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
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Many robot demonstration datasets contain heterogeneous demonstrations of varying quality. This heterogeneity may benefit policy pre-training, but can hinder robot performance when used with a final imitation learning objective. In particular, some strategies in the data may be less reliable than others or may be underrepresented in the data, leading to poor performance when such strategies are sampled at test time. Moreover, such unreliable or underrepresented strategies can be difficult even for people to discern, and sifting through demonstration datasets is time-consuming and costly. On the other hand, policy performance when trained on such demonstrations can reflect the reliability of different strategies. We thus propose for robots to self-curate based on online robot experience (Demo-SCORE). More specifically, we train and cross-validate a classifier to discern successful policy roll-outs from unsuccessful ones and use the classifier to filter heterogeneous demonstration datasets. Our experiments in simulation and the real world show that Demo-SCORE can effectively identify suboptimal demonstrations without manual curation. Notably, Demo-SCORE achieves over 15-35% higher absolute success rate in the resulting policy compared to the base policy trained with all original demonstrations.
fields
cs.RO 5years
2026 5representative citing papers
Selecting 50% of robot demonstrations by maximizing exposure to reusable primitive-transition patterns outperforms full-data training while halving training steps.
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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SIEVE: Structure-Aware Data Selection for Imitation Learning with VLA Models
Selecting 50% of robot demonstrations by maximizing exposure to reusable primitive-transition patterns outperforms full-data training while halving training steps.
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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.