ManipulationIntermediate
AdaTempo: Learning Shared Relative Tempo from Demonstrations for Faster Robot Manipulation
This preprint presents AdaTempo, a self-supervised method that speeds up visuomotor imitation policies by extracting shared phase-relative tempo patterns from demonstrations and resampling trajectories before training. It achieves up to a 3.57x speedup over demonstrations, and in real-robot tests raises aggregate success from 58.3% with ACT to 77.8% while completing tasks 2.02x faster. The approach matters because it embeds faster execution directly into standard policies such as ACT or Diffusion Policy without runtime retiming or extra speed-selection modules.