ChronoFlow Policy: Unifying Past-Future Interaction Flow in Visuomotor Policy Learning

Visual signals play a crucial role in policy learning by en-abling models to capture object motion and interaction dynamics. Just ashumans reason about actions using both past experience and anticipatedoutcomes, effective policies should integrate past interactions with futurepredictions. However, existing visuomotor policies typically model eitherhistorical context or future dynamics in isolation, lacking a unified tem-poral representation of interaction dynamics. In this work, we introduceChronoFlow, a temporally unified representation that captures past,current, and future interaction dynamics through sparse 3D keypointsof both objects and the gripper. Based on this representation, we proposeChronoFlow-Policy, a diffusion-based visuomotor policy that jointlylearns ChronoFlow and action sequences through a co-training objective.Experiments on 14 simulated tasks and 5 real-world manipulation tasksdemonstrate that ChronoFlow-Policy consistently outperforms strongdiffusion-policy baselines and improves robustness in long-horizon andnon-Markovian manipulation scenarios. Code and models will be releasedat https://github.com/The-kamisato-Sii/ChronoFlow-Policy.