Multi-robot systems can greatly enhance efficiency through coordination and collaboration, yet in practice, full- time communication is rarely available and interactions are constrained to close-range exchanges. Existing methods either maintain all-time connectivity, rely on fixed schedules, or adopt pairwise protocols, but none adapt effectively to dynamic spatio- temporal task distributions under limited communication, result- ing in suboptimal coordination. To address this gap, we propose CoCoPlan, a unified framework that co-optimizes collaborative task planning and team-wise intermittent communication. Our approach integrates a branch-and-bound architecture that jointly encodes task assignments and communication events, an adaptive objective function that balances task efficiency against communi- cation latency, and a communication event optimization module that strategically determines when, where and how the global connectivity should be re-established. Extensive experiments demonstrate that it outperforms state-of-the-art methods by achieving a 22.4% higher task completion rate, reducing com- munication overhead by 58.6%, and improving the scalability by supporting up to 100 robots in dynamic environments. Hardware experiments include the complex 2D office environment and large-scale 3D disaster-response scenario.

In this work, a multi-agent system handles complex tem- porally constrained tasks continuously released online. We formulate an online co-optimization problem to synthesize the collaborative task execution and communication strate- gies simultaneously. Unlike periodic team-wise or intermittent pair-wise communication protocols, our approach dynami- cally optimizes when, where and how agents communicate or collaborate based on real-time task specifications gathered online. To achieve this, we develop a branch-and-bound (BnB) framework with joint encoding of task planning and com- munication events within unified search nodes. During the tree expansion, nodes incrementally build temporal constraints while encapsulating both task sequences and communication events. A novel objective function balances the execution efficiency against communication latency for robustness across spatio-temporal task distributions. Moreover, communication events are optimized via an iterative algorithm that strategi- cally determines the communication locations and timings.