CCIL: Continuity-based Data Augmentation for Corrective Imitation Learning
CoRR(2023)
摘要
We present a new technique to enhance the robustness of imitation learning
methods by generating corrective data to account for compounding errors and
disturbances. While existing methods rely on interactive expert labeling,
additional offline datasets, or domain-specific invariances, our approach
requires minimal additional assumptions beyond access to expert data. The key
insight is to leverage local continuity in the environment dynamics to generate
corrective labels. Our method first constructs a dynamics model from the expert
demonstration, encouraging local Lipschitz continuity in the learned model. In
locally continuous regions, this model allows us to generate corrective labels
within the neighborhood of the demonstrations but beyond the actual set of
states and actions in the dataset. Training on this augmented data enhances the
agent's ability to recover from perturbations and deal with compounding errors.
We demonstrate the effectiveness of our generated labels through experiments in
a variety of robotics domains in simulation that have distinct forms of
continuity and discontinuity, including classic control problems, drone flying,
navigation with high-dimensional sensor observations, legged locomotion, and
tabletop manipulation.
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关键词
corrective imitation,data augmentation,learning,continuity-based
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