Meta-Learning Approaches for Rapid Adaptation of Autonomous Systems to Novel Environments: A Comparative Study

Abstract

Autonomous unmanned systems must rapidly adapt to novel environments with different terrain characteristics, weather conditions, obstacle distributions, and task requirements, yet traditional learning approaches require extensive retraining for each new scenario. This comparative study comprehensively examines meta-learning approaches—learning to learn—that enable autonomous systems to quickly adapt to new environments with minimal data. We systematically categorize meta-learning paradigms into optimization-based methods (MAML, Reptile) that learn initialization parameters enabling fast gradient-based adaptation, metric-based approaches (Prototypical Networks, Matching Networks) that learn embedding spaces for few-shot classification, and model-based methods that learn explicit adaptation mechanisms through recurrent or memory-augmented architectures. The study develops a unified evaluation framework comparing twelve prominent meta-learning algorithms across representative autonomous system tasks: vision-based navigation adapting to new visual appearances, terrain traversability assessment generalizing to novel surface types, and manipulation learning new object grasping strategies. Performance evaluation encompasses adaptation speed measured by samples required for target performance, final task performance after adaptation, computational efficiency for online learning, and generalization breadth across environment diversity. Comparative results reveal that optimization-based meta-learning achieves superior adaptation with 5-10 gradient steps compared to hundreds required by standard fine-tuning, while metric-based approaches excel in few-shot perception tasks. We investigate the impact of meta-training diversity, demonstrating that exposure to varied environments during meta-training significantly improves adaptation to out-of-distribution scenarios. The paper examines hybrid approaches combining multiple meta-learning paradigms and analyzes integration with sim-to-real transfer for practical deployment.