  This work presents Adaptive Robot Coordination (ARC), a novel hybrid
framework for multi-robot motion planning (MRMP) that employs local subproblems
to resolve inter-robot conflicts. ARC creates subproblems centered around
conflicts, and the solutions represent the robot motions required to resolve
these conflicts. The use of subproblems enables an inexpensive hybrid
exploration of the multi-robot planning space. ARC leverages the hybrid
exploration by dynamically adjusting the coupling and decoupling of the
multi-robot planning space. This allows ARC to adapt the levels of coordination
efficiently by planning in decoupled spaces, where robots can operate
independently, and in coupled spaces where coordination is essential. ARC is
probabilistically complete, can be used for any robot, and produces efficient
cost solutions in reduced planning times. Through extensive evaluation across
representative scenarios with different robots requiring various levels of
coordination, ARC demonstrates its ability to provide simultaneous scalability
and precise coordination. ARC is the only method capable of solving all the
scenarios and is competitive with coupled, decoupled, and hybrid baselines.
