Models trained with Deep Reinforcement learning (DRL) have been deployed in various areas of robotics with varying degree of success. To overcome the limitations of data gathering in the real world, DRL training utilizes simulated environments and transfers the learned policy to real-world scenarios, i.e., sim–real transfer. Simulators fail to accurately capture the entire dynamics of the real world, so simulation-trained policies often fail when applied to reality, termed a reality gap (RG). In this paper, we propose a search (mapping) algorithm that takes in real-world observation (images) and maps them to the policy-equivalent images in the simulated environment using a convolution neural network (CNN) model. The two-step training process, DRL policy and a mapping model, overcomes the RG problem with simulated data only. We evaluated the proposed system with a gripping task of a custom-made robot arm in the real world and compared the performance against a conventional DRL sim–real transfer system. The conventional system achieved a 15–57% success rate in gripping operation depending on the position of the target object while the mapping-based sim–real system achieved 100%. The experimental results demonstrated that the proposed DRL with mapping method appropriately corresponded the real world to the simulated environment, confirming that the scheme can achieve high sim–real generalization at significantly low training costs.

In several electronics applications, the instability of components containing n-type carbon nanotubes (CNTs) to atmospheric oxidation in harsh environments or high temperatures is a significant concern. Here, we reported that a dense molecular wrapping of n-type CNTs with phosphonium salts reduced the exposed CNT surface by 79% and suppressed the electrophilic reaction of oxygen on the CNT surface. After aging at 353 K for 28 days, 89% of its initial thermoelectric power factor was retained (290.3 μW m–1 K–2). This opens new avenues for the use of n-type materials in high-temperature electronics.

This paper present a simultaneous optimization of the mechanism and control when two six degree of freedom robot arms in the cooperate button pushing task by combining reinforcement learning and genetic algorithm. The proposed optimization system uses a genetic algorithm to optimize the mechanism and reinforcement learning to optimize the control. The length of each link is changed to the length represented by the gene, and the target is to press six buttons in cooperation with two robot arms. The results are evaluated and optimization is performed by repeating crossovers and generational changes. As a result, it was confirmed that not only the control is optimized, but also the mechanism is optimized to achieve efficient and coordinated task achievement.

In recent years, industries have increasingly emphasized the need for high-speed, energy-efficient, and cost-effective solutions. As a result, there has been growing interest in developing flexible link manipulator robots to meet these requirements. However, reducing the weight of the manipulator leads to increased flexibility which, in turn, causes vibrations. This research paper introduces a novel approach for controlling the vibration and motion of a two-link flexible manipulator using reinforcement learning. The proposed system utilizes trust region policy optimization to train the manipulator’s end effector to reach a desired target position, while minimizing vibration and strain at the root of the link. To achieve the research objectives, a 3D model of the flexible-link manipulator is designed, and an optimal reward function is identified to guide the learning process. The results demonstrate that the proposed approach successfully suppresses vibration and strain when moving the end effector to the target position. Furthermore, the trained model is applied to a physical flexible manipulator for real-world control verification. However, it is observed that the performance of the trained model does not meet expectations, due to simulation-to-real challenges. These challenges may include unanticipated differences in dynamics, calibration issues, actuator limitations, or other factors that affect the performance and behavior of the system in the real world. Therefore, further investigations and improvements are recommended to bridge this gap and enhance the applicability of the proposed approach.