Weijun Dong

Learning from vast video data allows point-flow-based planners to create generalizable task plans, guiding robots via future keypoint trajectories. But how can we ensure low-level execution doesn't become the system's bottleneck? Introducing our #ICLR2026 paper, HinFlow: Translating Flow to Policy via Hindsight Online Imitation

Proud to be a co-first author of this work! 🎉 Really excited to share our #ICLR2026 paper — HinFlow: Translating Flow to Policy via Hindsight Online Imitation. Great collaboration with the entire team!

Huge thanks to collaborators and supervisors: Yitian Zheng, @YeZhangchen0731, @ShengjieWa34067, Yuyang Liu, Chongjie Zhang, @ChuanWen15, and @gao_young!

We hope our work hints at the broader potential of hindsight relabeling — perhaps as a useful ingredient in the era of scaling up robotic foundation models. Check for more details! Webpage: dwjshift.github.io/HinFlow Arxiv: arxiv.org/abs/2512.19269 Code: github.com/yzc0731/HinFlow

Learning from vast video data allows point-flow-based planners to create generalizable task plans, guiding robots via future keypoint trajectories. But how can we ensure low-level execution doesn't become the system's bottleneck? Introducing our #ICLR2026 paper, HinFlow: Translating Flow to Policy via Hindsight Online Imitation