Foundational Ethics of Non-Humanoid Human-Robot Interaction
Foundational Ethics of Non-Humanoid Human–Robot Interaction 非人形机器人人机交互伦理框架
The Foundational Ethics of Non-Humanoid Human–Robot Interaction provides a theoretical framework for non-humanoid robots such as industrial manipulators, AGVs, drones, and robot swarms operating in shared human spaces. Developed as Version 1.0 from He Yi's New Humanism theory lineage, it addresses three core differences from humanoid scenarios: the principal's absence, motion-based interaction channels, and multi-unit swarm objects. The framework establishes a seven-layer structure containing sub-theories like Bystander Sovereignty and Behavioral Legibility with quantifiable mechanisms designed for inclusion in standards and contracts.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
Researchers, developers, and standards-makers should read this to understand ethical design and governance for non-humanoid and swarm robotics.
What the document contains
The document covers an ethical and theoretical framework tailored for non-humanoid robots, including industrial manipulators, drones, robot swarms, and infrastructure-embedded embodied intelligence. It details mechanisms like burden budgets, three-channel state declarations, perception confirmation, and event-triggered black boxes across a seven-layer structure.
As a theoretical preprint, it lacks formal legal enactment or finalized standardization text, focusing instead on derivation from meta-axioms.
- Non-humanoid robot ethics must account for machines that lack a face but possess physical force and mobility in shared spaces.
- The framework addresses three fundamental differences from humanoid scenarios: absent principals, motion-based interaction channels, and swarm objects.
- It introduces three dedicated sub-theories: Bystander Sovereignty, Behavioral Legibility, and Swarm Accountability.
- The theory's mechanisms—such as burden budgets, three-channel state declarations, and event-triggered black boxes—are designed to be quantifiable and auditable.
- The ultimate goal is to translate ethical principles directly into standards, contracts, reviews, and evidence chains.
· Meta-axiom transformations · Sub-theories (Bystander Sovereignty, Behavioral Legibility, Swarm Accountability) · Three-layer defense system · Domain provisions · Life-cycle governance · Assessment tools · Implementation path
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
4/5Clear responsibility for AI outcomes
Three dedicated sub-theories follow: Bystander Sovereignty, Behavioral Legibility, and Swarm Accountability.
Description
screening score · 2026-10-24
Fairness
0/5Non-discrimination and equitable treatment
screening score · 2026-10-24
Human Dignity
2/5Respect for the inherent worth of persons
Industrial manipulators, AGVs and AMRs, drones, delivery and cleaning robots, robotic animals, exoskeletons, robot swarms, and infrastructure-embedded embodied intelligence do not take the human form as their interaction paradigm, yet they continuously enter shared human space
Description
screening score · 2026-10-24
Human Oversight
4/5Humans remain in control of consequential decisions
event-triggered black boxes, and reserved termination rights—are all quantifiable, auditable, and admissible into standards and contracts
Description
screening score · 2026-10-24
Privacy
0/5Protection of personal data and private life
screening score · 2026-10-24
Transparency
4/5Openness about how AI systems work and are used
core mechanisms—burden budgets, three-channel state declaration, perception confirmation, adversarial task verification
Description
screening score · 2026-10-24
Enforcement
4/5Consequences and compliance mechanisms with real force
Its core mechanisms—burden budgets, three-channel state declaration, perception confirmation, adversarial task verification, three-source attribution, event-triggered black boxes, and reserved termination rights—are all quantifiable, auditable, and admissible into standards and contracts.
Description
screening score · 2026-10-24
Impact Assessment
3/5Required assessment of an AI system's risks/impacts before or during use
The framework unfolds in seven layers: meta-axiom transformations, sub-theories, a three-layer defense system, domain provisions, life-cycle governance, assessment tools, and an implementation path.
Description
screening score · 2026-10-24