Combining learning from demonstration and search algorithm for dynamic goal-directed assembly task planning

Ahmed Khodair Abbas Abbas, Frederic Maire, Feras Dayoub, Sareh Rowlands

Abstract:

In this paper, a learning approach is proposed
to enable robots to generate assembly plans to
assist users during assembly tasks. This plan is
generated using a CAD model that represents
a fully assembled goal object. The CAD model
is induced by tracking a demonstrator assembling the components of that object. Forward
assembly planning is an NP-hard problem, but
we introduce pruning methods for the search
tree that make the approach practical. Our dynamic planner generates an assembly plan that
a user can follow to reproduce an identical object. Our system guides the user during the
assembly task by suggesting parts to connect
and how to connect them. If the user deviates
from the suggested plan, the system analyses
the unexpected state, and determines whether
the user’s action has brought the partially assembled object to a doomed state from which
the goal state is not reachable. In this case, the
user is warned about the dead-end. Otherwise,
the system dynamically revises the current assembly plan. The system is validated with experiments on IKEA and LEGO objects.