© 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