Geometric diversity of demonstration trajectories exhibits an inverted-U effect on imitation learning success, with the peak shifting lower as mastery increases via more data, easier tasks, or stronger priors.
Curating demon- strations using online experience
4 Pith papers cite this work. Polarity classification is still indexing.
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Power spectral density of trajectories ranks demonstration quality for imitation learning, enabling rollout-free curation that improves fine-tuned policy success.
TSD applies two physics metrics to identify salient trajectory segments for dataset compression and expansion in robotic imitation learning, yielding comparable performance with 25% less data on average.
SPARC generates reliable spatial annotations for robot demonstrations by leveraging spatio-temporal task structure, outperforming detection baselines on localization accuracy while retaining more samples and enabling competitive model performance without manual annotations.
citing papers explorer
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Geometric Entropy: When Trajectory Diversity Helps and Hurts in Imitation Learning
Geometric diversity of demonstration trajectories exhibits an inverted-U effect on imitation learning success, with the peak shifting lower as mastery increases via more data, easier tasks, or stronger priors.
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An Efficient Metric for Data Quality Measurement in Imitation Learning
Power spectral density of trajectories ranks demonstration quality for imitation learning, enabling rollout-free curation that improves fine-tuned policy success.
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TSD: A Physics-Inspired Trajectory Saliency Detector for Efficient Imitation Learning
TSD applies two physics metrics to identify salient trajectory segments for dataset compression and expansion in robotic imitation learning, yielding comparable performance with 25% less data on average.
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SPARC: Reliable Spatial Annotations from Robot Demonstrations at Scale
SPARC generates reliable spatial annotations for robot demonstrations by leveraging spatio-temporal task structure, outperforming detection baselines on localization accuracy while retaining more samples and enabling competitive model performance without manual annotations.