© sebastian thrun, CMU, CS226 Statistical Techniques In Robotics Sebastian Thrun (Instructor) and Josh Bao (TA) Office: Gates 154, Office hours: Monday 1:30-3pm

© sebastian thrun, CMU, Administrative Information  Sebastian Thrun  Josh  Web:  list: tba  Time:Mon/Wed, 9:30-10:45am  Location:380 X  Appointments: Mon 1:30-3:00 (Sebastian)  tba (Josh)

© sebastian thrun, CMU, 20003

4 Goals  Enable you to program robots and embedded systems in a robust fashion  Enable you to understand the intrinsic assumptions in your robot software  Enable you to pursue original research in probabilistic robotics  Sway you into joining a young and fascinating research field: probabilistic robotics

© sebastian thrun, CMU, What this course is not  Intro to robotics  Little work  Low on math

© sebastian thrun, CMU, Course Schedule Localization March31-April 14 Mapping April 21-May 5 Decision Making May 10-May26 Multi-Agent May 17

© sebastian thrun, CMU, What You Should Do  Think  Think differently  Be critical  Come up with Original Research

© sebastian thrun, CMU, What Is A Good Project  tbd Haptic Mapping Learning Models of Outdoor Traffic Flow

© sebastian thrun, CMU, Requirements  On your own Written assignment(s) Warm-up project (mobile robot localization) Midterm exam  In teams of three: Research Project

© sebastian thrun, CMU, Your next tasks  Check out Web site Read assigned paper Download map+sensor data and program robot localization algorithm  Come to class on April 5 th (9:30am-10:45am)

© sebastian thrun, CMU,

© sebastian thrun, CMU,

© sebastian thrun, CMU,

© sebastian thrun, CMU,

© sebastian thrun, CMU, Five Sources of Uncertainty Environment Dynamics Random Action Effects Sensor Limitations Inaccurate Models Approximate Computation

© sebastian thrun, CMU, Trends in Robotics Reactive Paradigm (mid-80’s) no models relies heavily on good sensing Probabilistic Robotics (since mid-90’s) seamless integration of models and sensing inaccurate models, inaccurate sensors Hybrids (since 90’s) model-based at higher levels reactive at lower levels Classical Robotics (mid-70’s) exact models no sensing necessary

© sebastian thrun, CMU,

© sebastian thrun, CMU, Rhino

© sebastian thrun, CMU, Minerva

© sebastian thrun, CMU, The CMU/Pitt Nursebot Initiative

© sebastian thrun, CMU, People Detection Mike Montemerlo

© sebastian thrun, CMU, Learning Models of People Maren Bennewitz

© sebastian thrun, CMU, D Mapping Result With: Christian Martin

© sebastian thrun, CMU, Multi-Robot Exploration

© sebastian thrun, CMU, Helicopter Control

© sebastian thrun, CMU, Mine Mapping

© sebastian thrun, CMU, Campus Navigation

© sebastian thrun, CMU, NASA DART site

© sebastian thrun, CMU, Campus Map (in Progress)

© sebastian thrun, CMU, What are interesting problems?  Mapping, automatic, manual, guided?  Probabilistic localization, landmarks?, odometer!,  Route planning, collision avoidance  Multi-robot sensor fusion, cooperation

© sebastian thrun, CMU, How can we solve them?

© sebastian thrun, CMU,

© sebastian thrun, CMU, Where Am I/?

© sebastian thrun, CMU, Nature of Sensor Data: Uncertainty Odometry Data Range Data

© sebastian thrun, CMU,

© sebastian thrun, CMU, Warm-Up Assignment: Localization, Due April 14, 04

© sebastian thrun, CMU, Warm-Up Assignment: Localization

© sebastian thrun, CMU, Warm-Up Assignment: Localization