Memorize, Adapt, Ignore: Diagnosing Robot Learning Mechanisms under Training Data Variation
Uncovering how robotic manipulation policies internalize and adapt to training data variations.
Training data variation, whether through designing a domain randomization (DR) scheme in simulation or curating demonstrations for imitation learning, is a primary lever for improving the robustness of robotic manipulation policies. Yet its underlying mechanisms remain poorly und...
ArXiv CS.RO
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Understanding how robots selectively memorize or generalize from training variations is crucial for creating reliable autonomous systems that perform well in real-world environments. This research provides a diagnostic framework that helps engineers refine training strategies to prevent overfitting and improve cross-domain flexibility.
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