Category: News
iNEST Spoke 8 Kick-off
SEDUCE Final Meeting
Paper @Machines 2023
Our paper Robot Navigation in Crowded Environments: A Reinforcement Learning Approach was accepted for publication in the scientific journal Machines 23. The paper is co-authored with Matteo Caruso, Enrico Regolin and Stefano Seriani. It presents controllers for driving mobile robots that must safely navigate a crowded environment while trying to…
Paper @HSCC23
Our paper Conformal Quantitative Predictive Monitoring of STL Requirements for Stochastic Processes was accepted to HSCC23, co-authored with Nicola Paoletti for King’s College London. We propose a quantitative predictive monitor over the robustness of satisfaction of an STL property for stochastic processes.
AI dissemination project
The PCTO project organized in collaboration with Liceo Scientifico Galileo Galilei in Trieste has started. The project consists of a series of lectures targeted for high school students. They will familiarize with the handling and interpretation of data with the purpose of developing systems of recommendation
iNEST project
Talk @ FBK
Talk @Bocconi
iNEST Spoke 8 Kick-off
SEDUCE Final Meeting
Paper @Machines 2023
Our paper Robot Navigation in Crowded Environments: A Reinforcement Learning Approach was accepted for publication in the scientific journal Machines 23. The paper is co-authored with Matteo Caruso, Enrico Regolin and Stefano Seriani. It presents controllers for driving mobile robots that must safely navigate a crowded environment while trying to…
Paper @HSCC23
Our paper Conformal Quantitative Predictive Monitoring of STL Requirements for Stochastic Processes was accepted to HSCC23, co-authored with Nicola Paoletti for King’s College London. We propose a quantitative predictive monitor over the robustness of satisfaction of an STL property for stochastic processes.
AI dissemination project
The PCTO project organized in collaboration with Liceo Scientifico Galileo Galilei in Trieste has started. The project consists of a series of lectures targeted for high school students. They will familiarize with the handling and interpretation of data with the purpose of developing systems of recommendation