Results 151 to 160 of about 8,669,620 (261)
Multi-agent Deep Reinforcement Learning with Human Strategies
Multi-agent Deep Reinforcement Learning with Human ...
S Nahavandi (13392411) +2 more
core
Mutual Adaptation and Influence: Review of Latent Dynamics Models in Human–Robot Interaction
Robots are becoming better teammates by learning hidden patterns in how people act. This review covers how state‐of‐the‐art approaches leverage these patterns to help robots not only react but also anticipate and guide cooperation. Here, we synthesize a unifying framework for these approaches, classify and review existing works, and highlight key ...
Mason O. Smith +4 more
wiley +1 more source
Towards Autonomous Bio-Inspired Optimization: Deep Reinforcement Learning for Adaptive Metaheuristic Orchestration. [PDF]
Carrasco C +5 more
europepmc +1 more source
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 +3 more
wiley +1 more source
6G-Enabled FANET-IoT Framework for Intelligent Watershed Monitoring Using Multi-Agent Deep Reinforcement Learning. [PDF]
Raza R +5 more
europepmc +1 more source
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 +6 more
wiley +1 more source
Adaptive Control of Lower-Limb Assistive Exoskeleton for Rehabilitation Using Deep Reinforcement Learning. [PDF]
Foroutannia A +2 more
europepmc +1 more source
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 +6 more
wiley +1 more source
Push the Limits: Human‐Like Multimodal Extreme Embodied Intelligence for Autonomous Racing
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 +13 more
wiley +1 more source
Robust Path Planning via Deep Reinforcement Learning. [PDF]
Kang D, Park J, Kim P.
europepmc +1 more source

