Titlebook: Intelligent Systems and Applications; Proceedings of the 2 Kohei Arai Conference proceedings 2024 The Editor(s) (if applicable) and The Aut

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Semantic State Prediction in Robotic Cloth Manipulation,nd benchmarks is another challenge impeding progress in robotic cloth manipulation. In this paper, we make a first attempt to solve the problem of semantic state estimation through RGB-D data only in an end-to-end manner with the help of deep neural networks. Since neural networks require large amou

inspiration

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Unmanned Ground Vehicle Navigation Using Artificial Neural Networks,d Ground Vehicle (UGV) prototype is designed and implemented to reach its target autonomously in an unknown maze environment. The UGV uses two microcontrollers: Raspberry Pi and Arduino Mega. The Raspberry Pi is the high-level microcontroller that uses Artificial Neural Network (ANN) for decision-ma

malign

An Integrated Analysis for Identifying Iconic Gestures in Human-Robot Interactions,quire synchronous integration of speech, human posture, and motions. Iconic gestures are a major subclass of co-speech gestures that express entities and actions by their attributes such as shape-contours, magnitude, and proximity using the synchronous motions of fingers, palms, and spoken phrases.

無能的人

AI-Driven Runtime Monitoring of Energy Consumption in Autonomous Delivery Drones,ngestion in urban areas and sparse transportation infrastructure in remote rural areas plague current package delivery processes. Autonomous delivery systems, e.g., autonomous delivery drones, are considered a viable alternative to improve the last-mile delivery process. In the development of an aut

Blood-Clot

Semantic State Prediction in Robotic Cloth Manipulation,nts of labeled data, we introduce a novel Mujoco simulator to generate a large-scale fully annotated robotic textile manipulation dataset including bimanual actions. Finally, we provide a set of baseline deep neural networks and benchmark them on the problem of semantic state prediction on our proposed dataset.

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,Multi-agent Reinforcement Learning for?Unmanned Aerial Vehicle Capture-the-Flag Game Behavior,successfully transfer simulation machine learning results to hardware. This approach is particularly favorable for real-world robotics because of the sample efficiency, potential for rapid iterations, and the ability to leverage strong and effective non-machine learned control techniques within the reinforcement learning approach.