缩影 发表于 2025-3-25 06:20:54
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 amouinspiration 发表于 2025-3-25 11:34:13
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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-mamalign 发表于 2025-3-25 19:30:52
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.无能的人 发表于 2025-3-25 22:43:47
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 autBlood-Clot 发表于 2025-3-26 01:05:35
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.Demonstrate 发表于 2025-3-26 06:17:18
http://reply.papertrans.cn/48/4701/470035/470035_27.png笨拙的你 发表于 2025-3-26 11:06:07
http://reply.papertrans.cn/48/4701/470035/470035_28.png轨道 发表于 2025-3-26 13:55:29
http://reply.papertrans.cn/48/4701/470035/470035_29.png生来 发表于 2025-3-26 18:55:58
,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.