Graph-of-Constraints Model Predictive Control for Reactive Multi-agent Task and Motion Planning

• URL: http://arxiv.org/abs/2603.18400v2
• Date: Sun, 22 Mar 2026 20:10:56 GMT
• Title: Graph-of-Constraints Model Predictive Control for Reactive Multi-agent Task and Motion Planning
• Abstract summary: We introduce GoC-MPC, a generalized sequence-of-constraints framework integrated with MPC.<n>GoC-MPC naturally supports partially ordered tasks, dynamic agent coordination, and disturbance recovery.<n> Experiments demonstrate that GoC-MPC achieves higher success rates, significantly faster TAMP computation, and shorter overall paths compared to recent baselines.
• Score: 14.664456948527294
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 .

Related papers

• Improving the Efficiency of Language Agent Teams with Adaptive Task Graphs [52.26652574704317]
  Large language models (LLMs) are increasingly deployed in teams, yet existing coordination approaches often occupy two extremes.<n>We introduce Language Agent Teams for Task Evolution (LATTE), a framework for coordinating LLM teams inspired by distributed systems.
  arXiv  Detail & Related papers  (2026-05-07T14:19:17Z)
• Step-level Optimization for Efficient Computer-use Agents [51.29573359027217]
  We argue that strong computer-use agents remain expensive and slow in practice.<n>We propose an event-driven, step-level cascade for computer-use agents.
  arXiv  Detail & Related papers  (2026-04-29T19:59:36Z)
• UMBRELLA: Uncertainty-aware Multi-robot Reactive Coordination under Dynamic Temporal Logic Tasks [5.794342083222513]
  This paper addresses the problem of coordinating multi-robot systems for collaborative tasks involving dynamic and moving targets.<n>We explicitly model the uncertainty in target motion prediction via Conformal Prediction (CP)<n>The proposed framework (UMBRELLA) combines the Monte Carlo Tree Search (MCTS) over partial plans with uncertainty-aware rollouts.
  arXiv  Detail & Related papers  (2026-03-26T12:40:04Z)
• A Hierarchical Error-Corrective Graph Framework for Autonomous Agents with LLM-Based Action Generation [3.503249117641051]
  We propose a Hierarchical Error-Corrective Graph FrameworkforAutonomousAgentswithLLM-BasedActionGeneration(HECG),whichincorporates three core innovations.<n>MDTS: by integrating task quality metrics (Q), confidence/cost metrics (C), reward metrics (R), and LLM-based semantic reasoning scores (LLM-Score), MDTS achieves multi-dimensional alignment between quantitative performance and semantic context.<n> EMC: unlike simple confusion matrices or overall performance metrics, EMC provides structured attribution of task failures by categorizing errors into ten types, such as Strategy Errors (Strategy Whe) and Script
  arXiv  Detail & Related papers  (2026-03-09T13:46:00Z)
• MagicAgent: Towards Generalized Agent Planning [73.21129030631421]
  We present textbfMagicAgent, a series of foundation models specifically designed for generalized agent planning.<n>We introduce a lightweight and scalable synthetic data framework that generates high-quality trajectories across diverse planning tasks.<n>We show that MagicAgent-32B and MagicAgent-30B-A3B achieve superior performance across diverse open-source benchmarks.
  arXiv  Detail & Related papers  (2026-02-22T01:39:16Z)
• Multi-Agent Pointer Transformer: Seq-to-Seq Reinforcement Learning for Multi-Vehicle Dynamic Pickup-Delivery Problems [17.3780399150554]
  This paper proposes an end-to-end centralized decision-making framework based on sequence-to-sequence, named Multi-Agent Pointer Transformer (MAPT)<n>MAPT significantly outperforms existing baseline methods in terms of performance and substantial computational time advantages compared to classical operations research methods.
  arXiv  Detail & Related papers  (2025-11-21T17:32:10Z)
• Multi-Action Self-Improvement for Neural Combinatorial Optimization [0.979731979071071]
  Self-improvement models iteratively refine their policies by generating and imitating high-quality solutions.<n>These approaches fail to exploit the structure of problems involving the coordination of multiple agents.<n>We extend self-improvement to operate over joint multi-agent actions.
  arXiv  Detail & Related papers  (2025-10-14T08:26:27Z)
• Code-as-Monitor: Constraint-aware Visual Programming for Reactive and Proactive Robotic Failure Detection [56.66677293607114]
  We propose Code-as-Monitor (CaM) for both open-set reactive and proactive failure detection.<n>To enhance the accuracy and efficiency of monitoring, we introduce constraint elements that abstract constraint-related entities.<n>Experiments show that CaM achieves a 28.7% higher success rate and reduces execution time by 31.8% under severe disturbances.
  arXiv  Detail & Related papers  (2024-12-05T18:58:27Z)
• Dynamic Multi-Robot Task Allocation under Uncertainty and Temporal Constraints [52.58352707495122]
  We present a multi-robot allocation algorithm that decouples the key computational challenges of sequential decision-making under uncertainty and multi-agent coordination. We validate our results over a wide range of simulations on two distinct domains: multi-arm conveyor belt pick-and-place and multi-drone delivery dispatch in a city.
  arXiv  Detail & Related papers  (2020-05-27T01:10:41Z)
• Goal-Conditioned End-to-End Visuomotor Control for Versatile Skill Primitives [89.34229413345541]
  We propose a conditioning scheme which avoids pitfalls by learning the controller and its conditioning in an end-to-end manner. Our model predicts complex action sequences based directly on a dynamic image representation of the robot motion. We report significant improvements in task success over representative MPC and IL baselines.
  arXiv  Detail & Related papers  (2020-03-19T15:04:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.