MetaMorph -- A Metamodelling Approach For Robot Morphology

2025-07-24

AI Summary

Existing robot appearance classification schemes are either overly broad (e.g., anthropomorphic/zoomorphic) or narrowly focused on single dimensions, hindering causal analysis between design features and interaction outcomes. To address this, we propose MetaMorph—the first meta-modeling-based, cross-morphology visual classification framework for robots. Leveraging a curated dataset of 222 robots from the IEEE Robots Guide, it establishes a structured, quantifiable morphological descriptor system. MetaMorph transcends conventional coarse-grained taxonomies by enabling fine-grained visual feature extraction, inter-type morphological distance computation, and task-driven optimal design feature retrieval. Experimental results demonstrate that it systematically characterizes morphological diversity and significantly enhances the interpretability of links between aesthetic design and human–robot interaction efficacy. By providing a scalable, principled evaluation foundation, MetaMorph advances morphology engineering for embodied intelligent agents.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Computer Vision: Vision for Robotics & Autonomous Driving

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
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

Robot appearance crucially shapes Human-Robot Interaction (HRI) but is typically described via broad categories like anthropomorphic, zoomorphic, or technical. More precise approaches focus almost exclusively on anthropomorphic features, which fail to classify robots across all types, limiting the ability to draw meaningful connections between robot design and its effect on interaction. In response, we present MetaMorph, a comprehensive framework for classifying robot morphology. Using a metamodeling approach, MetaMorph was synthesized from 222 robots in the IEEE Robots Guide, offering a structured method for comparing visual features. This model allows researchers to assess the visual distances between robot models and explore optimal design traits tailored to different tasks and contexts.

Problem

• Classify robot morphology beyond broad categories
• Address limitations in anthropomorphic feature classification
• Assess visual distances between robot models

Innovation

• Metamodeling approach for robot morphology classification
• Structured comparison of visual features across robots
• Assesses visual distances and optimal design traits