CS 547: Sensing and Planning in Robotics

1. CS 547: Sensing and Planning in Robotics Gaurav S. Sukhatme Computer Science Robotics Research Laboratory University of Southern California gaurav@usc.edu http://robotics.usc.edu/~gaurav

2. Administrative Matters • Signup - please fill in the details on one of the sheets • Web page http://robotics.usc.edu/~gaurav/CS547 • Email list cs547@robotics.usc.edu • Grading (3 quizzes 45%, class participation 5%, and project 50%) • TA: Mike Rubenstein (mrubenst@usc.edu) • Note: First quiz on Sept 8, scores available on Sept 10 by 1pm

3. Project and Textbook • Project • Team projects only (exceptions are hard to obtain) • Equipment (Pioneer/Segway robots with sensors, Player/Stage/Gazebo software) • Book • Probabilistic Robotics (Thrun, Burgard, Fox) • Available at the Paper Clip in University Village (North of Jefferson Blvd.)

4. I expect you to • come REGULARLY to class • visit the class web page FREQUENTLY • read email EVERY DAY • SPEAK UP when you have a question • START EARLY on your project • If you don’t • the likelihood of learning anything is small • the likelihood of obtaining a decent grade is small

5. In this course you will • learn how to address the fundamental problem of robotics i.e. how to combat uncertainty using the tools of probability theory • We will explore the advantages and shortcomings of the probabilistic method • Survey modern applications of robots • Read some cutting edge papers from the literature

6. Syllabus and Class Schedule • 8/25 Introduction, math review • 9/1 Math review, Bayes filtering • 9/8 Math review, Quiz 1 • 9/15 Quiz 1 discussion, Player/Stage/Gazebo tutorial • 9/22 Kalman and Particle filters • 9/29 Probabilistic Kinematics • 10/6 Sensor Models • 10/13 Localization papers • 10/20 No class, but Project Proposals due to the TA • 10/27 Cooperative localization papers and Quiz 2 • 11/3 Quiz 2 discussion, Mapping • 11/10 SLAM • 11/17 Acting under uncertainty • 11/24 Quiz 3 • 12/1 Final project demos

7. Robotics Yesterday

8. Robotics Today

9. Robotics Tomorrow?

10. What is robotics/a robot ? • Background • Term robot invented by Capek in to mean a machine that would willing and ably do our dirty work for us • The first use of robotics as a word appears in Asimovs science • Definition (Brady): Robotics is the intelligent connection of perception of action

11. Reactive Paradigm (mid-80’s) • no models • relies heavily on good sensing Trends in Robotics Research • Classical Robotics (mid-70’s) • exact models • no sensing necessary • Hybrids (since 90’s) • model-based at higher levels • reactive at lower levels • Probabilistic Robotics (since mid-90’s) • seamless integration of models and sensing • inaccurate models, inaccurate sensors Robots are moving away from factory floors to Entertainment, Toys, Personal service. Medicine, Surgery, Industrial automation (mining, harvesting), Hazardous environments (space, underwater)

12. Examples

13. Entertainment Robots: Toys

14. Entertainment Robots: RoboCup

15. Autonomous Flying Machines

16. Humanoids

17. Mobile Robots as Museum Tour-Guides

18. Tasks to be Solved by Robots • Planning • Perception • Modeling • Localization • Interaction • Acting • Manipulation • Cooperation • ...

19. Uncertainty is Inherent/Fundamental • Uncertainty arises from four major factors: • Environment is stochastic, unpredictable • Robots actions are stochastic • Sensors are limited and noisy • Models are inaccurate, incomplete

20. Range Data Nature of Sensor Data Odometry Data

21. Probabilistic Robotics Key idea: Explicit representation of uncertainty using the calculus of probability theory • Perception = state estimation • Action = utility optimization

22. Advantages and Pitfalls • Can accommodate inaccurate models • Can accommodate imperfect sensors • Robust in real-world applications • Best known approach to many hard robotics problems • Computationally demanding • False assumptions • Approximate

23. Axioms of Probability Theory Pr(A) denotes probability that proposition A is true.

24. B A Closer Look at Axiom 3

25. Using the Axioms

26. Discrete Random Variables • Xdenotes a random variable. • Xcan take on a finite number of values in {x1, x2, …, xn}. • P(X=xi), or P(xi), is the probability that the random variable X takes on value xi. • P( ) is called probability mass function. • E.g. .

27. Continuous Random Variables • Xtakes on values in the continuum. • p(X=x), or p(x), is a probability density function. • E.g. p(x) x

28. Joint and Conditional Probability • P(X=x and Y=y) = P(x,y) • If X and Y are independent then P(x,y) = P(x) P(y) • P(x | y) is the probability of x given y P(x | y) = P(x,y) / P(y) P(x,y) = P(x | y) P(y) • If X and Y are independent thenP(x | y) = P(x)

29. Law of Total Probability Discrete case Continuous case

30. Reverend Thomas Bayes, FRS (1702-1761) Clergyman and mathematician who first used probability inductively and established a mathematical basis for probability inference

31. Bayes Formula

32. Normalization Algorithm:

33. Conditioning • Total probability: • Bayes rule and background knowledge:

34. Simple Example of State Estimation • Suppose a robot obtains measurement z • What is P(open|z)?

35. count frequencies! Causal vs. Diagnostic Reasoning • P(open|z) is diagnostic. • P(z|open) is causal. • Often causal knowledge is easier to obtain. • Bayes rule allows us to use causal knowledge:

36. Example • P(z|open) = 0.6 P(z|open) = 0.3 • P(open) = P(open) = 0.5 • z raises the probability that the door is open.

37. Combining Evidence • Suppose our robot obtains another observation z2. • How can we integrate this new information? • More generally, how can we estimateP(x| z1...zn )?

38. Recursive Bayesian Updating Markov assumption: znis independent of z1,...,zn-1if we know x.

39. Example: Second Measurement • P(z2|open) = 0.5 P(z2|open) = 0.6 • P(open|z1)=2/3 • z2lowers the probability that the door is open.

40. Actions • Often the world is dynamic since • actions carried out by the robot, • actions carried out by other agents, • or just the time passing by change the world. • How can we incorporate such actions?

41. Typical Actions • The robot turns its wheels to move • The robot uses its manipulator to grasp an object • Plants grow over time… • Actions are never carried out with absolute certainty. • In contrast to measurements, actions generally increase the uncertainty.

42. Modeling Actions • To incorporate the outcome of an action u into the current “belief”, we use the conditional pdf P(x|u,x’) • This term specifies the pdf that executing u changes the state from x’ to x.

43. Example: Closing the door

44. State Transitions P(x|u,x’) for u = “close door”: If the door is open, the action “close door” succeeds in 90% of all cases.

45. Integrating the Outcome of Actions Continuous case: Discrete case:

46. Example: The Resulting Belief