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X-WR-CALNAME:In Pursuit of Dexterous and Generalizable Robot Manipulation u
sing Reinforcement Learning\, Imitation Learning\, and Foundation Models
X-WR-TIMEZONE:America/Los_Angeles
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DTSTART:20070311T020000
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DTSTART:20071104T020000
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UID:2024-11-18-daniel-seita@cml.ics.uci.edu
DTSTAMP:20241118T000000Z
SEQUENCE:57690
DTSTART;TZID=America/Los_Angeles:20241118T130000
DTEND;TZID=America/Los_Angeles:20241118T140000
SUMMARY:[CML Seminar] Daniel Seita: In Pursuit of Dexterous and Generalizab
le Robot Manipulation using Reinforcement Learning\, Imitation Learning\,
and Foundation Models
LOCATION:Donald Bren Hall 4011
DESCRIPTION:Daniel Seita\, Assistant Professor of Computer Science\, Univer
sity of Southern California\n\nTitle: In Pursuit of Dexterous and Generali
zable Robot Manipulation using Reinforcement Learning\, Imitation Learning
\, and Foundation Models\n\nAbstract: The robotics community has seen sign
ificant progress in applying machine learning for robot manipulation. Howe
ver\, despite this progress\, developing a system capable of generalizable
robot manipulation remains fundamentally difficult\, especially when mani
pulating in clutter and adjusting deformable objects such as fabrics\, rop
e\, and liquids. Some promising techniques for developing general robot ma
nipulation systems include reinforcement learning\, imitation learning\, a
nd more recently\, leveraging foundation models trained on internet-scale
data\, such as GPT-4. In this talk\, I will discuss our recent work on (1)
deep reinforcement learning for dexterous manipulation in clutter\, (2) f
oundation models and imitation learning for bimanual manipulation\, and (3
) our benchmarks and applications of foundation models for deformable obje
ct manipulation.\n\nhttps://cml.ics.uci.edu/seminars/2024-11-18-daniel-sei
ta
X-ALT-DESC;FMTTYPE=text/html:Daniel Seita\, Assistant Pr
ofessor of Computer Science\, University of Southern California
Title: In Pursuit of Dexterous and Generalizable Robot Manipulation us
ing Reinforcement Learning\, Imitation Learning\, and Foundation Models
Abstract: The robotics community has seen significant progress
in applying machine learning for robot manipulation. However\, despite th
is progress\, developing a system capable of generalizable robot manipulat
ion remains fundamentally difficult\, especially when manipulating in clut
ter and adjusting deformable objects such as fabrics\, rope\, and liquids.
Some promising techniques for developing general robot manipulation syste
ms include reinforcement learning\, imitation learning\, and more recently
\, leveraging foundation models trained on internet-scale data\, such as G
PT-4. In this talk\, I will discuss our recent work on (1) deep reinforcem
ent learning for dexterous manipulation in clutter\, (2) foundation models
and imitation learning for bimanual manipulation\, and (3) our benchmarks
and applications of foundation models for deformable object manipulation.
https://cml.ics.uci.edu/seminars/2024-11-18-daniel-seita
URL:https://cml.ics.uci.edu/seminars/2024-11-18-daniel-seita
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