Neural Style Transfer with Twin-Delayed DDPG for Shared Control of Robotic Manipulators
CNRS（法国国家科学研究中心）；Univ Rennes（雷恩大学）；Inria（法国国家信息与自动化研究所）；IRISA（雷恩高等计算机与通信科学研究院）；Universidad Carlos III de Madrid (UC3M)（马德里卡洛斯三世大学）；Robotics Lab（机器人实验室）
Raul Fernandez-Fernandez, Marco Aggravi, Paolo Robuffo Giordano, Juan G. Victores, Claudio Pacchierotti
神经风格迁移（Neural Style Transfer, NST）是一类能够对元素（最常见为图像）进行处理，使其呈现另一元素外观或风格的算法。每个元素被定义为内容（Content）与风格（Style）的结合：内容可从概念上定义为该元素的“是什么”，风格则定义为“如何呈现”。在此背景下，我们提出了一种定制化的NST框架，用于将一组风格迁移到机械臂的运动中——例如，同一项机器人任务可以以愤怒、开心、平静或悲伤的方式完成。该框架采用自编码器架构提取并定义目标机器人运动的内容与风格，再通过双延迟深度确定性策略梯度（Twin Delayed Deep Deterministic Policy Gradient, TD3）网络，利用自编码器定义的损失函数生成机器人控制策略。我们提出的神经策略风格迁移TD3（Neural Policy Style Transfer TD3, NPST3）通过引入训练好的风格来改变机器人运动。该方法既可以离线部署，用于在动态环境中执行自主机器人运动；也可以在线部署，用于在运行时调整遥操作机器人的运动风格。所涉及的风格可以从人类演示中在线学习。我们招募了73名志愿者开展人类受试者评估，要求他们识别若干代表性机器人运动背后的风格。结果显示识别率良好，证明使用该方法可以向机器人传递不同的风格。
英文摘要：
Neural Style Transfer (NST) refers to a class of algorithms able to manipulate an element, most often images, to adopt the appearance or style of another one. Each element is defined as a combination of Content and Style: the Content can be conceptually defined as the what and the Style as the how of said element. In this context, we propose a custom NST framework for transferring a set of styles to the motion of a robotic manipulator, e.g., the same robotic task can be carried out in an angry, happy, calm, or sad way. An autoencoder architecture extracts and defines the Content and the Style of the target robot motions. A Twin Delayed Deep Deterministic Policy Gradient (TD3) network generates the robot control policy using the loss defined by the autoencoder. The proposed Neural Policy Style Transfer TD3 (NPST3) alters the robot motion by introducing the trained style. Such an approach can be implemented either offline, for carrying out autonomous robot motions in dynamic environments, or online, for adapting at runtime the style of a teleoperated robot. The considered styles can be learned online from human demonstrations. We carried out an evaluation with human subjects enrolling 73 volunteers, asking them to recognize the style behind some representative robotic motions. Results show a good recognition rate, proving that it is possible to convey different styles to a robot using this approach.
Edge Delayed Deep Deterministic Policy Gradient: efficient continuous control for edge scenarios
Human Inspired Technology Research Centre, University of Padova（帕多瓦大学人机智能技术研究中心）；Department of Information Engineering, University of Padova（帕多瓦大学信息工程学院）
A Novel Deep Reinforcement Learning Method for Computation Offloading in Multi-User Mobile Edge Computing with Decentralization
School of Information and Communications Technology, Hanoi University of Science and Technology, Vietnam（河内科学技术大学信息与通信学院）；Faculty of Electrical and Electronic Engineering, Hanoi Open University, Vietnam（河内开放大学电子与电气工程学院）；Posts and Telecommunications Institute of Technology Hanoi, Vietnam（河内邮电技术研究所）；Faculty of Informatics, University of Debrecen, Hungary（德布勒恩大学信息学院）
Replacing Tunable Parameters in Weather and Climate Models with State-Dependent Functions using Reinforcement Learning
Department of Applied Mathematics and Theoretical Physics, University of Cambridge（剑桥大学应用数学与理论物理系）；School of Mathematical Sciences, Lancaster University（兰卡斯特大学数学科学学院）；Department of Computer Science and Technology, University of Cambridge（剑桥大学计算机科学与技术系）；Met Office Hadley Centre（英国气象局哈德利中心）
AdapJ: An Adaptive Extended Jacobian Controller for Soft Manipulators
Biorobotics Institute and the Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna（生物机器人研究所和机器人与人工智能卓越部门，圣安娜高等学院）；Multi-scale Medical Robotics Centre and Chow Yuk Ho Technology Centre for Innovative Medicine, The Chinese University of Hong Kong（多尺度医疗机器人中心和创新医学技术中心，香港中文大学）；Department of computer systems, Tallinn University of Technology（计算机系统系，塔林技术大学）
Dynamic Sliding Mode Control based on Fractional calculus subject to uncertain delay based chaotic pneumatic robot
Farabina Noshirvani’s smart Co.（法拉比亚·诺希拉维智能公司）；Robotic Research Lab.（机器人研究实验室）；Faculty of Electrical and Robotic Engineering, Shahrood University of Technology（沙霍尔德大学电气与机器人工程学院）
Transferring human emotions to robot motions using Neural Policy Style Transfer
Universidad Complutense de Madrid (UCM)（马德里康普顿斯大学 (UCM)）；Universidad Carlos III de Madrid (UC3M)（马德里卡洛斯三世大学 (UC3M)）；CNRS（法国国家科学研究中心）；University Rennes（雷恩大学）；Inria（法国国家信息与自动化研究所）；IRISA（法国布列塔尼计算机科学与随机系统研究所）
