We propose SAIL (Speed-Adaptive Imitation Learning), a framework for enabling faster-than- demonstration execution of policies by addressing key technical challenges in robot dynamics and state-action distribution shifts. SAIL achieves up to a 4× speedup over demonstration speed in simulation and up to 3.2× speedup on physical robot.
MimicLabs: What Matters in Learning from Large-Scale Datasets for Robot Manipulation Vaibhav Saxena*,
Matthew Bronars*,
Nadun Ranawaka*,
Kuancheng Wang,
Woo Chul Shin,
Soroush Nasiriany,
Ajay Mandlekar†,
Danfei Xu† ICLR, 2025
We develop a data generation framework to procedurally emulate key sources of diversity in robot datasets. Using this framework, we generate large-scale datasets with controlled variations to analyze how collection diversity and retrieval strategies impact downstream policy learning.