  Multi-object transport using multi-robot systems has the potential for
diverse practical applications such as delivery services owing to its efficient
individual and scalable cooperative transport. However, allocating
transportation tasks of objects with unknown weights remains challenging.
Moreover, the presence of infeasible tasks (untransportable objects) can lead
to robot stoppage (deadlock). This paper proposes a framework for dynamic task
allocation that involves storing task experiences for each task in a scalable
manner with respect to the number of robots. First, these experiences are
broadcasted from the cloud server to the entire robot system. Subsequently,
each robot learns the exclusion levels for each task based on those task
experiences, enabling it to exclude infeasible tasks and reset its task
priorities. Finally, individual transportation, cooperative transportation, and
the temporary exclusion of tasks considered infeasible are achieved. The
scalability and versatility of the proposed method were confirmed through
numerical experiments with an increased number of robots and objects, including
unlearned weight objects. The effectiveness of the temporary deadlock avoidance
was also confirmed by introducing additional robots within an episode. The
proposed method enables the implementation of task allocation strategies that
are feasible for different numbers of robots and various transport tasks
without prior consideration of feasibility.
