Results 51 to 60 of about 120,210 (259)

Robotic Control for Human–Robot Collaborative Assembly Based on Digital Human Model and Reinforcement Learning

Advanced Robotics Research, EarlyView.
This work presents a robotic control method for human–robot collaborative assembly based on a biomechanics‐constrained digital human model. Reinforcement learning is used to generate physiologically plausible human motion trajectories, which are integrated into a virtual environment for robot control learning.
Bitao Yao, Hongzhou Ai, Wenjun Xu, Zude Zhou, Lihui Wang   +4 more
wiley   +1 more source

Towards Automated Quality Control in Industrial Systems: Developing Markov Decision Process Model for Optimized Decision-Making

Proceedings of the International Conference on Applied Innovations in IT
In the context of rapidly evolving industrial environments, optimizing decision-making for quality control is crucial. This paper develops a Markov Decision Process (MDP) model aimed at enhancing automated quality control and reducing scrap in ...
Katerina Mitkovska-Trendova, Robert Minovski, Verica Bakeva, Simeon Trendov, Dimitar Bogatinov   +4 more
doaj   +1 more source

Identifying Cytokine Motif‐Containing, Immunomodulatory Bacterial Proteins in Human Gut Microbiome

Advanced Science, EarlyView.
By building and constructing HMM (Upper left, blue), the authors identify CMCPs in bacteria genomes and CRC related metagenomes and enriched CRC‐related CMCPs (Upper right, blue). They analyze sequence and structural similarity of hits (Lower left, green), test function with engineered EcN delivered to tumors in a mouse tumor model (Lower right, pink ...
Ziyu Wang, Siqi Guo, Jing Li, Qianqian Huang, Jing Ning, Binbin Xia, Xun Lv, Xiang Liu, Zeyu Gao, Jine Li, Longchao Liu, Moshi Song, Jun Wang   +12 more
wiley   +1 more source

From pixels to planning: scale-free active inference

Frontiers in Network Physiology
This paper describes a discrete state-space model and accompanying methods for generative modeling. This model generalizes partially observed Markov decision processes to include paths as latent variables, rendering it suitable for active inference and ...
Karl Friston, Karl Friston, Conor Heins, Tim Verbelen, Lancelot Da Costa, Lancelot Da Costa, Tommaso Salvatori, Dimitrije Markovic, Dimitrije Markovic, Alexander Tschantz, Magnus Koudahl, Christopher Buckley, Christopher Buckley, Thomas Parr   +13 more
doaj   +1 more source

Persistently Increased Expression of PKMzeta and Unbiased Gene Expression Profiles Identify Hippocampal Molecular Traces of a Long‐Term Active Place Avoidance Memory and “Shadow” Proteins

Advanced Science, EarlyView.
Protein complexes like KIBRA‐PKMζ are crucial for maintaining memories, forming month‐long protein traces in memory‐tagged neurons, but conventional RNA‐seq analysis fails to detect their transcript changes, leaving memory molecules undetected in the shadows of abundantly‐expressed genes.
Jiyeon Han, Alejandro Grau‐Perales, Rayna M. Harris, Edith Lesburguères, Hsin‐Yi Kao, Asit Pal, Juan Marcos Alarcon, Todd C. Sacktor, Stefano Martiniani, Hans A. Hofmann, André A. Fenton   +10 more
wiley   +1 more source

Autonomous Flight Strategy Selection and Interval Maintenance for Aircraft With Unknown Flight Intentions

IEEE Access
To enhance the operational safety and efficiency of aircraft under uncertain or unknown flight intentions, a decision-making framework based on Markov Decision Processes with incomplete information (IIG-MDP) is proposed in this paper.
Yang Zhou, Xinmin Tang, Xuanming Ren
doaj   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

First passage risk probability optimality for continuous time Markov decision processes [PDF]

Kybernetika, 2019
Summary: In this paper, we study continuous time Markov decision processes (CTMDPs) with a denumerable state space, a Borel action space, unbounded transition rates and nonnegative reward function. The optimality criterion to be considered is the first passage risk probability criterion.
Huo, Haifeng, Wen, Xian
openaire   +2 more sources

MGDP: Mastering a Generalized Depth Perception Model for Quadruped Locomotion

Advanced Science, EarlyView.
ABSTRACT Perception‐based Deep Reinforcement Learning (DRL) controllers demonstrate impressive performance on challenging terrains. However, existing controllers still face core limitations, struggling to achieve both terrain generality and platform transferability, and are constrained by high computational overhead and sensitivity to sensor noise.
Yinzhao Dong, Ji Ma, Yidan Lu, Jiahui Zhang, Wanyue Li, Yeke Chen, Teng Zhang, Xuechao Chen, Zhangguo Yu, Peng Lu   +9 more
wiley   +1 more source

Online algorithms for POMDPs with continuous state, action, and observation spaces

, 2018
Online solvers for partially observable Markov decision processes have been applied to problems with large discrete state spaces, but continuous state, action, and observation spaces remain a challenge.
Kochenderfer, Mykel, Sunberg, Zachary
core   +1 more source