neurorobotics

The aim of the current study was to investigate children's brain responses to robot-assisted language learning. EEG brain signals were collected from 41 Japanese children who learned French vocabularies in two groups; half of the children learned new words from a social robot that narrated a story in French using animations on a computer screen (Robot group) and the other half watched the same animated story on the screen but only with a voiceover narration and without the robot (Display group). To examine brain activation during the learning phase, we extracted EEG functional connectivity (FC) which is defined as the rhythmic synchronization of signals recorded from different brain areas...

Ultra-flat carrying robots (UCR) are used to carry soft targets for functional safety road tests of intelligent driving vehicles and should have superior control performance. For the sake of analyzing and upgrading the motion control performance of the ultra-flat carrying robot, this paper develops the mathematical model of its motion control system on the basis of the test data and the system identification method. Aiming at ameliorating the defects of the standard particle swarm optimization (PSO) algorithm, namely, low accuracy, being susceptible to being caught in a local optimum, and slow convergence when dealing with the parameter identification problems of complex systems, this paper proposes a refined PSO algorithm with inertia weight cosine adjustment and introduction of natural selection principle (IWCNS-PSO), and verifies the superiority of the algorithm by test functions...

Navigating robots with precision in complex environments remains a significant challenge. In this article, we present an innovative approach to enhance robot localization in dynamic and intricate spaces like homes and offices. We leverage Visual Question Answering (VQA) techniques to integrate semantic insights into traditional mapping methods, formulating a novel position hypothesis generation to assist localization methods, while also addressing challenges related to mapping accuracy and localization reliability...

No abstract text is available yet for this article.

This study introduces an intelligent learning model for classification tasks, termed the voting-based Double Pseudo-inverse Extreme Learning Machine (V-DPELM) model. Because the traditional method is affected by the weight of input layer and the bias of hidden layer, the number of hidden layer neurons is too large and the model performance is unstable. The V-DPELM model proposed in this paper can greatly alleviate the limitations of traditional models because of its direct determination of weight structure and voting mechanism strategy...

A flexible spine is critical to the motion capability of most animals and plays a pivotal role in their agility. Although state-of-the-art legged robots have already achieved very dynamic and agile movement solely relying on their legs, they still exhibit the type of stiff movement that compromises movement efficiency. The integration of a flexible spine thus appears to be a promising approach to improve their agility, especially for small and underactuated quadruped robots that are underpowered because of size limitations...

No abstract text is available yet for this article.

INTRODUCTION: Deep deterministic policy gradient (DDPG)-based path planning algorithms for intelligent robots struggle to discern the value of experience transitions during training due to their reliance on a random experience replay. This can lead to inappropriate sampling of experience transitions and overemphasis on edge experience transitions. As a result, the algorithm's convergence becomes slower, and the success rate of path planning diminishes. METHODS: We comprehensively examines the impacts of immediate reward, temporal-difference error (TD-error), and Actor network loss function on the training process...

No abstract text is available yet for this article.

The field of human-computer interaction is expanding, especially within the domain of intelligent technologies. Scene understanding, which entails the generation of advanced semantic descriptions from scene content, is crucial for effective interaction. Despite its importance, it remains a significant challenge. This study introduces RGBD2Cap, an innovative method that uses RGBD images for scene semantic description. We utilize a multimodal fusion module to integrate RGB and Depth information for extracting multi-level features...

Path planning is an essential part of robot intelligence. In this paper, we summarize the characteristics of path planning of industrial robots. And owing to the probabilistic completeness, we review the rapidly-exploring random tree (RRT) algorithm which is widely used in the path planning of industrial robots. Aiming at the shortcomings of the RRT algorithm, this paper investigates the RRT algorithm for path planning of industrial robots in order to improve its intelligence. Finally, the future development direction of the RRT algorithm for path planning of industrial robots is proposed...

This survey explores the symbiotic relationship between Machine Learning (ML) and music, focusing on the transformative role of Artificial Intelligence (AI) in the musical sphere. Beginning with a historical contextualization of the intertwined trajectories of music and technology, the paper discusses the progressive use of ML in music analysis and creation. Emphasis is placed on present applications and future potential. A detailed examination of music information retrieval, automatic music transcription, music recommendation, and algorithmic composition presents state-of-the-art algorithms and their respective functionalities...

INTRODUCTION: The control of infinite-dimensional rigid-flexible robotic arms presents significant challenges, with direct truncation of first-order modal models resulting in poor control quality and second-order models leading to complex hardware implementations. METHODS: To address these issues, we propose a fuzzy super twisting mode control method based on approximate inertial manifold dimensionality reduction for the robotic arm. This innovative approach features an adjustable exponential non-singular sliding surface and a stable continuous super twisting algorithm...

INTRODUCTION: Behavioral Cloning (BC) is a common imitation learning method which utilizes neural networks to approximate the demonstration action samples for task manipulation skill learning. However, in the real world, the demonstration trajectories from human are often sparse and imperfect, which makes it challenging to comprehensively learn directly from the demonstration action samples. Therefore, in this paper, we proposes a streamlined imitation learning method under the terse geometric representation to take good advantage of the demonstration data, and then realize the manipulation skill learning of assembly tasks...

The objective of few-shot fine-grained learning is to identify subclasses within a primary class using a limited number of labeled samples. However, many current methodologies rely on the metric of singular feature, which is either global or local. In fine-grained image classification tasks, where the inter-class distance is small and the intra-class distance is big, relying on a singular similarity measurement can lead to the omission of either inter-class or intra-class information. We delve into inter-class information through global measures and tap into intra-class information via local measures...

Tiny objects in remote sensing images only have a few pixels, and the detection difficulty is much higher than that of regular objects. General object detectors lack effective extraction of tiny object features, and are sensitive to the Intersection-over-Union (IoU) calculation and the threshold setting in the prediction stage. Therefore, it is particularly important to design a tiny-object-specific detector that can avoid the above problems. This article proposes the network JSDNet by learning the geometric Jensen-Shannon (JS) divergence representation between Gaussian distributions...

INTRODUCTION: The emergence of cross-modal perception and deep learning technologies has had a profound impact on modern robotics. This study focuses on the application of these technologies in the field of robot control, specifically in the context of volleyball tasks. The primary objective is to achieve precise control of robots in volleyball tasks by effectively integrating information from different sensors using a cross-modal self-attention mechanism. METHODS: Our approach involves the utilization of a cross-modal self-attention mechanism to integrate information from various sensors, providing robots with a more comprehensive scene perception in volleyball scenarios...

No abstract text is available yet for this article.

INTRODUCTION: Millipedes can avoid obstacle while navigating complex environments with their multi-segmented body. Biological evidence indicates that when the millipede navigates around an obstacle, it first bends the anterior segments of its corresponding anterior segment of its body, and then gradually propagates this body bending mechanism from anterior to posterior segments. Simultaneously, the stride length between pairs of legs inside the bending curve decreases to coordinate the leg motions with the bending mechanism of the body segments...

Deployment of Reinforcement Learning (RL) algorithms for robotics applications in the real world requires ensuring the safety of the robot and its environment. Safe Robot RL (SRRL) is a crucial step toward achieving human-robot coexistence. In this paper, we envision a human-centered SRRL framework consisting of three stages: safe exploration, safety value alignment, and safe collaboration. We examine the research gaps in these areas and propose to leverage interactive behaviors for SRRL. Interactive behaviors enable bi-directional information transfer between humans and robots, such as conversational robot ChatGPT...