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FlexRQC: Model for a Flexible Robot-Driven Quality Control Station
(Elsevier Ltd., 2020)
As the investment on a dedicated quality control stations is not desirable for limited production batches. In general, those systems result in very optimised systems and the lack of flexibility since they are designed for ...
Visual Odometry in Challenging Environments: An Urban Underground Railway Scenario Case
(IEEE, 2022)
Localization is one of the most critical tasks for an autonomous vehicle, as position information is required to understand its surroundings and move accordingly. Visual Odometry (VO) has shown promising results in the ...
Goal-Conditioned Reinforcement Learning within a Human-Robot Disassembly Environment
(MDPI, 2022)
The introduction of collaborative robots in industrial environments reinforces the need to provide these robots with better cognition to accomplish their tasks while fostering worker safety without entering into safety ...
A review on reinforcement learning for contact-rich robotic manipulation tasks
(Elsevier, 2023)
Research and application of reinforcement learning in robotics for contact-rich manipulation tasks have exploded in recent years. Its ability to cope with unstructured environments and accomplish hard-to-engineer behaviors ...
Learning and generalising object extraction skill for contact-rich disassembly tasks: an introductory study
(Springer, 2021)
Remanufacturing automation must be designed to be flexible and robust enough to overcome the uncertainties, conditions of the products, and complexities in the planning and operation of the processes. Machine learning ...
skrl: Modular and Flexible Library for Reinforcement Learning
(ArXiv, 2022)
skrl is an open-source modular library for reinforcement learning written in Python and designed with a focus on readability, simplicity, and transparency of algorithm implementations. Apart from supporting environments ...