.86% gain over unsupervised
benchmark), LS (29.36% gain for Frechet Inception Distance), and even in low
data regime. Our code and models are available at
this https URL .

该研究提出了一种自我监督的方法，从大量可获得的非标注网页爬取的面部视频中学习高度鲁棒和通用的面部嵌入，提取可转移特征，称之为 MARLIN，有效，一致地执行多种人脸分析任务，包括 FAR，FER，DFD 和 LS，甚至在低数据范围内表现良好。