ConFlow: Constraints-Guided Flow Matching Improves Robot Motion Generation
A new framework called ConFlow introduces constraint-guided flow matching for robot motion generation by integrating constraints directly into the training process using differentiable barrier or cost functions. The method leverages conditional Gaussian processes and uses infeasible demonstrations as negative supervision to enhance constraint satisfaction. Experiments on a two-robot navigation task show that ConFlow achieves lower collision rates and higher trajectory quality compared to standard flow matching approaches.
Why it matters: Integrating constraints during training addresses the gap between training and inference in generative motion models, potentially improving the safety and reliability of robot motion planning.
Full story at: arXiv Robotics ↗