Denoising-based Contractive Imitation Learning
This addresses a fundamental challenge in imitation learning for robotics or autonomous systems, but it is incremental as it builds on existing frameworks without requiring new expert data.
The paper tackled the covariate shift problem in imitation learning by proposing a denoising-based method (DeCIL) that enhances contraction properties of state transitions, resulting in improved success rates under noise perturbation.
A fundamental challenge in imitation learning is the \emph{covariate shift} problem. Existing methods to mitigate covariate shift often require additional expert interactions, access to environment dynamics, or complex adversarial training, which may not be practical in real-world applications. In this paper, we propose a simple yet effective method (DeCIL) to mitigate covariate shift by incorporating a denoising mechanism that enhances the contraction properties of the state transition mapping. Our approach involves training two neural networks: a dynamics model ( f ) that predicts the next state from the current state, and a joint state-action denoising policy network ( d ) that refines this state prediction via denoising and outputs the corresponding action. We provide theoretical analysis showing that the denoising network acts as a local contraction mapping, reducing the error propagation of the state transition and improving stability. Our method is straightforward to implement and can be easily integrated with existing imitation learning frameworks without requiring additional expert data or complex modifications to the training procedure. Empirical results demonstrate that our approach effectively improves success rate of various imitation learning tasks under noise perturbation.