Improved Autonomy and Adaptability Using GANs and Meta-Learning for Robotic Systems

Abstract

The use of Generative Adversarial Networks and meta-learning provides a new prospect that can revolutionize both the decentralization and adaptability of robotic systems. In this way, the approach leverages GANs to generate different synthetic datasets and meta-learning for quick task transfer, which enables robots to exhibit outstanding generalization capability across different tasks and terrains. In our experiments, robots trained using this hybrid method were only 25% better in task performance and 30% less time was taken for re-training for new tasks compared to an unaided traditional method. These outcomes demonstrate the possibility of using both the GANs and meta-learning to future directions in the creation of fully autonomous and adaptive robotics systems useful for functioning in real environments.