Transfer Learning Strategies for Cross-Domain Deployment of Autonomous Systems: From Simulation to Reality

Abstract

The reality gap between simulated training environments and real-world deployment remains a fundamental obstacle to the practical implementation of learning-based autonomous systems. This paper provides a comprehensive analysis of transfer learning strategies that enable effective cross-domain knowledge transfer from simulation to physical unmanned vehicles across aerial, ground, and marine platforms. We systematically categorize transfer learning approaches into four paradigms: domain randomization, domain adaptation, meta-learning, and sim-to-real transfer through progressive fidelity. The study examines the theoretical foundations of each approach, analyzing how they address distribution shifts in visual appearance, physical dynamics, sensor characteristics, and environmental stochasticity. Through extensive literature synthesis, we evaluate the effectiveness of specific techniques including randomized rendering, physics parameter variation, adversarial domain adaptation, model-agnostic meta-learning (MAML), and reality-gap reward shaping. Particular emphasis is placed on identifying which aspects of autonomous behavior—perception, planning, or control—benefit most from different transfer strategies. We analyze case studies spanning vision-based UAV navigation, autonomous ground vehicle path following, and underwater manipulation, quantifying performance degradation and sample efficiency gains. The paper critically examines the role of simulation fidelity, investigating whether photorealistic rendering and high-fidelity physics are necessary or whether strategic abstraction yields superior transfer. Emerging approaches including digital twins, hardware-in-the-loop integration, and few-shot adaptation techniques are evaluated for their practical applicability. We identify critical success factors including the choice of invariant representations, curriculum design from simple to complex environments, and hybrid approaches combining model-based and model-free learning. The study concludes with actionable guidelines for practitioners and highlights open research challenges in formal guarantees for transferred policies and zero-shot generalization capabilities.