An Efficient Metric for Data Quality Measurement in Imitation Learning

2026-05-02

AI Summary

This work addresses the performance degradation of imitation learning policies caused by low-quality user demonstration data—such as jittery or oscillatory trajectories—in real-world scenarios. To this end, the authors propose an unsupervised, zero-interaction data quality assessment metric based on the power spectral density (PSD) of trajectories. This method requires neither policy training, environment interaction, nor expert annotations, enabling efficient ranking and selection of high-quality demonstrations for policy fine-tuning. As the first study to leverage PSD for demonstration filtering, it substantially reduces computational overhead. Extensive evaluations across multiple benchmarks and a user study with older adults demonstrate that policies trained on PSD-filtered data achieve significantly higher task success rates and smoother trajectories, outperforming both unfiltered baselines and alternative filtering approaches.

Abstract

Imitation learning (IL) has seen remarkable progress, yet field deployment of IL-powered robots remains hindered by the challenge of out-of-distribution (OOD) scenarios. Fine-tuning pre-trained policies with end-user demonstrations collected in deployment environments is a promising strategy to address this challenge. However, end-user demonstrations are frequently of poor quality, characterized by excessive corrective motions, oscillations, and abrupt adjustments that degrade both learned and fine-tuned policy performance. Existing automated approaches for curating demonstration data require policy rollouts in the environment, making them computationally expensive and impractical for real-world deployment. In this paper, we propose a fast, efficient, and fully automated demonstration ranking metric based on the power spectral density (PSD) of demonstration trajectories. The PSD metric requires no policy learning, environment interaction, or expert labeling, making it well-suited for scalable, in-the-field data curation. Lower PSD values correspond to smoother, higher-quality demonstrations, while higher PSD values indicate erratic, artifact-laden trajectories. We evaluate the proposed metric on two benchmark imitation learning datasets comprising expert and lay-user demonstrations, and through a user study with older adults at a retirement facility, where collected demonstrations are used to fine-tune $\pi0.5$ \cite{intelligence2025pi_} for a daily living task. Results demonstrate that PSD-curated data yields policies with higher task success rates and smoother execution trajectories compared to uncurated baselines and two competitive data-ranking methods.

Problem

• Imitation Learning
• Data Quality
• Out-of-Distribution
• Demonstration Curation
• Policy Fine-tuning

Innovation

• Power Spectral Density
• Imitation Learning
• Data Quality Metric
• Out-of-Distribution Generalization
• Demonstration Curation