Bilatu
5-tik 1-5 emaitza erakusten
Evaluación de la experiencia de uso de un entorno robótico industrial en realidad virtualEvaluation of the user experience of an industrial robotic environment in virtual reality
(AEIPRO, 2022)
Industry 4.0 is leading to a whole new level of process automation, thus redefining the role of humans, and altering existing jobs in yet unknown ways. Although the number of robots in the manufacturing industry has been ...
Shielded Reinforcement Learning: A review of reactive methods for safe learning
(IEEE, 2023)
Reinforcement Learning (RL) algorithms are showing promising results in simulated environments, but their replication in real physical applications, even more so in safety-critical applications, is not yet guaranteed. ...
A Scalable and Unified Multi-Control Framework for KUKA LBR iiwa Collaborative Robots
(IEEE, 2023)
The trend towards industrialization and digitalization has led more and more companies to deploy robots in their manufacturing facilities. In the field of collaborative robotics, the KUKA LBR iiwa is one of the benchmark ...
Fear Field: Adaptive constraints for safe environment transitions in Shielded Reinforcement Learning
(CEUR-WS.org, 2023)
Shielding methods for Reinforcement Learning agents show potential for safety-critical industrial applications. However, they still lack robustness on nominal safety, a key property for safety control systems. In the case ...
Application of Computer Vision and Deep Learning in the railway domain for autonomous train stop operation
(IEEE, 2020)
The purpose of this paper is to present the results of the analysis of the application of Deep Learning in the railway domain with a particular focus on a train stop operation. The paper proposes an approach consisting of ...