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Mostrati risultati da 1 a 13 di 13
Tipologia Anno Titolo Titolo inglese Autore File
Lauree triennali 2025 Behavior Tree-Based Task Planning for UR5 robot Behavior Tree-Based Task Planning for UR5 robot RUÇI, REZART
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 triennali 2024 Comparative Analysis of Learning Approaches for Internal Temperature Estimation of Lithium-Ion Batteries Comparative Analysis of Learning Approaches for Internal Temperature Estimation of Lithium-Ion Batteries NALUMANSI, ANNE FRANCES
Lauree triennali 2024 Control algorithms for magnetically actuated microrobots Control algorithms for magnetically actuated microrobots SILANI, JACOPO
Lauree triennali 2024 Data-Driven Characterization of Batteries Using Neural Networks Data-Driven Characterization of Batteries Using Neural Networks MOHAMMAD, OBAI
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 triennali 2024 Impatto delle reti wireless sul controllo di robot Impact of Wireless Networks on Robot Control MARTINI, NICOLÒ
Lauree triennali 2025 Latency Measurement on Raspberry Pi using ROS2 Latency Measurement on Raspberry Pi using ROS2 AMINI, NAZANIN
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
Lauree triennali 2025 Wireless Communication Latency in Robot Control Wireless Communication Delays in Robot Control BURDIASHVILI, VACHE
Mostrati risultati da 1 a 13 di 13
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