COMP 150: Developmental Robotics

Instructor: Jivko Sinapovwww.cs.tufts.edu/~jsinapov

This Week

● Brief history and overview of robotics

● Getting started with robot simulators

Sharing information and knowledge between two robots

Basic Robotics Research Problems

● Mapping and Localization– Vision-based

– Laser-based

● Navigation and Movement– Planning Trajectories

– Obstacle Avoidance

● Object Manipulation– Grasping

– Pick and Place

– Dextrous Manipulation

Mapping and Localization, a.k.a where in the world is the robot?

Odometry-based Localization

Example

ROBOT starts at (0,0)

Commands:– FORWARD(15 m)

– TURN_LEFT(90 deg)

– FORWARD(15 m)

– TURN_LEFT(90 deg)

– FORWARD(25 m)

Where does the robot end up?

Here is what actually happens...

Odometry Motion Model

Case Study: Minerva Robot (late 90s)

Case Study: Minerva Robot (late 90s)

Using Ceiling Maps for Localization

Under a light

Measurement z: P(z|x):

Next to a light

Measurement z: P(z|x):

Eslewhere

Measurement z: P(z|x):

Video

Introduction to Particle Filters

[Thrun, Burgard & Fox (2005)]

At start, the robot has no idea...

Next, the robot senses a door...

Since there are 3 identical doors the robot can be next to any one of them

Therefore, we inflate balls (particles) that are next to doors and shrink all others

Therefore, we inflate balls (particles) that are next to doors and shrink all others

Before we continue we have to make all balls to be of equal size. We need to resample.

Resampling Rules

=

=

=

After Resampling...

Next, the robot moves to the right...

...so we move the balls as well

...and add some position noise

Next, the robot senses that it is next to one of the three doors

Now we have to resample again

The robot moves again

… so we must move all balls (particles) to the right again

And so on...

Another case study

Latest and Greatest: 3D Mapping

[ Michael Kaess, Georgia Tech]

Latest and Greatest: 3D Mapping

[ Michael Kaess, Georgia Tech]

State of the art: Cartographer

[ https://www.youtube.com/watch?v=cK6s7soVwws ]

Grasping and Manipulation

Kinematics

Forward Kinematics

GIVEN

FIND

Inverse Kinematics

FIND

GIVEN

Grasping

Typically framed as pose estimation

Learning-Based Approaches

[ http://wiki.ros.org/agile_grasp ]

Agile-grasp with HSR1

Function-Based Grasping

[http://www.madry.pro/images/projects/task_grasping/grasp_examples/object_task.png]

Next...

● Getting started with a robot simulator