FlowMPC framework enhances imitation learning with world models

Researchers have developed FlowMPC, a new framework that enhances the performance of Flow Matching (FM) policies in multimodal action spaces. By integrating a learned world model with an imitation-learned FM policy, FlowMPC enables Model Predictive Path Integral (MPPI) planning for improved test-time actions. Experiments on ManiSkill manipulation tasks, specifically PickCube and PickSingleYCB, demonstrated that FlowMPC outperformed FM policies alone, showing notable gains in end-of-episode success rates. This approach suggests that world-model-based planning can effectively complement FM imitation policies without altering their training objective. AI

IMPACT Enhances imitation learning for robotics by combining flow matching with world models for improved planning.

RANK_REASON The cluster contains a research paper detailing a new framework and experimental results. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Flow Matching for Generative Modeling
• FlowMPC
• Hansen et al., 2024
• Jiang et al., 2025
• ManiSkill
• PickCube
• PickSingleYCB
• Tao et al., 2025
• TD-MPC2

• paper
• model release

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