Results 71 to 80 of about 6,944,236 (307)

Data‐Driven Bulldozer Blade Control for Autonomous Terrain Leveling

Advanced Robotics Research, EarlyView.
A simulation‐driven framework for autonomous bulldozer leveling is presented, combining high‐fidelity terramechanics simulation with a neural‐network‐based reduced‐order model. Gradient‐based optimization enables efficient, low‐level blade control that balances leveling quality and operation time.
Harry Zhang, Ganesh Arivoli, Huzaifa Unjhawala, Luning Bakke, Radu Serban, Dan Negrut   +5 more
wiley   +1 more source

Reinforcement learning for mobile robot mapless navigation: a review of recent advances

Journal of Intelligent Systems
Mobile robot navigation remains a critical challenge in robotics, with applications spanning autonomous vehicles, search and rescue, and other dynamic environments.
Kok Vernon, Ezugwu Absalom E., Olusanya Micheal   +2 more
doaj   +1 more source

Dynamic Multichannel Sensing in Cognitive Radio: Hierarchical Reinforcement Learning

IEEE Access, 2021
Efficient use of spectral resources is critical in wireless networks and has been extensively studied in recent years. Dynamic spectrum access (DSA) is one of the key techniques on utilizing the spectral resources. Among them, reinforcement learning (RL)
Shuai Liu, Jiayun Wu, Jing He
doaj   +1 more source

A novel policy for pre-trained deep reinforcement learning for speech emotion recognition

, 2022
Deep Reinforcement Learning (deep RL) has gained tremendous success in gaming but it has rarely been explored for Speech Emotion Recognition (SER). In the RL literature, policy used by the RL agent plays a major role in action selection, however, there ...
Rana, Rajib, Sara Khalifa, Schuller, Björn W., Schuller, Bjorn W, Bjorn Schuller, Khalifa, Sara, Rajapakshe, Thejan, Thejan Rajapakshe, Rajib Rana, Jiajun Liu, Liu, Jiajun   +10 more
core   +1 more source

Cross‐Scale Hierarchical Targeted Delivery System Based on Small‐Scale Magnetic Robots

Advanced Robotics Research, EarlyView.
This article reviews a cross‐scale hierarchical targeted delivery system that integrates magnetic continuum robots and magnetic microrobots. By combining rapid long‐range navigation with precise microscale targeting, the system overcomes key limitations of single‐scale approaches.
Junjian Zhou, Zhiqiang Chen, Yuanbiao Ma, Zhiqiang Tang, Qianqian Wang   +4 more
wiley   +1 more source

DRIVE‐SAFE: Data‐Driven Robustness and Informed Validation for Evolving Specifications via Formal Evaluation

Advanced Robotics Research, EarlyView.
DRIVE‐SAFE evaluates learning‐based, black‐box autonomous driving policies against evolving temporal safety requirements using Signal Temporal Logic robustness metrics. It aggregates distributional robustness measures with domain‐informed weights to guide iterative retraining.
Kristy Sakano, Jianyu An, Dinesh Manocha, Huan Xu   +3 more
wiley   +1 more source

Scilab-RL: A software framework for efficient reinforcement learning and cognitive modeling research

SoftwareX
One problem with researching cognitive modeling and reinforcement learning (RL) is that researchers spend too much time on setting up an appropriate computational framework for their experiments.
Jan Benad, Frank Röder, Manfred Eppe
doaj   +1 more source

Push the Limits: Human‐Like Multimodal Extreme Embodied Intelligence for Autonomous Racing

Advanced Robotics Research, EarlyView.
Autonomous racing tests embodied intelligence under extreme control limits of speed, actuation, and decision‐making. Inspired by human multimodality, we present Apex, an embodied agent deployed on a super‐formula racing car at Yas Marina. Integrating physics‐informed perception, multimodal learning, and tasking, Apex cuts lap times by 15% and achieves ...
Yanxin Zhou, Baichuan Lou, Wenhui Huang, Binbin Hu, Haohan Yang, Haochen Liu, Xiangkun He, Chee Kiong Ong, Yiran Zhang, Tianchu Su, Bolin Zhao, Xiaoyu Mo, Holden King Ho Li, Chen Lv   +13 more
wiley   +1 more source

MLP-RL-CRD: diagnosis of cardiovascular risk in athletes using a reinforcement learning-based multilayer perceptron

, 2023
Objective. Pre-participation medical screening of athletes is necessary to pinpoint individuals susceptible to cardiovascular events. Approach. The article presents a reinforcement learning (RL)-based multilayer perceptron, termed MLP-RL-CRD, designed to
Ru-San Tan (435098), Roohallah Alizadehsani (13057710), Muhammad Mehrtash (17722245), Marzieh Mirzaeibonehkhater (17722239), Arsam Bostani (17722236), Hamidreza Najafi (17722242), Rajendra Acharya (1390232)   +6 more
core   +1 more source

RL-NSB: Reinforcement Learning-Based 5G Network Slice Broker [PDF]

IEEE/ACM Transactions on Networking, 2019
Network slicing is considered one of the main pillars of the upcoming 5G networks. Indeed, the ability to slice a mobile network and tailor each slice to the needs of the corresponding tenant is envisioned as a key enabler for the design of future networks.
Vincenzo Sciancalepore, Xavier Costa-Pérez, Albert Banchs   +2 more
openaire   +5 more sources