Behavior Cloning and Replay of Humanoid Robot via a Depth Camera

The technique of behavior cloning is to equip a robot with the capability of learning control skills through observation, which can naturally perform human–robot interaction. Despite many related studies in the context of humanoid robot behavior cloning, the problems of the unnecessary recording of similar actions and more efficient storage forms than recording actions by joint angles or motor counts are still worth discussing. To reduce the storage burden on robots, we implemented an end-to-end humanoid robot behavior cloning system, which consists of three modules, namely action emulation, action memorization, and action replay. With the help of traditional machine learning methods, the system can avoid recording similar actions while storing actions in a more efficient form. A jitter problem in the action replay is also handled. In our system, an action is defined as a sequence of many pose frames. We propose a revised key-pose detection algorithm to keep minimal poses of each action to minimize storage consumption. Subsequently, a clustering algorithm for key poses is implemented to save each action in the form of identifiers series. Finally, a similarity equation is proposed to avoid the unnecessary storage of similar actions, in which the similarity evaluation of actions is defined as an LCS problem. Experiments on different actions have shown that our system greatly reduces the storage burden of the robot while ensuring that the errors are within acceptable limits. The average error of the revised key-pose detection algorithm is reduced by 69% compared to the original and 26% compared to another advanced algorithm. The storage consumption of actions is reduced by 97% eventually. Experimental results demonstrate that the system can efficiently memorize actions to complete behavioral cloning

Behavior Cloning and Replay of Humanoid Robot via a Depth Camera

The technique of behavior cloning is to equip a robot with the capability of learning control skills through observation, which can naturally perform human–robot interaction. Despite many related studies in the context of humanoid robot behavior cloning, the problems of the unnecessary recording of similar actions and more efficient storage forms than recording actions by joint angles or motor counts are still worth discussing. To reduce the storage burden on robots, we implemented an end-to-end humanoid robot behavior cloning system, which consists of three modules, namely action emulation, action memorization, and action replay. With the help of traditional machine learning methods, the system can avoid recording similar actions while storing actions in a more efficient form. A jitter problem in the action replay is also handled. In our system, an action is defined as a sequence of many pose frames. We propose a revised key-pose detection algorithm to keep minimal poses of each action to minimize storage consumption. Subsequently, a clustering algorithm for key poses is implemented to save each action in the form of identifiers series. Finally, a similarity equation is proposed to avoid the unnecessary storage of similar actions, in which the similarity evaluation of actions is defined as an LCS problem. Experiments on different actions have shown that our system greatly reduces the storage burden of the robot while ensuring that the errors are within acceptable limits. The average error of the revised key-pose detection algorithm is reduced by 69% compared to the original and 26% compared to another advanced algorithm. The storage consumption of actions is reduced by 97% eventually. Experimental results demonstrate that the system can efficiently memorize actions to complete behavioral cloning

Positioning method of expressway ETC gantry by multi‐source traffic data

Abstract With the rapid development of expressway Electronic Toll Collection (ETC) technology in China, the expressway management system is becoming digital and intelligent, which provides a solid foundation for expressway vehicle infrastructure cooperation and autonomous driving. The gantry position is the key part of the ETC system. However, there are still some problems (e.g. gantry position missing or false), which can seriously affect the intelligent development of expressways. To address these two issues, an ETC gantry positioning method is proposed. First, the ETC transaction data and the GPS data on expressways are preprocessed to remove abnormal data and retrieve missed data. Then, combined with Dead Reckoning (DR) and Median Center, the potential position of the gantry is calculated from ETC transaction data and GPS data. Finally, the switching strategy based on Kalman Filter (KF) is used to capture the final gantry position. By comparing the results of the proposal with the collected gantry position, it is found that the positioning error of the gantry position calculated by this proposal is about 37 m. The positioning accuracy is 98.78% with the threshold of 100 m. The experimental results show that the proposal can effectively locate the expressway gantry

HY1C/D-CZI <i>Noctiluca scintillans</i> Bloom Recognition Network Based on Hybrid Convolution and Self-Attention

Accurate Noctiluca scintillans bloom (NSB) recognition from space is of great significance for marine ecological monitoring and underwater target detection. However, most existing NSB recognition models require expert visual interpretation or manual adjustment of model thresholds, which limits model application in operational NSB monitoring. To address these problems, we developed a Noctiluca scintillans Bloom Recognition Network (NSBRNet) incorporating an Inception Conv Block (ICB) and a Swin Attention Block (SAB) based on the latest deep learning technology, where ICB uses convolution to extract channel and local detail features, and SAB uses self-attention to extract global spatial features. The model was applied to Coastal Zone Imager (CZI) data onboard Chinese ocean color satellites (HY1C/D). The results show that NSBRNet can automatically identify NSB using CZI data. Compared with other common semantic segmentation models, NSBRNet showed better performance with a precision of 92.22%, recall of 88.20%, F1-score of 90.10%, and IOU of 82.18%

Observation of periodic optical spectra and soliton molecules in a novel passively mode-locked fiber laser

Due to the necessity of making a series of fine adjustments after mode-locking in most experiments for preparing soliton molecules, the repeatability of the preparations remains a challenge. Here, we propose a novel all-polarization-maintaining erbium-doped fiber laser that utilizes a nonlinear amplifying loop mirror for mode-locking and features a linear structure. This laser can stably output soliton molecules without any additional adjustment once the mode-locking self-starts. It can achieve all-optical switching to single-pulse operation through changing the pumping power, making it suitable for studying the mechanism of soliton molecule bond rupture. In addition, multi-pulse operation can also be achieved. Compared to the single-pulse state, this laser is operated in the multi-pulse state at a lower pumping power level, in contrast to previous methods that relied on increasing pumping power to generate multi-pulses. As a result, these multi-pulses are not susceptible to spectral distortion caused by nonlinear effects and maintain pulse stability close to that of the single-pulse. Furthermore, as the pumping power decreases, a peak with periodic intensity variation appears in the spectral center of both the single-pulse state and multi-pulse states. Combined with the experimental facts, we propose a multistability model to explain this phenomenon. With its ability to switch between soliton molecule, soliton, and multi-pulse states, this flexible laser can serve as a versatile toolbox for studying soliton dynamics

Solvent-Free Buchwald–Hartwig Amination of Heteroaryl Chlorides by <i>N</i>‑Heterocyclic Carbene–Palladium Complex (SIPr)^Ph2Pd(cin)Cl at Room Temperature

Using the robust N-heterocyclic carbene–palladium complex (SIPr)Ph2Pd(cin)Cl, a highly efficient and easy-to-operate method has been developed at room temperature for the solvent-free Buchwald–Hartwig amination of heteroaryl chlorides with various amines. The amount of catalyst can be as low as 0.05 wt %. The system was demonstrated on 47 substrates and successfully applied to the synthesis of commercial pharmaceuticals and candidate drugs with high yields. Furthermore, the protocol can be used to prepare aniline derivatives on a multigram scale without yield loss