DARP: Semi-parametric retrieval-based imitation learning reduces compounding errors by 15-46%
Researchers introduce DARP (Difference-Aware Retrieval Policies), a semi-parametric imitation learning method that retrieves k-nearest neighbor demonstrations at inference time and predicts actions based on relative distance vectors between neighbor and query states. The approach reparameterizes behavior cloning around local neighborhood structure rather than global state-to-action mappings, requiring no additional data collection or online expert feedback. Across continuous control and robotic manipulation tasks, DARP shows 15-46% performance improvements over standard behavior cloning, including on high-dimensional visual inputs.
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