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Humanoid Factors: Design Principles for AI Humanoids in Human Worlds

Xinyuan Liu, Eren Sadikoglu, Ransalu Senanayake, Lixiao Huang

TL;DR

The paper introduces Humanoid Factors (HoF), a four-p pillar framework (Physical, Cognitive, Social, Ethical) to design, evaluate, and govern humanoids coexisting with humans in shared environments.It argues that AI foundation models enable humanoids to operate across perception, reasoning, planning, social signaling, memory, and governance, but require carefully staged training (pre-, mid-, post-training) and holistic evaluation beyond task completion.An illustrative experiment shows that a geometric task success does not guarantee cognitively legible, human-like motion, highlighting the need for joint human-humanoid evaluation across HoF pillars.Overall, HoF advocates integrated design, evaluation, and governance, urging policymakers, researchers, and industry to adopt shared primitives and standards for safe, trustworthy, and socially acceptable humanoid deployment.

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.

Humanoid Factors: Design Principles for AI Humanoids in Human Worlds

TL;DR

The paper introduces Humanoid Factors (HoF), a four-p pillar framework (Physical, Cognitive, Social, Ethical) to design, evaluate, and govern humanoids coexisting with humans in shared environments.It argues that AI foundation models enable humanoids to operate across perception, reasoning, planning, social signaling, memory, and governance, but require carefully staged training (pre-, mid-, post-training) and holistic evaluation beyond task completion.An illustrative experiment shows that a geometric task success does not guarantee cognitively legible, human-like motion, highlighting the need for joint human-humanoid evaluation across HoF pillars.Overall, HoF advocates integrated design, evaluation, and governance, urging policymakers, researchers, and industry to adopt shared primitives and standards for safe, trustworthy, and socially acceptable humanoid deployment.

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.
Paper Structure (140 sections, 8 equations, 9 figures, 5 tables)

This paper contains 140 sections, 8 equations, 9 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Humanoid Factors Framework
  3. Physical Pillar (P)
  4. Layer P1: Morphology (What the humanoid is)
  5. Morphology variants.
  6. Layer P2: Sensing (What the humanoid perceives)
  7. Egocentric vision and active gaze control.
  8. Somatic and auditory sensing.
  9. Sensory parallels and extensions.
  10. Layer P3: Action (How the humanoid acts and communicates)
  11. Actuation.
  12. Interaction surfaces and expressivity.
  13. Layer P4: Operation (How the humanoid sustains operation)
  14. The energy–compute–thermal trilemma.
  15. Robustness and fall resilience.
  16. ...and 125 more sections

Figures (9)

  • Figure 1: The proposed Humanoid Factors framework for humanoid design.
  • Figure 2: The Four Pillars of Humanoid Factors.
  • Figure 3: Visual examples of the four morphological embodiment classes: M-Static (Upper-body), M-Wheel (Mobile Manipulator), M-Biped (Locomotion Biped), and M-Full (Full Humanoid).
  • Figure 4: HoF readiness level of generative models
  • Figure 5: The Data Pyramid for humanoid foundation models. Our visualization is inspired by the framework proposed in zhu2024datapyramid.
  • ...and 4 more figures