  Robots are good at performing repetitive tasks in modern manufacturing
industries. However, robot motions are mostly planned and preprogrammed with a
notable lack of adaptivity to task changes. Even for slightly changed tasks,
the whole system must be reprogrammed by robotics experts. Therefore, it is
highly desirable to have a flexible motion planning method, with which robots
can adapt to specific task changes in unstructured environments, such as
production systems or warehouses, with little or no intervention from
non-expert personnel. In this paper, we propose a user-guided motion planning
algorithm in combination with the reinforcement learning (RL) method to enable
robots automatically generate their motion plans for new tasks by learning from
a few kinesthetic human demonstrations. To achieve adaptive motion plans for a
specific application environment, e.g., desk assembly or warehouse
loading/unloading, a library is built by abstracting features of common human
demonstrated tasks. The definition of semantical similarity between features in
the library and features of a new task is proposed and further used to
construct the reward function in RL. The RL policy can automatically generate
motion plans for a new task if it determines that new task constraints can be
satisfied with the current library and request additional human demonstrations.
Multiple experiments conducted on common tasks and scenarios demonstrate that
the proposed user-guided RL-assisted motion planning method is effective.
