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This paper introduces Graph-of-Constraints Model Predictive Control (GoC-MPC), a novel framework for multi-agent Task and Motion Planning (TAMP) that integrates a generalized sequence-of-constraints approach with Model Predictive Control. GoC-MPC addresses limitations of existing methods by supporting partially ordered tasks and dynamic agent assignments, enabling adaptation to disturbances. Experiments demonstrate that GoC-MPC achieves higher success rates, faster computation, and shorter paths compared to baselines in multi-agent manipulation tasks using visual observations alone.
Coordinating multi-robot teams to complete manipulation tasks just got easier: GoC-MPC handles dynamic task assignments and disturbances without training data or environment models.
Sequences of interdependent geometric constraints are central to many multi-agent Task and Motion Planning (TAMP) problems. However, existing methods for handling such constraint sequences struggle with partially ordered tasks and dynamic agent assignments. They typically assume static assignments and cannot adapt when disturbances alter task allocations. To overcome these limitations, we introduce Graph-of-Constraints Model Predictive Control (GoC-MPC), a generalized sequence-of-constraints framework integrated with MPC. GoC-MPC naturally supports partially ordered tasks, dynamic agent coordination, and disturbance recovery. By defining constraints over tracked 3D keypoints, our method robustly solves diverse multi-agent manipulation tasks-coordinating agents and adapting online from visual observations alone, without relying on training data or environment models. Experiments demonstrate that GoC-MPC achieves higher success rates, significantly faster TAMP computation, and shorter overall paths compared to recent baselines, establishing it as an efficient and robust solution for multi-agent manipulation under real-world disturbances. Our supplementary video and code can be found at https://sites.google.com/view/goc-mpc/home .
