Seminario pubblico di Nicola Scianca (procedura valutativa per n.1 posto di Ricercatore a tempo determinato tipologia A - SC 09/G1, SSD ING-INF/04)
Martedì, 21 March, 2023 - 09:00
Aula Magna DIAG
In ottemperanza ai requisiti previsti dalla procedura selettiva di chiamata per n. 1 posto di ricercatore a tempo determinato tipologia A per il SC 09/G1 - SSD ING-INF/04, presso il Dipartimento di Ingegneria Informatica, Automatica e Gestionale Antonio Ruberti, codice concorso 2023RTDAPNRR006, Nicola Scianca terrà un seminario sulla sua attività di ricerca il 21/03/2023 alle 09:00 presso l'Aula Magna del DIAG e in collegamento Zoom al seguente indirizzo
Meeting ID: 891 5588 4158
TITLE: Stable and robust motion generation for humanoid robots: From walking to running
ABSTRACT: Gait Generation for humanoid robots is a complex problem, due to the need to meet hard requirements and to the inherently unstable nature of these systems. Model Predictive Control (MPC) has proven to be a very effective tool in this context, thanks to the possibility to enforce constraints, the most important being balance. In this presentation, I will discuss Intrinsically Stable MPC (IS-MPC), which includes an explicit stability constraint that ultimately guarantees bounded trajectories for the robot CoM. The basic IS-MPC scheme has been successfully applied in related contexts, such as humanoid teleoperation and control of balancing wheeled robots. A prolific line of research has been devoted to the development of a robust version of IS-MPC, which achieves the same properties of the original scheme (namely, recursive feasibility and internal stability) in the presence of perturbations by means of various tools, including advanced replanning techniques, such as step timing adaptation. Further extensions have been devised in order to achieve walking on uneven ground, as well as for generating highly dynamic motions, such as jumping or running.
BIO: Nicola Scianca received his PhD in Automatic Control, Bioengineering and Operational Research (Automatic Control curriculum) by Sapienza University of Rome in 2020. He is currently a post-doc fellow at DIAG. His research focuses on the application of Model Predictive Control (MPC) to humanoid robot locomotion. He was a Visiting PhD student at the MPC Lab of the University of California at Berkeley, working on MPC for autonomous driving. During his PostDoc at DIAG he was also a Visiting Researcher at the University of Tokyo.