  A novel skill learning approach is proposed that allows a robot to acquire
human-like visuospatial skills for object manipulation tasks. Visuospatial
skills are attained by observing spatial relationships among objects through
demonstrations. The proposed Visuospatial Skill Learning (VSL) is a goal-based
approach that focuses on achieving a desired goal configuration of objects
relative to one another while maintaining the sequence of operations. VSL is
capable of learning and generalizing multi-operation skills from a single
demonstration, while requiring minimum prior knowledge about the objects and
the environment. In contrast to many existing approaches, VSL offers
simplicity, efficiency and user-friendly human-robot interaction. We also show
that VSL can be easily extended towards 3D object manipulation tasks, simply by
employing point cloud processing techniques. In addition, a robot learning
framework, VSL-SP, is proposed by integrating VSL, Imitation Learning, and a
conventional planning method. In VSL-SP, the sequence of performed actions are
learned using VSL, while the sensorimotor skills are learned using a
conventional trajectory-based learning approach. such integration easily
extends robot capabilities to novel situations, even by users without
programming ability. In VSL-SP the internal planner of VSL is integrated with
an existing action-level symbolic planner. Using the underlying constraints of
the task and extracted symbolic predicates, identified by VSL, symbolic
representation of the task is updated. Therefore the planner maintains a
generalized representation of each skill as a reusable action, which can be
used in planning and performed independently during the learning phase. The
proposed approach is validated through several real-world experiments.
