Title: Translating Flow to Policy via Hindsight Online Imitation
Primary area: applications to robotics, autonomy, planning
Keywords: Robotic Manipulation, Imitation Learning, Pixel Flow

Abstract: Recent advances in hierarchical robot systems leverage a high-level planner to propose task plans and a low-level policy to generate robot actions.
This design allows training the planner on action-free or even non-robot data sources (e.g., videos), providing transferable high-level guidance.
Nevertheless, grounding these high-level plans into executable actions remains challenging, especially with the limited availability of high-quality robot data.
To this end, we propose to improve the low-level policy through online interactions.
Specifically, our approach collects online rollouts, retrospectively annotates the corresponding high-level goals from achieved outcomes, and aggregates these hindsight-relabeled experiences to update a goal-conditioned imitation policy.
Our method, Hindsight Flow-conditioned Online Imitation (HinFlow), instantiates this idea with 2D point flows as the high-level planner.
Across diverse manipulation tasks, our method achieves 
more than $2\times $
performance improvement over the base policy, significantly outperforming the existing methods.
Moreover, our framework enables policy acquisition from planners trained on cross-embodiment video data, demonstrating its potential for scalable and transferable robot learning.