CATALYST: Cognitive-To-Autonomy-Inspired Two-Stage Training Data Generation with Local-System-Aware Selection Technique
In conventional learning-based robotic dynamics modeling, physical information is mostly incorporated into the model or loss function, while the design of training data often relies on random sampling or uniform coverage, which can limit performance. To address this gap, this paper proposes the CATA…