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X-WR-CALNAME;VALUE=TEXT:Eventi DIAG
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TZID:Europe/Paris
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DTSTART:20221030T030000
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DTSTART:20230326T020000
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UID:calendar.25676.field_data.0@oba.diag.uniroma1.it
DTSTAMP:20260405T053347Z
CREATED:20230317T110514Z
DESCRIPTION: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 I
 nformatica\, Automatica e Gestionale Antonio Ruberti\, codice concorso 202
 3RTDAPNRR006\, Nicola Scianca terrà un seminario sulla sua attività di ric
 erca il 21/03/2023 alle 09:00 presso l'Aula Magna del DIAG e in collegamen
 to Zoom al seguente indirizzo https://uniroma1.zoom.us/j/89155884158?pwd=V
 ngyRUJzanhEc1JoeElycTRtN2QrUT09Meeting ID: 891 5588 4158Passcode: 921785 T
 ITLE: Stable and robust motion generation for humanoid robots: From walkin
 g to running ABSTRACT: Gait Generation for humanoid robots is a complex pr
 oblem\, due to the need to meet hard requirements and to the inherently un
 stable 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 presentat
 ion\, I will discuss Intrinsically Stable MPC (IS-MPC)\, which includes an
  explicit stability constraint that ultimately guarantees bounded trajecto
 ries for the robot CoM. The basic IS-MPC scheme has been successfully appl
 ied in related contexts\, such as humanoid teleoperation and control of ba
 lancing wheeled robots. A prolific line of research has been devoted to th
 e development of a robust version of IS-MPC\, which achieves the same prop
 erties of the original scheme (namely\, recursive feasibility and internal
  stability) in the presence of perturbations by means of various tools\, i
 ncluding 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 jumpi
 ng 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 a
 utonomous driving. During his PostDoc at DIAG he was also a Visiting Resea
 rcher at the University of Tokyo. 
DTSTART;TZID=Europe/Paris:20230321T090000
DTEND;TZID=Europe/Paris:20230321T090000
LAST-MODIFIED:20230317T110757Z
LOCATION:Aula Magna DIAG
SUMMARY: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) - Nicola Scianca
URL;TYPE=URI:http://oba.diag.uniroma1.it/node/25676
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