  We consider imitation learning with access only to expert demonstrations,
whose real-world application is often limited by covariate shift due to
compounding errors during execution. We investigate the effectiveness of the
Continuity-based Corrective Labels for Imitation Learning (CCIL) framework in
mitigating this issue for real-world fine manipulation tasks. CCIL generates
corrective labels by learning a locally continuous dynamics model from
demonstrations to guide the agent back toward expert states. Through extensive
experiments on peg insertion and fine grasping, we provide the first empirical
validation that CCIL can significantly improve imitation learning performance
despite discontinuities present in contact-rich manipulation. We find that: (1)
real-world manipulation exhibits sufficient local smoothness to apply CCIL, (2)
generated corrective labels are most beneficial in low-data regimes, and (3)
label filtering based on estimated dynamics model error enables performance
gains. To effectively apply CCIL to robotic domains, we offer a practical
instantiation of the framework and insights into design choices and
hyperparameter selection. Our work demonstrates CCIL's practicality for
alleviating compounding errors in imitation learning on physical robots.
