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.
Adversarial data collection: Human-collaborative perturba- tions for efficient and robust robotic imitation learning,
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A hybrid data collection strategy with a mobile camera arm in dual-arm robots reduces shortcut learning in VLA models and improves spatial generalization to unseen poses and configurations across ACT, Diffusion, and VLA architectures.
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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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The Moving Eye: Enhancing VLA Spatial Generalization via Hybrid Dynamic Data Collection
A hybrid data collection strategy with a mobile camera arm in dual-arm robots reduces shortcut learning in VLA models and improves spatial generalization to unseen poses and configurations across ACT, Diffusion, and VLA architectures.