Optimized Execution of Action Chains through Subgoal Refinement (bibtex)
by F Stulp and M Beetz
Abstract:
In this paper we propose a novel computation model for the execution of abstract action chains. In this computation model a robot first learns situation-specific performance models of abstract actions. It then uses these models to automatically specialize the abstract actions for their execution in a given action chain. This specialization results in refined chains that are optimized for performance. As a side effect this behavior optimization also appears to produce action chains with seamless transitions between actions.
Reference:
Optimized Execution of Action Chains through Subgoal Refinement (F Stulp and M Beetz), 2005. (ICAPS Workshop “Plan Execution: A Reality Check”)
Bibtex Entry:
@book{stulp_optimized_2005, title = {Optimized Execution of Action Chains through Subgoal Refinement}, year = {2005}, author = {F Stulp and M Beetz}, abstract = {In this paper we propose a novel computation model for the execution of abstract action chains. In this computation model a robot first learns situation-specific performance models of abstract actions. It then uses these models to automatically specialize the abstract actions for their execution in a given action chain. This specialization results in refined chains that are optimized for performance. As a side effect this behavior optimization also appears to produce action chains with seamless transitions between actions.}, url = {http://ic.arc.nasa.gov/people/sailesh/icaps2005wksp/}, }
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