Planning and Moving in Dynamic Environments - A Statistical Machine Learning Approach.
S Vijayakumar, M Toussaint, G Petkos and M Howard
Creating Brain-Like Intelligence, From Basic Principles to Complex Intelligent Systems
Lecture Notes in Computer Science
Springer Berlin / Heidelberg
In this chapter, we develop a new view on problems of move-
ment control and planning from a Machine Learning perspective. In this
view, decision making, control, and planning are all considered as an
inference or (alternately) an information processing problem, i.e., a prob-
lem of computing a posterior distribution over unknown variables condi-
tioned on the available information (targets, goals, constraints). Further,
problems of adaptation and learning are formulated as statistical learning
problems to model the dependencies between variables. This approach
naturally extends to cases when information is missing, e.g., when the
context or load needs to be inferred from interaction; or to the case of
apprentice learning where, crucially, latent properties of the observed
behavior are learnt rather than the motion copied directly.
With this account, we hope to address the long-standing problem of
designing adaptive control and planning systems that can flexibly be cou-
pled to multiple sources of information (be they of purely sensory nature
or higher-level modulations such as task and constraint information) and
equally formulated on any level of abstraction (motor control variables or
symbolic representations). Recent advances in Machine Learning provide
a coherent framework for these problems.