CS 344R: Robotics

Fall 2006. (unique #56355)
When/Where: TTh 11:00 - 12:30, PAI 3.20
Professor Benjamin Kuipers (kuipers@cs.utexas.edu)
- Office hours: TTh 10:00 - 11:00 am, TAY 4.130C.
Teaching Assistant: Aniket Murarka (aniket@cs.utexas.edu)
- Office hours: TBD
Prerequisites:
- Calculus and basic differential equations
- Good programming skills
- upper-division CS major or equivalent
This syllabus: http://www.cs.utexas.edu/users/kuipers/cs344R-F06.html

The goals and approach of this class will be the same in Fall 2006 as it was in Fall 2005. However, we are changing robots, from the Amigobot to the Sony AIBO. This has resulted in significant changes to the assignments, due to the differences in sensors and effectors, and some changes to the curriculum.

Curriculum

A robot is a computational system coupled with the physical world through its sensors and effectors. An intelligent robot learns about its world from experience, and uses the knowledge it accumulates to make better plans to achieve its goals. Robotics is hard, partly because it crosses many of the abstraction boundaries that simplify other areas of computer science.

This course has two goals. First, a number of important theories and techniques are taught, important to perception and action in a partially-known world. Second, students will work in teams, applying these methods to get intelligent behavior from physical robots.

Enrollment

This class will be limited to 30 students, who will be divided into 10 teams of three students, each with a robot to program. Please contact Professor Kuipers if you wish to be admitted to the class. If more than 30 students wish to sign up, there will be a waiting list.

Topics

Overview

A robot is an intelligent system in continuous sensorimotor interaction with its environment.

Control

Control laws are continuous dynamical systems that can be designed to bring the robot along a trajectory or keep it near a setpoint in spite of limited knowledge and sensor and motor errors.

Tracking

Kalman filters can be used to track a moving target from incomplete observations, or to track a moving robot while observing fixed landmarks.

Behavior Languages

Continuous behavior can sometimes be described in terms of discrete actions and resulting states. We can make simple topological maps by defining distinctive states and the actions linking them.

Occupancy grids

Very general environments can be described by a fine-grained grid, representing the probabilities that each cell is occupied. (Laser first, then sonar.)

SLAM (Simultaneous Localization and Mapping)

There is an elegant interaction between the mapping and localization proceses.

Topological mapping

The overall structure of the environment can be described in a more concise and useful way using a topological map. Describe places, paths, regions, boundaries, etc.

Social implications

Society will be deeply affected if intelligent robots become a reality. What kinds of implications should we consider?

Assignments

In theory, there's no difference between theory and practice.
But in practice, there is.

The course is built around a series of six demanding assignments, each asking your team to achieve an ever-increasing level of sophisticated behavior from your robot. Many of these assignments build on each other. It's entirely reasonable (though not perfectly accurate) to think of the lectures as driven by the needs of the assignments.

Assignments will be done in teams of three, each with their own robot.

An important goal of the course is for you to learn how to work successfully with your team, solving academic/technical, scheduling/coordination, and social/interpersonal problems as they arise so that your team is successful.

There will be robot programming assignments due approximately every two weeks throughout the semester. Each team will have its own robot (starting this semester, the Sony Aibo!) to work with.

Hello, World!

Demonstrate that you can read the sensors and control the motor primitives on your Aibo.

Get the camera image, to show what the Aibo is looking at.
Run a color-blob-tracker to track the location of a blob in the visual field.
Turn the head to face the blob.
Walk toward the blob until it fills more than half the visual field.

Tracking and catching

Use a Kalman filter to track the position of a ball rolling quickly toward the robot.

Track a colored ball as it rolls toward the Aibo.
Predict how closely it will approach the robot, and when.
If it will pass out of range, turn the head to follow it as it goes by, but keep paws down.
If the ball is close enough, reach out a paw and stop the ball as it passes close to the Aibo. (Half-credit for knocking it out of the way. No credit for missing.)
Extra credit: pick up the paw and push the ball back the way it came.

Walk up and kick the ball

Starting with the ball in the distance, walk up and kick it.

Identify a ball several body-lengths away.
Use the high-level walk, controlled by a PID controller, to approach it.
Modify the step-length while approaching the ball, to arrive with the legs in the right phase for the kick.
Make a smooth transition from the high-level walk to setting weight on the stance leg and using the swing leg to kick the ball.
(Don't worry where the ball goes. Yet.)

Learn skill at shooting the ball

Building on the previous skill, learn how to shoot the ball accurately.

On walking up to the ball, visually determine its location relative to the stance foot.
Set the lateral position of the swing leg, relative to the center of the ball.
Raise the head along with the kick, to observe the ball's trajectory.
Learn a relation between ball-position, leg lateral position, and ball trajectory.
Practice, practice, practice!

Build an occupancy grid metrical map

Using a sensorimotor trace from a robot with a laser range-finder, including accurate localization of the robot, build a map of the robot lab and the surrounding corridor.

Simultaneous localization and mapping (SLAM)

Solve the above problem, but without the accurate localization of the robot. You are given the significantly less accurate odometry readings, and must use the sensor observations to correct them.

Grading

The assignments will be graded by teams, with all members of each team receiving the same credit. (5%, 10%, 10%, 10%, 10%, 10%).
Individuals will received their own grades for the Midterm (15%) and Final (20%) exams.
There will also be credit for Class Participation (10%).

Attendance and Textbook

There is no textbook in this class. Research papers in robotics will be handed out and distributed online for you to read. Copies of the slides will be made available, ideally before class each day.

Since so much of the class comes from the lectures and discussion in class, attendance is required. I will take attendance at the beginning of the class period each day. (This also helps me learn your names.) Your attendance will be counted as part of the Class Participation grade.

The Computer Science Department has a Code of Conduct that describes the obligations of faculty and students. Read it at http://www.cs.utexas.edu/users/ear/CodeOfConduct.html.

BJK