In spite of the ease with which humans can pick up and use objects, object manipulation remains a popular and challenging research topic even after many decades. The object’s shape, weight, or position can make it impossible for the robot to manipulate it with a single arm. In such situations, dual-arm manipulators are needed. Furthermore, manipulation may require dragging or pushing objects. Dragging an object while considering the desired orientation of the object, along with uncertainties like friction on the surface and possible slipping of the object in the robot’s hands, is very challenging. Therefore, in this paper, we introduce a novel hierarchical deep deterministic policy gradient (HDDPG) that exploits the continuity of the state and action spaces based on the actor-critic, model-free algorithm as a strategy controller to solve the dual-arm object dragging problem. To evaluate the proposed algorithm, we conduct extensive experiments both in simulation and on a real adult-sized humanoid robot. We use 13 different objects, including keyboards, laptops, boxes, etc. These experiments demonstrate the effectiveness and high performance of the proposed algorithm, with an average success rate of 97.3% in simulations and 93.84% and 91.69% in real environments, with 1560 attempts on two different surfaces.

Summary The Covid‐19 pandemic has affected many lives over the past year. In addition to the enormous health cost, the necessary lockdowns and government‐mandated suspension to prevent the spread of the virus had a huge economic impact. The new challenges in 2021 were combating new virus mutations and providing effective vaccines globally. Artificial intelligent (AI) and machine learning have made significant improvements in many different applications during the last decades. One of the advanced and robust technologies in machine learning is deep learning (DL), which can be employed to help prevent initial infections and detect and monitor their progress and side effects. Fast and accurate Covid‐19 infection detection and treatment of suspected patients is essential to make better decisions, ensure treatment, and even save patients' lives. Modern technologies are required to achieve these objectives and create a sustainable society. This article presents a taxonomy in DL algorithms to cover both the technical novelties and empirical results techniques for Covid‐19 in smart cities. In this regard, (i) we demonstrate possible DL algorithms capable of combating Covid‐19; (ii) we propose an up‐to‐date perspective of DL algorithms in social prevention and medical treatment; and (iii) we identify the challenges in combating Covid‐19 outbreaks.

Wireless Sensor Networks (WSNs) are the key part of Internet of Things, as they provide the physical interface between on-field information and backbone analytic engines. An important role of WSNs-when collecting vital information-is to provide a consistent and reliable coverage. To Achieve this, WSNs must implement a highly reliable and efficient coverage recovery algorithm. In this paper, we take a fresh new approach to coverage recovery based on evolutionary algorithms. We propose EMACB-SA, which introduces a new evolutionary algorithm that selects coverage sets using a fitness function that balances energy efficiency and redundancy. The proposed algorithm improves network's coverage and lifetime in areas with heterogeneous event rate in comparison to previous works and hence, it is suitable for using in disaster management.