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AdaTempo addresses a critical bottleneck in imitation learning by enabling robots to learn variable-speed manipulation tasks directly from slow teleoperated demonstrations. By extracting shared relative-tempo structures, the method embeds optimized speed profiles into policies like ACT or Diffusion Policy, achieving significant performance gains without requiring runtime adjustments. This advancement pushes the boundaries of robot efficiency and adaptability in real-world scenarios where task phases demand different acceleration tolerances.
AdaTempo: Learning Shared Relative Tempo from Demonstrations for Faster Robot Manipulation
arXiv:2610.02706v1 Announce Type: new Abstract: Visuomotor policies trained via imitation learning often inherit the unnecessarily slow timing of teleoperated demonstrations. Yet uniform speedup is unreliable because different phases of a manipulation task tolerate acceleration differently. In this work, we introduce AdaTempo, a self-supervised method that accelerates visuomotor policies by exploiting shared relative-tempo structure in demonstrations. AdaTempo establishes phase correspondence, aggregates the aligned relative tempo into a consensus, and maps it to a continuous speedup profile used to resample demonstrations into accelerated training trajectories. Training standard policies such as ACT or Diffusion Policy on these resampled trajectories directly embeds the desired tempo in the learned behavior, without runtime tempo selection or online retiming. Extensive evaluations show that AdaTempo achieves up to a $3.57\times$ speedup and yields a stronger success--speed trade-off than the original policies and representative acceleration baselines.
Abstract:Visuomotor policies trained via imitation learning often inherit the unnecessarily slow timing of teleoperated demonstrations. Yet uniform speedup is unreliable because different phases of a manipulation task tolerate acceleration differently. In this work, we introduce AdaTempo, a self-supervised method that accelerates visuomotor policies by exploiting shared relative-tempo structure in demonstrations. AdaTempo establishes phase correspondence, aggregates the aligned relative tempo into a consensus, and maps it to a continuous speedup profile used to resample demonstrations into accelerated training trajectories. Training standard policies such as ACT or Diffusion Policy on these resampled trajectories directly embeds the desired tempo in the learned behavior, without runtime tempo selection or online retiming. Extensive evaluations show that AdaTempo achieves up to a $3.57\times$ speedup and yields a stronger success--speed trade-off than the original policies and representative acceleration baselines.
Robotics (cs.RO)
arXiv:2610.02706 [cs.RO]
(or arXiv:2610.02706v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.02706 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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