Abstract Multi-agent Task and Motion Planning (TAMP) has become a key research frontier in autonomous agents, supported by the rapid evolution of Embodied AI and the increasing deployment of robot teams in real-world environments. Unlike single-agent planning, multi-agent TAMP must jointly reason about discrete task allocation, continuous motion feasibility, communication constraints, and execution uncertainties. This survey provides a systematic analysis of recent advances in multi-agent TAMP from 2021 to 2025. We first integrate and refine existing taxonomies, and propose a unified classification framework based on three dimensions. A total of 60 representative works are reviewed under this taxonomy, revealing dominant solution strategies. Furthermore, this survey summarizes emerging research trends, including LLM-based TAMP, decentralized adaptive planning frameworks, and reinforcement learning based TAMP solutions. Finally, we highlight key future directions, such as VLM-based planning, Theory-based planning, data privacy and long-horizon task planning.
Tao et al. (2026) studied this question.