State-of-the-art in Robot Learning for Multi-Robot Collaboration: A Comprehensive Survey
B Wu, CS Suh - arXiv preprint arXiv:2408.11822, 2024 - arxiv.org
With the continuous breakthroughs in core technology, the dawn of large-scale integration of
robotic systems into daily human life is on the horizon. Multi-robot systems (MRS) built on …
robotic systems into daily human life is on the horizon. Multi-robot systems (MRS) built on …
: On-Device Real-Time Deep Reinforcement Learning for Autonomous Robotics
Autonomous robotic systems, like autonomous vehicles and robotic search and rescue,
require efficient on-device training for continuous adaptation of Deep Reinforcement …
require efficient on-device training for continuous adaptation of Deep Reinforcement …
That Doesn't Go There: Attacks on Shared State in {Multi-User} Augmented Reality Applications
Augmented Reality (AR) can enable shared virtual experiences between multiple users. In
order to do so, it is crucial for multi-user AR applications to establish a consensus on the" …
order to do so, it is crucial for multi-user AR applications to establish a consensus on the" …
Red: A systematic real-time scheduling approach for robotic environmental dynamics
Intelligent robots are designed to effectively navigate dynamic and unpredictable
environments laden with moving mechanical elements and objects. Such environment …
environments laden with moving mechanical elements and objects. Such environment …
Optimizing healthcare workforce for effective patient care: a cooperative game theory approach
Efficient staff allocation and workload management are critical challenges within the
healthcare industry, impacting patient satisfaction and treatment timeliness. Many hospitals …
healthcare industry, impacting patient satisfaction and treatment timeliness. Many hospitals …
Distantly-Supervised Joint Entity and Relation Extraction with Noise-Robust Learning
Joint entity and relation extraction is a process that identifies entity pairs and their relations
using a single model. We focus on the problem of training these models on distantly-labeled …
using a single model. We focus on the problem of training these models on distantly-labeled …
DRAL: Deep Reinforcement Adaptive Learning for Multi-UAVs Navigation in Unknown Indoor Environment
Autonomous indoor navigation of UAVs presents numerous challenges, primarily due to the
limited precision of GPS in enclosed environments. Additionally, UAVs' limited capacity to …
limited precision of GPS in enclosed environments. Additionally, UAVs' limited capacity to …