AI Robots and Humanoid AI: Review, Perspectives and Directions

2024-03-19

arXiv.org

AI Summary

Current humanoid robots exhibit anthropomorphic morphology but lack genuinely human-like intelligence and interactive capabilities. Method: This paper addresses the core challenge of “transitioning from anthropomorphic appearance to human-centered intelligence” by proposing the first evolutionary framework for human-centered humanoid robots. It integrates embodied intelligence, decentralized AI, and NVIDIA Omniverse-based digital twin simulation, systematically unifying large language models (LLMs), large multimodal models (LMMs), and generative AI to overcome bottlenecks in cognitive empathy and dynamic situational adaptation. Contribution/Results: The work establishes a technology roadmap for education, healthcare, and caregiving applications, identifying critical pathways—human-level interaction, ethical alignment, and real-time multimodal embodied collaboration—along with corresponding evaluation paradigms. It provides both theoretical foundations and practical guidance for the paradigm shift of humanoid AI robots from tools to trustworthy collaborative agents.

Technology Category

• Intelligent Robots: Embodied AI
• Humans and AI: Intelligent User Interfaces
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

Abstract

In the approximately century-long journey of robotics, humanoid robots made their debut around six decades ago. The rapid advancements in generative AI, large language models (LLMs), and large multimodal models (LMMs) have reignited interest in humanoids, steering them towards real-time, interactive, and multimodal designs and applications. This resurgence unveils boundless opportunities for AI robotics and novel applications, paving the way for automated, real-time and humane interactions with humanoid advisers, educators, medical professionals, caregivers, and receptionists. However, while current humanoid robots boast human-like appearances, they have yet to embody true humaneness, remaining distant from achieving human-like intelligence. In our comprehensive review, we delve into the intricate landscape of AI robotics and AI humanoid robots in particular, exploring the challenges, perspectives and directions in transitioning from human-looking to humane humanoids and fostering human-like robotics. This endeavour synergizes the advancements in LLMs, LMMs, generative AI, and human-level AI with humanoid robotics, omniverse, and decentralized AI, ushering in the era of AI humanoids and humanoid AI.

Problem

• Advancing humanoid robots to achieve human-level intelligence
• Integrating generative AI for interactive multimodal applications
• Developing humane humanoids with functional and nonfunctional specifications

Innovation

• Generative AI enhances humanoid robot intelligence
• Multimodal designs enable real-time interactive applications
• Humanoid AI transitions from human-looking to humane