CS393R: Autonomous Robots -- Resources

Tekkotsu

Week 0 (8/27): Class Overview

Week 1 (9/1, 9/3): Introduction to motion control

Week 2 (9/8, 9/10): Motion control continued

Week 3 (9/15,9/17): Probability/Sensing

Week 4 (9/27,9/29): Kalman Filters

Week 5 (9/29,10/1): Localization

• Slides from chapter 4 of the book: (ppt).
• Slides about the legged localization paper: (pdf).
• Some videos from the textbook authors.
• A page with some good videos and diagrams on particle filters
• Utility from Luis to upload parameter values to the Aibo wirelessly.

• A paper from Newcastle on Kalman filters for localization in RoboCup.
• Negative Information and Line Observations for Monte Carlo Localization.
  Todd Hester and Peter Stone.
  In IEEE International Conference on Robotics and Automation, May 2008.
• Adapting the Particle Size in Particle Filters Through KLD-Sampling
  Dieter Fox.
  In the International Journal of Robotic Research, IJRR, 2003.
  (An excellent description of robot localization - has some overlap with the textbook)
• Vision-Based Fast and Reactive Monte-Carlo Localization
  Thomas Roefer and Matthias Jungel.
  In the IEEE International Conference on Robotics and Automation, ICRA, 2003.
  (Another team's implementation details are in Sections III and IV)
• Fast and Robust Edge-Based Localization in the Sony Four-Legged Robot League
  Thomas Roefer and Matthias Jungel.
  In the Seventh International RoboCup Symposium, 2003.
  (On using field edges in localization)
• Making Use Of What You Don't see: Negative Information in Markov Localization
  Jan Hoffmann, Michael Spranger, Daniel Gohring and Matthias Jungel.
  In the IEEE International Conference on Intelligent Robots and Systems, IROS, 2005.
  (Recent article on using negative information in localization)
• Simultaneous Localization and Mapping (SLAM): Part I The Essential Algorithms
  Hugh Durrant-Whyte and Tim Bailey.
  

Week 6 (10/6,10/8): Walking

Week 7 (10/13,10/15): Action and sensor models

• Instance-Based Action Models for Fast Action Planning.
  Mazda Ahmadi and Peter Stone.
  In Ubbo Visser, Fernando Ribeiro, Takeshi Ohashi, and Frank Dellaert, editors, RoboCup-2007: Robot Soccer World Cup XI, Lecture Notes in Artificial Intelligence, pp. 1-16.
• The Spatial Semantic Hierarchy.
  Ben Kuipers
  AIJ, 2000.

Week 8 (10/20,10/22): Vision

• Slides from Tuesday on computer vision.
• Slides from Thursday on autonomous color learning and illumination invariance.
• The videos shown in class:
  Raw video
  Segmented video
  Segmented video over raw video
  Vision objects video
• The CMVision page.
• Research pages on UT Austin Villa color mapping, autonomous color learning, and follow-ups: basic vision; autonomous color learning; illumination invariance; learning with illumination invariance; hybrid representations;
• Work by Dan Stronger on Model-based vision.
• The UT Austin Villa 2003 Four-Legged Team, Extended version
  The University of Texas at Austin, Department of Computer Sciences, AI Laboratory Tech report UT-AI-TR-03-304.
  Read Sections 4, 4.1-4.3, 14.
• The UT Austin Villa 2004 RoboCup Four-Legged Team: Coming of Age
  Read Sections 3, 3.1,3.2 (and the first couple of appendices if you're interested)
• Using Layered Color Precision for a Self-Calibrating Vision System
  Matthias Jungel
  Robocup 2004
• Bayesian Color Estimation for Adaptive Vision-based Robot Localization.
  D. Schulz and D. Fox
  Proceedings of IROS, 2004.
• Color Learning on a Mobile Robot: Towards Full Autonomy under Changing Illumination
  Mohan Sridharan and Peter Stone.
  In The 20th International Joint Conference on Artificial Intelligence, pp. 2212
• B-Human Team Report and Code Release 2009.
  Thomas Röfer et al.

Week 9 (10/27, 10/29): Behavior Architectures

Week 10 (11/3,11/5): Behavior

Week 11 (11/10, 11/12): Multi-Robot Coordination

Week 12 (11/17, 11/19): Applications

Week 13 (11/24): Social Implications

Week 14 (12/1, 12/3): Project Demos