Generalist Robotics and Foundation Models for Embodied Intelligence: An Emerging Paradigm for Universal Robot Learning and Autonomous Task Execution

Abstract

Generalist robotics, powered by foundation models for robots, is rapidly transforming autonomous systems by enabling robots to handle diverse tasks, environments, and embodiments using unified learning architectures. This paper presents a comprehensive overview of the evolution, principles, methodologies, challenges, and future scope of general-purpose robotic intelligence. Unlike traditional robotics pipelines, which rely on task-specific controllers and domain-dependent features, generalist robotics integrates multimodal learning, large-scale data-driven modeling, and cross-embodiment generalization. Foundation models—trained on immense datasets of images, video, language, demonstrations, and proprioception—serve as a universal backbone enabling robots to perceive, reason, predict, and act with enhanced adaptability. This paper reviews literature on visual-language-action models, policy learning, robot-transformer architectures, and real-world deployment paradigms. It also highlights key limitations involving data scarcity, real-world variability, safety, interpretability, and hardware constraints. Finally, it discusses future trends shaping the next generation of embodied AI such as cloud-robotics integration, self-supervised lifelong learning, and human-robot collaborative general intelligence.

KEYWORDS: Generalist Robotics; Foundation Models; Embodied Intelligence; Multimodal Learning; Robot Transformers; Vision-Language Action Models; Universal Policies; Robotic Autonomy.