  We aim to enable an autonomous robot to learn new skills from demo videos and
use these newly learned skills to accomplish non-trivial high-level tasks. The
goal of developing such autonomous robot involves knowledge representation,
specification mining, and automated task planning. For knowledge
representation, we use a graph-based spatial temporal logic (GSTL) to capture
spatial and temporal information of related skills demonstrated by demo videos.
We design a specification mining algorithm to generate a set of parametric GSTL
formulas from demo videos by inductively constructing spatial terms and
temporal formulas. The resulting parametric GSTL formulas from specification
mining serve as a domain theory, which is used in automated task planning for
autonomous robots. We propose an automatic task planning based on GSTL where a
proposer is used to generate ordered actions, and a verifier is used to
generate executable task plans. A table setting example is used throughout the
paper to illustrate the main ideas.
