  Multi-agent systems can be extremely efficient when working concurrently and
collaboratively, e.g., for transportation, maintenance, search and rescue.
Coordination of such teams often involves two aspects: (i) selecting
appropriate sub-teams for different tasks; (ii) designing collaborative control
strategies to execute these tasks. The former aspect can be combinatorial
w.r.t. the team size, while the latter requires optimization over joint
state-spaces under geometric and dynamic constraints. Existing work often
tackles one aspect by assuming the other is given, while ignoring their close
dependency. This work formulates such problems as combinatorial-hybrid
optimizations (CHO), where both the discrete modes of collaboration and the
continuous control parameters are optimized simultaneously and iteratively. The
proposed framework consists of two interleaved layers: the dynamic formation of
task coalitions and the hybrid optimization of collaborative behaviors. Overall
feasibility and costs of different coalitions performing various tasks are
approximated at different granularities to improve the computational
efficiency. At last, a Nash-stable strategy for both task assignment and
execution is derived with provable guarantee on the feasibility and quality.
Two non-trivial applications of collaborative transportation and dynamic
capture are studied against several baselines.
