[Colloquium] REMINDER: 1/9 Talks at TTIC: David Held, Carnegie Mellon

[Mary Marre]

*When:*        Monday, January 9th at* 10:00** a**m CT   *


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

                   TTIC, 6045 S. Kenwood Avenue

                   5th Floor, Room 530


*Virtually:*  *via* Panopto (*livestream
<https://uchicago.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=6a5b241c-16ae-42c3-b12c-af7e015746e5>*
)


*Who: *         David Held, Carnegie Mellon University


------------------------------

*Title:* Relational Affordance Learning for Robot Manipulation

*Abstract:* Robots today are typically confined to interact with rigid,
opaque objects with known object models. However, the objects in our daily
lives are often non-rigid, can be transparent or reflective, and are
diverse in shape and appearance. I argue that, to enhance the capabilities
of robots, we should develop perception methods that estimate what robots
need to know to interact with the world. Specifically, I will present novel
perception methods that estimate “relational affordances”: task-specific
geometric relationships between objects that allow a robot to determine
what actions it needs to take to complete a task. These estimated
relational affordances can enable robots to perform complex tasks such as
manipulating cloth, articulated objects, grasping transparent and
reflective objects, and other manipulation tasks, generalizing to unseen
objects in a category and unseen object configurations. By reasoning about
relational affordances, we can achieve robust performance on difficult
robot manipulation tasks.


*Bio: *David Held is an assistant professor at Carnegie Mellon University
in the Robotics Institute and is the director of the RPAD lab: Robots
Perceiving And Doing. His research focuses on perceptual robot learning,
i.e. developing new methods at the intersection of robot perception and
planning for robots to learn to interact with novel, perceptually
challenging, and deformable objects. Prior to coming to CMU, David was a
post-doctoral researcher at U.C. Berkeley, and he completed his Ph.D. in
Computer Science at Stanford University.  David also has a B.S. and M.S. in
Mechanical Engineering at MIT.  David is a recipient of the Google Faculty
Research Award in 2017 and the NSF CAREER Award in 2021.

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




Mary C. Marre
Faculty Administrative Support
*Toyota Technological Institute*
*6045 S. Kenwood Avenue, Rm 517*
*Chicago, IL  60637*
*773-834-1757*
*mmarre at ttic.edu <mmarre at ttic.edu>*


On Mon, Jan 2, 2023 at 4:44 PM Mary Marre <mmarre at ttic.edu> wrote:

> *When:*        Monday, January 9th at* 10:00** a**m CT   *
>
>
> *Where:       *Talk will be given *live, in-person* at
>
>                    TTIC, 6045 S. Kenwood Avenue
>
>                    5th Floor, Room 530
>
>
> *Virtually:*  *via* Panopto (*livestream
> <https://uchicago.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=6a5b241c-16ae-42c3-b12c-af7e015746e5>*
> )
>
>
> *Who: *         David Held, Carnegie Mellon University
>
>
> ------------------------------
>
> *Title:* Relational Affordance Learning for Robot Manipulation
>
>
> *Abstract:* Robots today are typically confined to interact with rigid,
> opaque objects with known object models. However, the objects in our daily
> lives are often non-rigid, can be transparent or reflective, and are
> diverse in shape and appearance. I argue that, to enhance the capabilities
> of robots, we should develop perception methods that estimate what robots
> need to know to interact with the world. Specifically, I will present novel
> perception methods that estimate “relational affordances”: task-specific
> geometric relationships between objects that allow a robot to determine
> what actions it needs to take to complete a task. These estimated
> relational affordances can enable robots to perform complex tasks such as
> manipulating cloth, articulated objects, grasping transparent and
> reflective objects, and other manipulation tasks, generalizing to unseen
> objects in a category and unseen object configurations. By reasoning about
> relational affordances, we can achieve robust performance on difficult
> robot manipulation tasks.
>
>
> *Bio: *David Held is an assistant professor at Carnegie Mellon University
> in the Robotics Institute and is the director of the RPAD lab: Robots
> Perceiving And Doing. His research focuses on perceptual robot learning,
> i.e. developing new methods at the intersection of robot perception and
> planning for robots to learn to interact with novel, perceptually
> challenging, and deformable objects. Prior to coming to CMU, David was a
> post-doctoral researcher at U.C. Berkeley, and he completed his Ph.D. in
> Computer Science at Stanford University.  David also has a B.S. and M.S.
> in Mechanical Engineering at MIT.  David is a recipient of the Google
> Faculty Research Award in 2017 and the NSF CAREER Award in 2021.
>
> *Host:* Matthew Walter <mwalter at ttic.edu>
>
>
> Mary C. Marre
> Faculty Administrative Support
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue, Rm 517*
> *Chicago, IL  60637*
> *773-834-1757*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
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