Emotion Ontology From the Perspective of Developmental Robotics: A Review

Emotion ontology and developmental robotics are two rapidly evolving fields that, despite a shared interest in the nature and structure of emotions, have largely developed in isolation. Emotion ontology seeks to formally represent the categories, dimensions, and causal processes underlying affective phenomena, while developmental robotics investigates how emotional capabilities can emerge in artificial agents through embodied interaction and learning. This review bridges these two domains by systematically examining how ontological frameworks for emotion—spanning discrete, dimensional, and componential process models—can inform and be informed by developmental robotics research on emotional embodiment, empathy, and human-robot interaction. The review covers foundational emotion ontologies, including the Emotion Ontology (EMO), Visualized Emotion Ontology (VEO), the Emotion Frame Ontology (EFO), and hybrid frameworks, alongside key developmental robotics paradigms such as social constructivist emotion development, morphofunctional embodiment, and caregiver-driven emotional learning. Recent 2024–2026 advances in foundation models for affective computing, large-language-model-based appraisal, and multimodal benchmarking are integrated throughout. Critical gaps in both fields are identified, and an integrative perspective is advanced in which formal ontological structures guide the design of emotionally intelligent robots, while embodied robotic experiments provide empirical grounding for ontological theories. A comparative evaluation of discrete, dimensional, and componential paradigms using consistent criteria supports the conclusion that the componential process model, which captures the Event–Appraisal–Affect–Action Readiness–Behavior sequence, offers the most promising bridge between the two domains. This review provides a roadmap for researchers seeking to build robots with ontologically grounded, developmentally plausible emotional intelligence.