Lab Meeting 3/23, 2010 (fish60): Learning to Search: Functional Gradient Techniques for Imitation Learning

I will try to present this one:

Learning to Search: Functional Gradient Techniques for Imitation Learning
Nathan Ratliff, David Silver, J. Andrew Bagnell

Submitted to Autonomous Robotics Special Issue on Robot Learning, 2009

[download draft]

Abstract:
While planning algorithms have shown success in many real-world applications ranging from legged locomotion to outdoor unstructured navigation, such algorithms rely on fully specified cost functions that map sensor readings and environment models to quantifiable costs. Such cost functions are usually manually designed and programmed. Recently, a set of techniques has been developed that explore learning these functions from expert human demonstration. These algorithms apply an inverse optimal control approach to find a cost function for which planned behavior mimics an expert's demonstration.
The work we present extends the Maximum Margin Planning (MMP) frame- work to admit learning of more powerful, non-linear cost functions. These algorithms, known collectively as LEARCH (LEArning to seaRCH ), are simpler to implement than most existing methods, more efficient than previous attempts at non-linearization, more naturally satisfy common constraints on the cost function, and better represent our prior beliefs about the function's form.