Temporal Flow Policy adds physical time to robot policy memory
An arXiv robotics paper submitted on 14 Oct 2026 introduces Temporal Flow Policy (TFP), treating physical time as a missing dimension of robot policy memory. The authors argue that existing policy memories evolve with observation sequences, implicitly treating every update as the same amount of physical time, even though identical observation histories can span different durations and require different actions. TFP learns memory dynamics that retain past events and advance the memory state over measured time intervals, conditioning diffusion and flow-matching policies through that state and training with their native imitation objectives. In controlled comparisons with Diffusion Policy and π0.5, the authors report that TFP achieves higher average success than recurrent and state-space memories given the same intervals, and than a variant that feeds the interval to a learned gate; they also report gains on real-robot timing/memory tasks and LIBERO evaluations.
It suggests robot memory could be a state that evolves with elapsed physical time rather than a stored observation history, a design direction relevant to manipulation tasks that depend on precise timing.