DANG AT cs.brown.edu

CV FGL PGP

Projects:

• Learning Robot Soccer from Demonstration
• Discovering Categories in Robot Sensor Data

Publications:

• Daniel H Grollman and Odest Chadwicke Jenkins. Sparse incremental learning for interactive robot control policy estimation. In IEEE International Conference on Robotics and Automation, Pasadena, CA, USA, May 2008. [ bib | text ]
• Micah Lapping-Carr, Odest Chadwicke Jenkins, Daniel H Grollman, Jonas N Schwertfeger, and Theodora R Hinkle. Wiimote interfaces for lifelong robot learning. In AAAI Spring Symposium, Menlo Park, CA, USA, March 2008. [ bib | text ]
• Daniel H Grollman and Odest Chadwicke Jenkins. (machine) learning robot control policies. In NIPS Workshop on Robotics Challenges for Machine Learning, Whistler, BC, CAN, December 2007. [ bib | poster | text ]
• Daniel H Grollman and Odest Chadwicke Jenkins. Learning robot soccer from demonstration: Ball grasping. In Robotics: Science and Systems - Robot Manipulation: Sensing and Adapting to the Real World, Atlanta, GA, USA, June 2007. [ bib | poster | text ]
• Daniel H Grollman and Odest Chadwicke Jenkins. Learning robot soccer skills from demonstration. In IEEE International Conference on Development and Learning, London, UK, July 2007. [ bib | teaser | poster | text ]
• Daniel H Grollman and Odest Chadwicke Jenkins. Dogged learning for robots. In IEEE International Conference on Robotics and Automation, Rome, Italy, April 2007. [ bib | video | slides | text ]
• Daniel H. Grollman, Odest Chadwicke Jenkins, and Frank Wood. Discovering natural kinds of robot sensory experiences in unstructured environments. Journal of Field Robotics, 23(11-12):1077-1089, Nov-Dec 2006. [ bib | text | http ]
• Daniel H. Grollman, Odest Chadwicke Jenkins, and Frank Wood. Discovering natural kinds of robot sensory experiences in unstructured environments. In North East Student Colloquium on Artificial Intelligence, Ithica, NY, USA, April 2006. [ bib | slides | text ]
• Daniel H. Grollman, Odest Chadwicke Jenkins, and Frank Wood. Discovering natural kinds of robot sensory experiences in unstructured environments. In NIPS Workshop Machine Learning Based Robotics in Unstructured Environments., Whistler, BC, CAN, December 2005. [ bib | slides | text ]
• Carsten Magerkurth, Time Engelke, and Dan Grollman. A component based architecture for distributed, pervasive gaming applications. In ACM SIGCHI International Conference on Advances in Computer Entertainment Technology, Hollywood, CA, USA, June 2006. [ bib | text ]
• Daniel H Grollman, Odest Chadwicke Jenkins, and Frank Wood. Extensible data-driven classification of robot sensor data. Technical report, Brown University Department of Computer Science, 2005. [ bib | poster | text | .html ]
• David Eigen, Daniel Grollman, David Laidlaw, Benjamin Greenberg, and Erin Einbinder. Visualizing deep brain stimulation settings in obsessive compulsive disorder. In International Conference on Computer Graphics and Interactive Techniques, page 110, Los Angeles, California, 2004. [ bib | text ]
• Stuart Andrews, Lijuan Cai, David Gondek, Amy Greenwald, Daniel Grollman, Arni Mar Jonsson, Keith Hall, Matthew Lease, Bryant Ng, John Raiti, Victoria Sweetser, and Jenine Turner. Astrology: The study of astro teller. In International Conference on Machine Learning Physiological Modeling Contest, 2004. [ bib | text | .html ]

Other Presentations:

• Learning Robot Soccer Skills from Demonstration 3rd Annual New England Maniputlation Symposium
  [slides]

Media:

• Goodman L., Fasten L.(photo) Let's Make a Game. Brown Alumni Magazine July/Aug 2006
  [text | link]
• Robocup Toonami
  [vid]
• Learning Autonomous Robot Control from Demonstration. Conduit Volume 15, no. 1, pp. 4-5, spring 2006.
  [text]
• Grollman, D. Robert Redford is (like) a robot Conduit Volume 14, no. 1, pp. 21-22, spring 2005.
  [text]

Code:

Please email me if you are interested in using any code that I use/have written.
All code is in C++ unless otherwise noted.

• The Dogged Learning Framework
• Learning Boxes, I am very interested in expanding this list.
  • Sparse Online Gaussian Processes, based on Dr. Csato's thesis
  • Locally Weighted Projection Regression, adapted from Dr. Vijayakumar's code

Other:

I've managed to connect my macbook to my Aibos. Details here