My research is at the intersection of robotics and machine learning.
Specifically, in the area of Human-Robot Interaction I focus on
Human-Robot Policy Transfer, where a human user instantiates, on a
robot, an autonomous control policy to perform a desired task. I
approach this problem from a Learning from Demonstration standpoint,
whereby the control policy is estimated from data representative of
demonstrations of the task. I use teleoperative demonstration to
avoid correspondence issues and frame tasks as learning finite state
machines. Within this context, I have focused on learning the number
of machine states and their individual policies using nonparametric
Bayesian methods.
Recent Activities:
Multimap regression for perceptual aliasing in learning finite state
machine robot controllers from interactive demonstration.
Daniel H Grollman and Odest Chadwicke Jenkins.
In RSS Workshop on Regression in Robotics, Seattle, Washington,
USA, June 2009.
Winner: Best Poster.
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Poster ]
Rgame: Robotic gaming.
ICRA 2009 Robot Challenge - HRI, Kobe, Japan, May 2009.
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Handout |
Poster ]
Learning multimap robot control policies from demonstration.
University of Massachusetts, Amherst, Amherst, MA, April 2009.
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Slides ]
Contact info:
Daniel Grollman
Department of Computer Science
Brown University, Box 1910
115 Waterman St., 4th Floor
Providence, RI, USA 02912-1910