  It is critical for advanced manufacturing machines to autonomously execute a
task by following an end-user's natural language (NL) instructions. However, NL
instructions are usually ambiguous and abstract so that the machines may
misunderstand and incorrectly execute the task. To address this NL-based
human-machine communication problem and enable the machines to appropriately
execute tasks by following the end-user's NL instructions, we developed a
Machine-Executable-Plan-Generation (exePlan) method. The exePlan method
conducts task-centered semantic analysis to extract task-related information
from ambiguous NL instructions. In addition, the method specifies machine
execution parameters to generate a machine-executable plan by interpreting
abstract NL instructions. To evaluate the exePlan method, an industrial robot
Baxter was instructed by NL to perform three types of industrial tasks {'drill
a hole', 'clean a spot', 'install a screw'}. The experiment results proved that
the exePlan method was effective in generating machine-executable plans from
the end-user's NL instructions. Such a method has the promise to endow a
machine with the ability of NL-instructed task execution.
