Results 111 to 120 of about 9,252,589 (289)

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

Intelligent Sky Guardians (InSkyGuard): An Aerial Robotic Swarm for Autonomous Detection and Entrapment of Rogue Multirotors

Advanced Robotics Research, EarlyView.
Intelligent Sky Guardians (InSkyGuard) is introduced as a four‐drone swarm that autonomously detects, tracks, and safely captures rogue drones using a coordinated net system. Computer vision and leader–follower control architecture enable synchronized enclosure, while integrated failsafes enhance system reliability. Validated through closed‐environment
Joshua Hastings, Levanya Dewanarayana, Iline Shaju, Haider Al‐Wizni, Sampson Marquis, Weicheng Huang, Pooya Sareh   +6 more
wiley   +1 more source

Improving Exploration in Reinforcement Learning through Domain Knowledge and Parameter Analysis [PDF]

, 2010
This thesis presents novel work on how to improve exploration in reinforcement learning using domain knowledge and knowledge-based approaches to reinforcement learning.
Grzes, Marek
core   +2 more sources

Towards Safe Load Balancing based on Control Barrier Functions and Deep Reinforcement Learning

Accepted to IEEE/IFIP NOMS 2024International audienceDeep Reinforcement Learning (DRL) algorithms have recently made significant strides in improving network performance.
Quang, Pham Tran Anh, Leguay, Jérémie, Dinh, Lam   +2 more
core   +1 more source

Coordinated Volt/VAR Control in Distribution Networks Considering Demand Response via Safe Deep Reinforcement Learning

Energies
Volt–VAR control (VVC) is essential in maintaining voltage stability and operational efficiency in distribution networks, particularly with the increasing integration of distributed energy resources. Traditional methods often struggle to manage real-time
Dong Hua, Fei Peng, Suisheng Liu, Qinglin Lin, Jiahui Fan, Qian Li   +5 more
doaj   +1 more source

You can now have a chance to beat your grandparents at Briscola thanks to Deep Reinforcement Learning

, 2023
openDevelopment and training of an artificial intelligent agent through Reinforcement Learning, Deep Learning and Game Theory, able to play the game of Briscola.Development and training of an artificial intelligent agent through Reinforcement Learning ...
SINIGAGLIA, ALBERTO
core  

Intelligent Maintenance Review for Robots: Multimodal Information, Deep Diagnosis and Embodied Artificial Intelligence

Advanced Robotics Research, EarlyView.
This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao, Hongbo Wang, Naipeng Li, Shuo Wang, Yaguo Lei, Tonghai Wu, Junyi Cao   +6 more
wiley   +1 more source

Safe Reinforcement Learning With Deep Q-Networks for Energy-Efficient Sleep Scheduling in Wireless Sensor Networks

IEEE Access
Wireless sensor networks are widely used in Internet of Things applications where energy efficiency and reliable packet delivery must be jointly considered.
Abdelkarim Benjilali, Fadoua Yakine, Adil Kenzi   +2 more
doaj   +1 more source

Learning‐Based Soft Robotic Grasping: Recent Progress and Remaining Challenges

Advanced Robotics Research, EarlyView.
This review analyzes learning‐based soft robotic grasping from a pipeline‐oriented perspective, encompassing soft gripper design, multimodal sensing, and learning‐based planning and control. It surveys key neural network architectures and benchmark datasets and identifies critical challenges such as sim‐to‐real transfer, generalization, and continual ...
Arnab Majumder, Jelizaveta Konstantinova, Graham Deacon, Kaspar Althoefer   +3 more
wiley   +1 more source

A Hierarchical Framework for Interpretable, Safe, and Specialised Deep Reinforcement Learning

Safety-critical systems, which are crucial for human safety and the environment, are difficult to control and operate. Traditional controllers need precise models of these complex systems, which is hard to develop. \acrfull{drl} offers a potential solution by learning from interactions rather than detailed models, but it faces limitations such as non ...
openaire   +2 more sources