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Mostrati risultati da 1 a 6 di 6
A NMPC based adversarial drivers for driving simulators
2021/2022 BRONCA, NICCOLÒ
Analysis of clocks synchronization algorithms in wireless sensor networks
2011/2012 D'Elia, Edoardo
Kalman filtering for temperature estimation of electric motors
2021/2022 SÁNCHEZ EL RYFAIE, SAMIRA CAROLINA
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
Speeding up Reinforcement Learning Algorithms Through Demonstrations: Application to Robotic Manipulation
2024/2025 PANIZZO, MARCO
Tipologia | Anno | Titolo | Titolo inglese | Autore | File |
---|---|---|---|---|---|
Lauree magistrali | 2021 | A NMPC based adversarial drivers for driving simulators | A NMPC based adversarial drivers for driving simulators | BRONCA, NICCOLÒ | |
Lauree magistrali | 2011 | Analysis of clocks synchronization algorithms in wireless sensor networks | - | D'Elia, Edoardo | |
Lauree magistrali | 2021 | Kalman filtering for temperature estimation of electric motors | Kalman filtering for temperature estimation of electric motors | SÁNCHEZ EL RYFAIE, SAMIRA CAROLINA | |
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 | Speeding up Reinforcement Learning Algorithms Through Demonstrations: Application to Robotic Manipulation | Speeding up Reinforcement Learning Algorithms Through Demonstrations: Application to Robotic Manipulation | PANIZZO, MARCO |
Mostrati risultati da 1 a 6 di 6
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