Zero-Shot and Few-Shot Learning Algorithms for Autonomous Robotics in Unstructured Environments

Abstract

Autonomous robots operating in unstructured environments must navigate unpredictable conditions, dynamic obstacles, and novel tasks that cannot be fully anticipated during training. Traditional deep learning models rely heavily on large, labeled datasets and struggle when faced with unseen scenarios, limiting their adaptability and real-world deployment. This paper proposes a comprehensive framework for Zero-Shot and Few-Shot Learning Algorithms tailored for autonomous robotics, enabling robots to generalize from minimal or no prior examples. The framework integrates semantic embedding models, task-conditioned policy networks, meta-learning strategies, and multimodal perception modules to support rapid adaptation in unstructured and continuously evolving environments. Zero-shot learning is achieved through semantic-to-action mapping using knowledge graphs, language models, and attribute-based embeddings, while few-shot learning relies on gradient-based meta-learning, metric learning, and prototype adaptation for new robotic tasks. A unified training pipeline leverages multimodal sensory inputs—vision, LiDAR, proprioception, and natural language instructions—to build robust representations for manipulation, locomotion, and navigation. Experimental evaluations on simulated and real-world robotic platforms demonstrate that the proposed algorithms significantly outperform conventional deep RL and supervised models in task generalization, sample efficiency, and resilience to environmental variability. The results highlight the potential of zero-shot and few-shot learning to accelerate the development of scalable, adaptable, and intelligent autonomous robotic systems capable of reasoning, learning, and performing reliably in complex, unstructured scenarios.