Humanoid Factors: Design Principles for AI Humanoids in Human Worlds

arXiv cs.RO / 4/17/2026

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Key Points

The paper argues that as AI-powered humanoid robots enter human spaces, design must account for both human factors and new “humanoid factors” to ensure safe, usable coexistence.
It introduces a framework with four pillars—physical, cognitive, social, and ethical—to guide the development of humanoids intended to collaborate with people.
The authors analyze how general-purpose, AI foundation model-driven humanoids differ from human capabilities, and how those differences affect usability, trust, and safety.
They demonstrate the framework by applying it to a real-world humanoid control algorithm, showing that traditional robotics success metrics can miss crucial human cognition and interaction needs.
The work positions humanoid factors as a foundation for designing, evaluating, and governing long-term human–humanoid coexistence.

Abstract

Human factors research has long focused on optimizing environments, tools, and systems to account for human performance. Yet, as humanoid robots begin to share our workplaces, homes, and public spaces, the design challenge expands. We must now consider not only factors for humans but also factors for humanoids, since both will coexist and interact within the same environments. Unlike conventional machines, humanoids introduce expectations of human-like behavior, communication, and social presence, which reshape usability, trust, and safety considerations. In this article, we introduce the concept of humanoid factors as a framework structured around four pillars - physical, cognitive, social, and ethical - that shape the development of humanoids to help them effectively coexist and collaborate with humans. This framework characterizes the overlap and divergence between human capabilities and those of general-purpose humanoids powered by AI foundation models. To demonstrate our framework's practical utility, we then apply the framework to evaluate a real-world humanoid control algorithm, illustrating how conventional task completion metrics in robotics overlook key human cognitive and interaction principles. We thus position humanoid factors as a foundational framework for designing, evaluating, and governing sustained human-humanoid coexistence.