Describing constraint-based assembly tasks in unstructured natural language

Task-level industrial robot programming is a mundane, error-prone activity requiring expertise and skill. Since humans easily communicate with natural language (NL), it may be attractive to use speech or text as instruction means for robots. However, there has to be a substantial amount of knowledge in the system to translate the high-level language instructions to executable robot programs.
In this paper, the method of Stenmark and Nugues (2013) for natural language programming of robotized assembly tasks is extended. The core idea of the method is to use a generic semantic parser to produce a set of predicate-argument structures from the input sentences. The algorithm presented here facilitates extraction of more complicated, advanced task instructions involving cardinalities, conditionals, parallelism and constraint-bounded programs, besides plain sequences of commands.
The bottleneck of this approach is the availability of easily parametrizable robotic skills and functionalities in the system, rather than the natural language understanding by itself.