Integrating Flow Matching and Model Predictive Control for Safe Robot Navigation

Key points

• This work proposes a framework that combines flow-matching-based path planning with model predictive path-following control to integrate data-driven path generation with real-time, constraint-aware execution.1
• The flow-matching model captures multimodal path distributions, while the predictive controller adapts motion online to satisfy state and input constraints and avoid obstacles using updated local sensor information.1
• An event-triggered re-planning scheme incorporates predictive control solutions into subsequent path generation, promoting collision-free navigation in environments with previously unseen obstacles.1
• The framework provides a systematic approach to combining flow matching with predictive control for adaptive, data-driven navigation.1

Sources (1)

• [1]Integrating Flow Matching and Model Predictive Control for Safe Robot Navigation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 08:03 AM

  Learning-based path planners based on diffusion and flow matching can generate diverse trajectories from demonstrations but typically lack mechanisms for constraint satisfaction and collision avoidance during deployment.

  This work proposes a framework that combines flow-matching-based path planning with model predictive path-following control to integrate data-driven path generation with real-time, constraint-aware execution.

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