We demonstrate the automatic transfer of an
assembly task from human to robot. This work extends efforts
showing the utility of linguistic models in verifiable robot
control policies by now performing real visual analysis of
human demonstrations to automatically extract a policy for the
task. This method tokenizes each human demonstration into a
sequence of object connection symbols, then transforms the set
of sequences from all demonstrations into an automaton, which
represents the task-language for assembling a desired object.
Finally, we combine this assembly automaton with a kinematic
model of a robot arm to reproduce the demonstrated task.
We describe several algorithms used for the inference of linguistic robot policies from human
demonstration. First, tracking and match objects using the Hungarian Algorithm. Then, we convert Regular Expressions to Nondeterministic Finite Automata (NFA) using the McNaughton-Yamada-Thompson
Algorithm. Next, we use Subset Construction to convert to a Deterministic Finite Automaton. Finally, we
minimize finite automata using either Hopcroft's Algorithm or Brzozowski's Algorithm.