AutoSpeed: Annotation-Free Stage-Adaptive Motion Speed Learning for Robot Manipulation

Different stages of manipulation tasks exhibit varying lev-els of difficulty, suggesting stage-dependent motion speeds and temporalprediction horizons. However, existing IL-based visuomotor policies typ-ically imitate the execution speed of expert demonstrations and operatewith a fixed temporal prediction horizon, limiting flexibility and over-all task throughput. In this paper, we introduce AutoSpeed, a model-agnostic learning framework that enables existing visuomotor policies topredict trajectories with stage-adaptive motion speeds, without requir-ing speed or stage annotations. We treat future trajectories at differentspeeds as candidate optimization targets, evaluate each candidate us-ing a composite cost that trades off prediction error against predictionhorizon, and optimize the policy toward the minimum-cost candidate.With a fixed-length action sequence, speed modulation adjusts the ef-fective temporal prediction horizon: simple stages are executed fasterwith a longer prediction horizon, whereas complex stages are executedmore slowly with a shorter prediction horizon. Specifically, we implementspeed modulation in the frequency domain via the discrete cosine trans-form (DCT), which enables smooth, non-integer speed scaling and thuspreserves motion continuity. Extensive evaluations show that AutoSpeedsubstantially reduces task execution time while also improving successrates. Under the AutoSpeed framework, the inferred motion speeds ex-hibit a strong correspondence with task stages.