[Theory] NOW: 2/3 TTIC Colloquium: Raia Hadsell, Google DeepMind

[Mary Marre]

*When:*      Monday, February 3rd at 11:00 am



*Where:*     TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526



*Who: *       Raia Hadsell, Google DeepMind



*Title:*        Scalable Robot Learning in Rich Environments


*Abstract: *Deep reinforcement learning has rapidly grown as a research
field with far-reaching potential for artificial intelligence. Games and
simple physical simulations have been used as the main benchmark domains
for many fundamental developments. As the field matures, it is important to
develop more sophisticated learning systems with the aim of solving more
complex real-world tasks, but problems like catastrophic forgetting and
data efficiency remain critical, particularly for robotic domains. This
talk will cover some of the challenges that exist for learning from
interactions in more complex, constrained, and real-world settings, in
particular legged locomotion, navigation, and tactile manipulation, and
describe some promising new approaches that have emerged.



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


For more information on the colloquium series or to subscribe to the
mailing list, please see http://www.ttic.edu/colloquium.php

Mary C. Marre
Administrative Assistant
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Room 517*
*Chicago, IL  60637*
*p:(773) 834-1757*
*f: (773) 357-6970*
*mmarre at ttic.edu <mmarre at ttic.edu>*


On Mon, Feb 3, 2020 at 9:56 AM Mary Marre <mmarre at ttic.edu> wrote:

> *When:*      Monday, February 3rd at 11:00 am
>
>
>
> *Where:*     TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526
>
>
>
> *Who: *       Raia Hadsell, Google DeepMind
>
>
>
> *Title:*        Scalable Robot Learning in Rich Environments
>
>
> *Abstract: *Deep reinforcement learning has rapidly grown as a research
> field with far-reaching potential for artificial intelligence. Games and
> simple physical simulations have been used as the main benchmark domains
> for many fundamental developments. As the field matures, it is important to
> develop more sophisticated learning systems with the aim of solving more
> complex real-world tasks, but problems like catastrophic forgetting and
> data efficiency remain critical, particularly for robotic domains. This
> talk will cover some of the challenges that exist for learning from
> interactions in more complex, constrained, and real-world settings, in
> particular legged locomotion, navigation, and tactile manipulation, and
> describe some promising new approaches that have emerged.
>
>
>
> *Host:* Matthew Walter <mwalter at ttic.edu>
>
>
> For more information on the colloquium series or to subscribe to the
> mailing list, please see http://www.ttic.edu/colloquium.php
>
>
>
> Mary C. Marre
> Administrative Assistant
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Room 517*
> *Chicago, IL  60637*
> *p:(773) 834-1757*
> *f: (773) 357-6970*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
>
> On Sun, Feb 2, 2020 at 10:44 PM Mary Marre <mmarre at ttic.edu> wrote:
>
>> *When:*      Monday, February 3rd at 11:00 am
>>
>>
>>
>> *Where:*     TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526
>>
>>
>>
>> *Who: *       Raia Hadsell, Google DeepMind
>>
>>
>>
>> *Title:*        Scalable Robot Learning in Rich Environments
>>
>>
>> *Abstract: *Deep reinforcement learning has rapidly grown as a research
>> field with far-reaching potential for artificial intelligence. Games and
>> simple physical simulations have been used as the main benchmark domains
>> for many fundamental developments. As the field matures, it is important to
>> develop more sophisticated learning systems with the aim of solving more
>> complex real-world tasks, but problems like catastrophic forgetting and
>> data efficiency remain critical, particularly for robotic domains. This
>> talk will cover some of the challenges that exist for learning from
>> interactions in more complex, constrained, and real-world settings, in
>> particular legged locomotion, navigation, and tactile manipulation, and
>> describe some promising new approaches that have emerged.
>>
>>
>>
>> *Host:* Matthew Walter <mwalter at ttic.edu>
>>
>>
>> For more information on the colloquium series or to subscribe to the
>> mailing list, please see http://www.ttic.edu/colloquium.php
>>
>>
>>
>> Mary C. Marre
>> Administrative Assistant
>> *Toyota Technological Institute*
>> *6045 S. Kenwood Avenue*
>> *Room 517*
>> *Chicago, IL  60637*
>> *p:(773) 834-1757*
>> *f: (773) 357-6970*
>> *mmarre at ttic.edu <mmarre at ttic.edu>*
>>
>>
>> On Mon, Jan 27, 2020 at 10:18 PM Mary Marre <mmarre at ttic.edu> wrote:
>>
>>> *When:*      Monday, February 3rd at 11:00 am
>>>
>>>
>>>
>>> *Where:*     TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526
>>>
>>>
>>>
>>> *Who: *       Raia Hadsell, Google DeepMind
>>>
>>>
>>>
>>> *Title:*        Scalable Robot Learning in Rich Environments
>>>
>>>
>>> *Abstract: *Deep reinforcement learning has rapidly grown as a research
>>> field with far-reaching potential for artificial intelligence. Games and
>>> simple physical simulations have been used as the main benchmark domains
>>> for many fundamental developments. As the field matures, it is important to
>>> develop more sophisticated learning systems with the aim of solving more
>>> complex real-world tasks, but problems like catastrophic forgetting and
>>> data efficiency remain critical, particularly for robotic domains. This
>>> talk will cover some of the challenges that exist for learning from
>>> interactions in more complex, constrained, and real-world settings, in
>>> particular legged locomotion, navigation, and tactile manipulation, and
>>> describe some promising new approaches that have emerged.
>>>
>>>
>>>
>>> *Host:* Matthew Walter <mwalter at ttic.edu>
>>>
>>>
>>> For more information on the colloquium series or to subscribe to the
>>> mailing list, please see http://www.ttic.edu/colloquium.php
>>>
>>>
>>>
>>> Mary C. Marre
>>> Administrative Assistant
>>> *Toyota Technological Institute*
>>> *6045 S. Kenwood Avenue*
>>> *Room 517*
>>> *Chicago, IL  60637*
>>> *p:(773) 834-1757*
>>> *f: (773) 357-6970*
>>> *mmarre at ttic.edu <mmarre at ttic.edu>*
>>>
>>
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