[Colloquium] REMINDER: 3/2 Talks at TTIC: David Held, UC Berkeley

[Mary Marre via Colloquium]

When:     Thursday, March 2nd at 11:00 am

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

Who:        David Held, UC Berkeley


Title:         Robots in Clutter: Learning to Understand Environmental
Changes

Abstract:
Robots today are confined to operate in relatively simple, controlled
environments.   One reason for this is that current methods for processing
visual data tend to break down when faced with occlusions, viewpoint
changes, poor lighting, and other challenging but common situations that
occur when robots are placed in the real world.  I will show that we can
train robots to handle these variations by modeling the causes behind
visual appearance changes.  If robots can learn how the world changes over
time, they can be robust to the types of changes that objects often
undergo.  I demonstrate this idea in the context of autonomous driving, and
I will show how we can use this idea to improve performance for every step
of the robotic perception pipeline: object segmentation, tracking, and
velocity estimation.  I will also present some recent work on learning to
manipulate objects, using a similar framework of learning environmental
changes.  By learning how the environment can change over time, we can
enable robots to operate in the complex, cluttered environments of our
daily lives.

Host: Matthew Walter <mwalter at ttic.edu>




Mary C. Marre
Administrative Assistant
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Room 504*
*Chicago, IL  60637*
*p:(773) 834-1757*
*f: (773) 357-6970*
*mmarre at ttic.edu <mmarre at ttic.edu>*





On Fri, Feb 24, 2017 at 9:53 AM, Mary Marre <mmarre at ttic.edu> wrote:

> When:     Thursday, March 2nd at 11:00 am
>
> Where:    TTIC, 6045 S Kenwood Avenue, 5th Floor, Room 526
>
> Who:        David Held, UC Berkeley
>
>
> Title:         Robots in Clutter: Learning to Understand Environmental
> Changes
>
> Abstract:
> Robots today are confined to operate in relatively simple, controlled
> environments.   One reason for this is that current methods for processing
> visual data tend to break down when faced with occlusions, viewpoint
> changes, poor lighting, and other challenging but common situations that
> occur when robots are placed in the real world.  I will show that we can
> train robots to handle these variations by modeling the causes behind
> visual appearance changes.  If robots can learn how the world changes over
> time, they can be robust to the types of changes that objects often
> undergo.  I demonstrate this idea in the context of autonomous driving, and
> I will show how we can use this idea to improve performance for every step
> of the robotic perception pipeline: object segmentation, tracking, and
> velocity estimation.  I will also present some recent work on learning to
> manipulate objects, using a similar framework of learning environmental
> changes.  By learning how the environment can change over time, we can
> enable robots to operate in the complex, cluttered environments of our
> daily lives.
>
> Host: Matthew Walter <mwalter at ttic.edu>
>
>
> Mary C. Marre
> Administrative Assistant
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Room 504*
> *Chicago, IL  60637*
> *p:(773) 834-1757 <(773)%20834-1757>*
> *f: (773) 357-6970 <(773)%20357-6970>*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
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