用世界模型提升流匹配策略的推理能力，无需修改训练目标
FlowMPC: Improving Flow Matching policies with World Models
- 将流匹配策略与学习的世界模型结合，测试时用MPPI规划动作序列
- 在PickCube和PickSingleYCB任务上，成功率显著提升，尤其体现在最终成功上
- 适合做机器人操作中多模态行为克隆的改进，不改变原有训练方式
流匹配（Flow Matching, FM）是一种在多模态动作空间中进行行为克隆的强大方法 [Jiang et al., 2025]，但由于其未直接优化期望回报，测试时表现仍有提升空间。本文探讨是否可通过学习的世界模型，结合模型预测路径积分（MPPI）规划，对由FM策略生成的动作序列进行优化。基于TD-MPC2 [Hansen et al., 2024]，提出FlowMPC框架，将模仿学习的FM策略与学习的世界模型结合，用于ManiSkill操控任务 [Tao et al., 2025]。在PickCube和PickSingleYCB任务上，引入世界模型后性能优于纯FM策略，端到端成功率提升明显。结果表明，基于世界模型的规划可有效补充流式模仿策略，且无需修改原训练目标。
原文摘要 · Abstract (English)
Flow Matching (FM) is a powerful approach for behavior cloning in multimodal action spaces [Jiang et al., 2025], but because it is not trained to directly maximize expected return, there is still room to improve how FM policies act at test time. This work investigates whether a learned world model can improve FM policies by enabling Model Predictive Path Integral (MPPI) planning over candidate action sequences proposed by the policy. Building on TD-MPC2 [Hansen et al., 2024], I introduce FlowMPC, a framework that combines an imitation-learned FM policy with a learned world model for test-time planning in ManiSkill manipulation tasks [Tao et al., 2025]. Across PickCube and PickSingleYCB, adding the world model improved performance over the FM policy alone, with especially clear gains in end-of-episode success. These results suggest that world-model-based planning can effectively complement flow-based imitation policies without modifying the FM training objective.
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