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Robot Skill Learning and the Effect of Basis Function Choice
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@TechReport{Scheider:TR1992,
  author = 	 {J.G. Schneider and Christopher M. Brown},
  title = 	 {Robot Skill Learning and the Effect of Basis Function Choice},
  institution =  {Comp. Sci. Dept. University of Rochester},
  year = 	 {1992},
  number = 	 {437},
  address = 	 {Rochester NY},
  month = 	 {September},
  abstract =     { 
We present a computational, constructive theory of tunable, open loop trajectory 
skills. A skill is a controller whose outputs achieve any task in a space 
characterized by n parameters, n > 1. Throwing a ball at a target is a 3-
dimensional task if the target may be anywhere within a 3-dimensional volume. 
Repetitous pick and place tasks are zero-dimensional, and thus not skills. Skills 
are performed open loop for speed reasons: we assume the entire command sequence 
is generated before any feedback can become available. We do not assume prior 
knowledge of plant or task models, so skills must be at least partly learned. A 
skill output is a vector of values---in our work so far it is generated as the 
sum of a base vector and a weighted change vector whose weight accomplishes the 
tuning. Learning consists of a search for the best set of base and change vectors. 
An interpretation process maps skill outputs into sequences of commands for the 
plant by using basis functions (given a priori in this paper). The basis functions 
may be arbitrarily complex. We claim that appropriate basis functions can speed up 
the learning process and overcome the limitations of the linear trajectory tuning 
algorithm. This report describes a skill learning algorithm and experiments done 
with various basis functions and control methods for a one-dimensional throwing 
task. It concludes with a discussion of future work in learning basis functions, 
higher dimensional tasks, and comparisons against common learning and control 
algorithms.    }

}