  Multi-robot cooperative transport is crucial in logistics, housekeeping, and
disaster response. However, it poses significant challenges in environments
where objects of various weights are mixed and the number of robots and objects
varies. This paper presents Task-priority Intermediated Hierarchical
Distributed Policies (TIHDP), a multi-agent Reinforcement Learning (RL)
framework that addresses these challenges through a hierarchical policy
structure. TIHDP consists of three layers: task allocation policy (higher
layer), dynamic task priority (intermediate layer), and robot control policy
(lower layer). Whereas the dynamic task priority layer can manipulate the
priority of any object to be transported by receiving global object information
and communicating with other robots, the task allocation and robot control
policies are restricted by local observations/actions so that they are not
affected by changes in the number of objects and robots. Through simulations
and real-robot demonstrations, TIHDP shows promising adaptability and
performance of the learned multi-robot cooperative transport, even in
environments with varying numbers of robots and objects. Video is available at
https://youtu.be/Rmhv5ovj0xM
