BEGIN:VCALENDAR VERSION:2.0 PRODID:-//128.220.36.25//NONSGML kigkonsult.se iCalcreator 2.26.9// CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Laboratory for Computational Sensing + Robotics X-WR-CALDESC: X-FROM-URL:https://lcsr.jhu.edu X-WR-TIMEZONE:America/New_York BEGIN:VTIMEZONE TZID:America/New_York X-LIC-LOCATION:America/New_York BEGIN:STANDARD DTSTART:20231105T020000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RDATE:20241103T020000 TZNAME:EST END:STANDARD BEGIN:DAYLIGHT DTSTART:20240310T020000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RDATE:20250309T020000 TZNAME:EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:ai1ec-12597@lcsr.jhu.edu DTSTAMP:20240329T072710Z CATEGORIES: CONTACT:Ashley Moriarty\; amoriar2@jhu.edu\; https://wse.zoom.us/s/94623801 186 DESCRIPTION:
\n
Abstract:
\nAn enduring goal of AI and robotics has been to
build a robot capable of robustly performing a wide variety of tasks in a
wide variety of environments\; not by sequentially being programmed (or t
aught) to perform one task in one environment at a time\, but rather by in
telligently choosing appropriate actions for whatever task and
\nenvi
ronment it is facing. This goal remains a challenge. In this talk I’ll des
cribe recent work in our lab aimed at the goal of general-purpose robot ma
nipulation by integrating task-and-motion planning with various forms of m
odel learning. In particular\, I’ll describe approaches to manipulating ob
jects without prior shape models\, to acquiring composable sensorimotor sk
ills\, and to exploiting past experience for more efficient planning.
\n
Biography:
\nTomas Lozano-Perez is p
rofessor in EECS at MIT\, and a member of CSAIL. He was a recipient of the
2011 IEEE Robotics Pioneer Award and a co-recipient of the 2021 IEEE Robo
tics and Automation Technical Field Award. He is a Fellow of the AAAI\, AC
M\, and
\nIEEE.