Results 81 to 90 of about 199,039 (272)

Bayesian Reinforcement Learning via Deep, Sparse Sampling

, 2020
We address the problem of Bayesian reinforcement learning using efficient model-based online planning. We propose an optimism-free Bayes-adaptive algorithm to induce deeper and sparser exploration with a theoretical bound on its performance relative to ...
Basu, Debabrota, Dimitrakakis, Christos, Grover, Divya   +2 more
core  

Flexible Sensor‐Based Human–Machine Interfaces with AI Integration for Medical Robotics

Advanced Robotics Research, EarlyView.
This review explores how flexible sensing technology and artificial intelligence (AI) significantly enhance human–machine interfaces in medical robotics. It highlights key sensing mechanisms, AI‐driven advancements, and applications in prosthetics, exoskeletons, and surgical robotics.
Yuxiao Wang, Zhipeng Jiang, Sun Hwa Kwon, Matthew Ibrahim, Alexander Dang, Lin Dong   +5 more
wiley   +1 more source

Deep learning solutions for smart city challenges in urban development

Scientific Reports
In the realm of urban planning, the integration of deep learning technologies has emerged as a transformative force, promising to revolutionize the way cities are designed, managed, and optimized.
Pengjun Wu, Zhanzhi Zhang, Xueyi Peng, Ran Wang   +3 more
doaj   +1 more source

Maneuver strategy recognition technology for enemy combat aircraft based on Bayesian deep learning

Shenzhen Daxue xuebao. Ligong ban
Enhancing identification of enemy combat aircraft maneuver strategies is a critical factor in improving air combat decision-making capabilities. As traditional deep learning models often show overconfidence in complex and variable combat environment, and
YUAN Yinlong, ZHANG Sijie, CHENG Yun, HUA Liang   +3 more
doaj   +1 more source

Analytically Tractable Bayesian Deep Q-Learning

, 2021
Reinforcement learning (RL) has gained increasing interest since the demonstration it was able to reach human performance on video game benchmarks using deep Q-learning (DQN). The current consensus for training neural networks on such complex environments is to rely on gradient-based optimization.
Ha, Luong, Nguyen, Goulet, James-A.
openaire   +2 more sources

Multimodal Actuation and Environment Adaptive Strategies of Bio‐Inspired Micro/Nanorobots in Precision Medicine

Advanced Robotics Research, EarlyView.
An introduction for multidrive and environment‐adaptive micro/nanorobotics: design and fabrication strategies, intelligent actuation, and their applications. Various intelligent actuation approaches—magnetic, acoustic, optical, chemical, and biological—can be synergistically designed to enhance flexibility and adaptive behavior for precision medicine ...
Aiqing Ma, Han Gong, Jiacheng Ouyang, Liqi Chen, Tianfang Kang, Yiran He, Guojun Huang, Sidi Zheng, Ting Yin, Hong Pan, Lintao Cai   +10 more
wiley   +1 more source

Hyperparameter optimization of machine learning models for predicting actual evapotranspiration

Machine Learning with Applications
Direct measurement of actual evapotranspiration (AET) using eddy covariance and lysimeters is challenging, particularly in large areas, due to high cost, technical complexity, and the need for specialized instrumentation.
Chalachew Muluken Liyew, Elvira Di Nardo, Stefano Ferraris, Rosa Meo   +3 more
doaj   +1 more source

Hard‐Magnetic Soft Millirobots in Underactuated Systems

Advanced Robotics Research, EarlyView.
This review provides a comprehensive overview of hard‐magnetic soft millirobots in underactuated systems. It examines key advances in structural design, physics‐informed modeling, and control strategies, while highlighting the interplay among these domains.
Qiong Wang, Zixuan Zhang, Bingyan Li, Junkai Wang, Hongcheng Gu   +4 more
wiley   +1 more source

Generalized Bayesian deep reinforcement learning

Bayesian reinforcement learning (BRL) is a method that merges principles from Bayesian statistics and reinforcement learning to make optimal decisions in uncertain environments. As a model-based RL method, it has two key components: (1) inferring the posterior distribution of the model for the data-generating process (DGP) and (2) policy learning using
Roy, Shreya Sinha, Everitt, Richard G., Robert, Christian P., Dutta, Ritabrata   +3 more
openaire   +2 more sources

3D Printing of Soft Robotic Systems: Advances in Fabrication Strategies and Future Trends

Advanced Robotics Research, EarlyView.
Collectively, this review systematically examines 3D‐printed soft robotics, encompassing material selections, function integration, and manufacturing methodologies. Meanwhile, fabrication strategies are analyzed in order of increasing complexity, highlighting persistent challenges with proposed solutions.
Changjiang Liu, Bin Cui, Xumin Huang, Xiaoyu (Rayne) Zheng, T. Thang Vo‐Doan, Ruirui Qiao   +5 more
wiley   +1 more source