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Mostrati risultati da 1 a 6 di 6
Biliary tract localization using data augmentation techniques in endoscopic images for semi-autonomous robotic surgery applications
2023/2024 CONTE, FEDERICA
Design of a Model-Free Reinforcement Learning Algorithm Robust to Irreversible Events for Robotic Manipulation
2024/2025 ROSSI, LEONARDO
Learning Simultaneously Policies and Action Sequences for Robotic Manipulation Tasks
2024/2025 KURTOGLU, METEHAN
Learning stack of tasks for robotic mobile manipulation
2023/2024 ADAMI, ALESSANDRO
Neural Network Based Learning of Inverse Dynamics for Soft Robot Arm Control
2024/2025 GIGANTE, FRANCESCO
Robot Learning Techniques Based on Large Language Models and NLP: A Survey
2024/2025 SATAPATHY, ANASUYA
Tipologia | Anno | Titolo | Titolo inglese | Autore | File |
---|---|---|---|---|---|
Lauree magistrali | 2023 | Biliary tract localization using data augmentation techniques in endoscopic images for semi-autonomous robotic surgery applications | Biliary tract localization using data augmentation techniques in endoscopic images for semi-autonomous robotic surgery applications | CONTE, FEDERICA | |
Lauree magistrali | 2024 | Design of a Model-Free Reinforcement Learning Algorithm Robust to Irreversible Events for Robotic Manipulation | Design of a Model-Free Reinforcement Learning Algorithm Robust to Irreversible Events for Robotic Manipulation | ROSSI, LEONARDO | |
Lauree magistrali | 2024 | Learning Simultaneously Policies and Action Sequences for Robotic Manipulation Tasks | Learning Simultaneously Policies and Action Sequences for Robotic Manipulation Tasks In this research, aim is to explore how robots can learn to perform complex tasks more effectively by combining reinforcement learning, behavior trees, and genetic programming. The idea is to help robots simultaneously figure out not just what actions to take, but also the best sequence of those actions to complete tasks like grasping or assembling objects. By using reinforcement learning, the robot can learn from trial and error, improving its decision-making over time. Behavior trees offer a structured way to define and adapt complex behaviors, making the robot's actions more flexible. Meanwhile, genetic programming will be used to evolve and optimize these behaviors, helping the robot find the most efficient strategies even in unpredictable environments. Ultimately, this research aims to create robots that are not only more capable but also more adaptable to the challenges they encounter in the real world. | KURTOGLU, METEHAN | |
Lauree magistrali | 2023 | Learning stack of tasks for robotic mobile manipulation | Learning stack of tasks for robotic mobile manipulation | ADAMI, ALESSANDRO | |
Lauree magistrali | 2024 | Neural Network Based Learning of Inverse Dynamics for Soft Robot Arm Control | Neural Network Based Learning of Inverse Dynamics for Soft Robot Arm Control | GIGANTE, FRANCESCO | |
Lauree triennali | 2024 | Robot Learning Techniques Based on Large Language Models and NLP: A Survey | Robot Learning Techniques Based on Large Language Models and NLP: A Survey | SATAPATHY, ANASUYA |
Mostrati risultati da 1 a 6 di 6
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