  Compared with the widely investigated homogeneous multi-robot collaboration,
heterogeneous robots with different capabilities can provide a more efficient
and flexible collaboration for more complex tasks. In this paper, we consider a
more challenging heterogeneous ad hoc teamwork collaboration problem where an
ad hoc robot joins an existing heterogeneous team for a shared goal.
Specifically, the ad hoc robot collaborates with unknown teammates without
prior coordination, and it is expected to generate an appropriate cooperation
policy to improve the efficiency of the whole team. To solve this challenging
problem, we leverage the remarkable potential of the large language model (LLM)
to establish a decentralized heterogeneous ad hoc teamwork collaboration
framework that focuses on generating reasonable policy for an ad hoc robot to
collaborate with original heterogeneous teammates. A training-free hierarchical
dynamic planner is developed using the LLM together with the newly proposed
Interactive Reflection of Thoughts (IRoT) method for the ad hoc agent to adapt
to different teams. We also build a benchmark testing dataset to evaluate the
proposed framework in the heterogeneous ad hoc multi-agent tidying-up task.
Extensive comparison and ablation experiments are conducted in the benchmark to
demonstrate the effectiveness of the proposed framework. We have also employed
the proposed framework in physical robots in a real-world scenario. The
experimental videos can be found at https://youtu.be/wHYP5T2WIp0.
