  Robotic systems may frequently come across similar manipulation planning
problems that result in similar motion plans. Instead of planning each problem
from scratch, it is preferable to leverage previously computed motion plans,
i.e., experiences, to ease the planning. Different approaches have been
proposed to exploit prior information on novel task instances. These methods,
however, rely on a vast repertoire of experiences and fail when none relates
closely to the current problem. Thus, an open challenge is the ability to
generalise prior experiences to task instances that do not necessarily resemble
the prior. This work tackles the above challenge with the proposition that
experiences are "decomposable" and "malleable", i.e., parts of an experience
are suitable to relevantly explore the connectivity of the robot-task space
even in non-experienced regions. Two new planners result from this insight:
experience-driven random trees (ERT) and its bi-directional version ERTConnect.
These planners adopt a tree sampling-based strategy that incrementally extracts
and modulates parts of a single path experience to compose a valid motion plan.
We demonstrate our method on task instances that significantly differ from the
prior experiences, and compare with related state-of-the-art experience-based
planners. While their repairing strategies fail to generalise priors of tens of
experiences, our planner, with a single experience, significantly outperforms
them in both success rate and planning time. Our planners are implemented and
freely available in the Open Motion Planning Library.
