[Theory] REMINDER: 2/15 Talks at TTIC: Nakul Gopalan, Georgia Tech

[Mary Marre]

*When:*        Tuesday, February 15th at* 11:00 am CT*


*Where:       *Talk will be given *live, in-person* at

                   TTIC, 6045 S. Kenwood Avenue

                   5th Floor, Room 530



*Where:*       Zoom Virtual Talk (*register in advance here*
<https://uchicagogroup.zoom.us/webinar/register/WN_ApMvOwCsR1uRaTZ_wcls0A>)


*Who: *         Nakul Gopalan, Georgia Tech


*Title*:          Robots, Language, and Representations

*Abstract*: Robots are increasingly present in our lives, from cleaning our
houses to automating logistics. However, these robots are still present in
our lives as solitary agents, performing structured tasks, without the
power to collaborate and learn with humans.

A key challenge here is that robots perceive the world and operate in it
using sensors and actuators that are continuous, low-level and noisy.
However, people on the other hand, reason, plan, specify and teach tasks
using high-level concepts without worrying about the low-level continuous
nature of the world. To address this challenge, I develop computational
methods that firstly, allow robots to learn representations, and skills to
solve novel tasks. Moreover, these methods, and representations also enable
robots to be taught and programmed using natural language communication,
allowing robots to understand a human partner's intent.

In this talk I first demonstrate how representations for planning and
language understanding can be learned together to follow commands in novel
environments. In the second part of the talk, I demonstrate a more
practical approach in which language can be grounded to pre-trained deep
policy representations to solve novel task specifications.
Together, these approaches empower robots to learn unstructured tasks via
language and demonstrations. I will then discuss the implications of such
approaches in collaborative task solving with robots in homes, offices and
industries.

*Bio*: Nakul Gopalan is a postdoctoral researcher in the CORE Robotics Lab
with Prof. Matthew Gombolay at Georgia Tech. He completed his PhD at Brown
University's Computer Science department in 2019. Previously he was a
graduate student in Prof. Stefanie Tellex's H2R lab at Brown. His research
interests lie at the intersection of language grounding and robot learning.
Nakul has developed algorithms and methods that allow robots to be trained
by leveraging demonstrations and natural language descriptions. Such
learning would improve the usability of robots within homes and offices.
His other research interests are in hierarchical reinforcement learning and
planning. His work has received a best paper award at the RoboNLP workshop
at ACL 2017.

*Host:* *Matthew Walter* <mwalter at ttic.edu>



Mary C. Marre
Faculty Administrative Support
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Chicago, IL  60637*
*mmarre at ttic.edu <mmarre at ttic.edu>*


On Tue, Feb 8, 2022 at 3:27 PM Mary Marre <mmarre at ttic.edu> wrote:

> *When:*        Tuesday, February 15th at* 11:00 am CT*
>
>
> *Where:       *Talk will be given *live, in-person* at
>
>                    TTIC, 6045 S. Kenwood Avenue
>
>                    5th Floor, Room 530
>
>
>
> *Where:*       Zoom Virtual Talk (*register in advance here*
> <https://uchicagogroup.zoom.us/webinar/register/WN_ApMvOwCsR1uRaTZ_wcls0A>
> )
>
>
> *Who: *         Nakul Gopalan, Georgia Tech
>
>
> *Title*:          Robots, Language, and Representations
>
> *Abstract*: Robots are increasingly present in our lives, from cleaning
> our houses to automating logistics. However, these robots are still present
> in our lives as solitary agents, performing structured tasks, without the
> power to collaborate and learn with humans.
>
> A key challenge here is that robots perceive the world and operate in it
> using sensors and actuators that are continuous, low-level and noisy.
> However, people on the other hand, reason, plan, specify and teach tasks
> using high-level concepts without worrying about the low-level continuous
> nature of the world. To address this challenge, I develop computational
> methods that firstly, allow robots to learn representations, and skills to
> solve novel tasks. Moreover, these methods, and representations also enable
> robots to be taught and programmed using natural language communication,
> allowing robots to understand a human partner's intent.
>
> In this talk I first demonstrate how representations for planning and
> language understanding can be learned together to follow commands in novel
> environments. In the second part of the talk, I demonstrate a more
> practical approach in which language can be grounded to pre-trained deep
> policy representations to solve novel task specifications.
> Together, these approaches empower robots to learn unstructured tasks via
> language and demonstrations. I will then discuss the implications of such
> approaches in collaborative task solving with robots in homes, offices and
> industries.
>
> *Bio*: Nakul Gopalan is a postdoctoral researcher in the CORE Robotics
> Lab with Prof. Matthew Gombolay at Georgia Tech. He completed his PhD at
> Brown University's Computer Science department in 2019. Previously he was a
> graduate student in Prof. Stefanie Tellex's H2R lab at Brown. His research
> interests lie at the intersection of language grounding and robot learning.
> Nakul has developed algorithms and methods that allow robots to be trained
> by leveraging demonstrations and natural language descriptions. Such
> learning would improve the usability of robots within homes and offices.
> His other research interests are in hierarchical reinforcement learning and
> planning. His work has received a best paper award at the RoboNLP workshop
> at ACL 2017.
>
> *Host:* *Matthew Walter* <mwalter at ttic.edu>
>
>
> Mary C. Marre
> Faculty Administrative Support
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Chicago, IL  60637*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.cs.uchicago.edu/pipermail/theory/attachments/20220215/8015345e/attachment-0001.html>