arXiv:2606.09758v1 Announce Type: cross Abstract: Parametric imitation learning via behavior cloning can suffer from poor generalization to out-of-distribution states due to compounding errors during deployment. We show that reusing the training data during inference via a semi-parametric retrieval-based imitation learning approach can alleviate this challenge. We present Difference-Aware Retrieval Policies for Imitation Learning (DARP), a semi-parametric retrieval-based imitation learning approach that addresses this limitation by reparameterizing the imitation learning problem in terms of lo
The continuous drive to improve AI model robustness and generalization, particularly in practical applications like robotics, motivates this research to overcome existing limitations.
Improving imitation learning's generalization to novel situations is critical for deploying AI in dynamic real-world environments, reducing the need for extensive handcrafted policies or retraining.
This research introduces a novel approach that enhances the reliability and adaptability of imitated behaviors, potentially leading to more robust autonomous systems.
- · Robotics developers
- · AI-driven automation companies
- · Reinforcement learning researchers
AI models, particularly in robotics, gain improved generalization capabilities from training data.
The cost and complexity of deploying AI systems in varied environments could decrease as models become more robust to out-of-distribution states.
This could accelerate the adoption of autonomous systems in sectors requiring high reliability and adaptability, potentially impacting various industries.
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Read at arXiv cs.LG