Meta-Domain Adaptive Transfer (MDAT) Learning for Rapid Adaptation in Simulated Robotic Tasks

This paper introduces Meta-Domain Adaptive Transfer (MDAT), a novel approach combining meta-learning, adaptive domain randomization, and feature disentanglement for rapid adaptation in simulated robotic tasks. We evaluate MDAT across three distinct robotic scenarios: object manipulation, navigation, and bipedal locomotion. Our results demonstrate significant improvements in adaptation speed, task performance, and generalization capabilities compared to existing methods such as MAML, Traditional Transfer Learning (TTL), Domain Randomization (DR), and Progressive Neural Networks (PNN).