## 2 Related Work  
Industrial robot design is transitioning from purely technology-driven performance toward human-robot affective symbiosis. As control systems streamline and intelligence capabilities advance, robotic structures now support greater dexterity and expressive forms [1]. Market and exhibition trends concurrently reveal growing emphasis on humanistic considerations and emotional expression in robot styling, where form serves not merely functional purposes but conveys brand identity and enhances user affinity. Approachable, aesthetically refined designs mitigate user alienation in production environments while reinforcing brand recognition and loyalty. Consequently, a central challenge in contemporary industrial robot design involves aligning form with user perceptual preferences to evoke positive affective experiences.  
Kansei engineering--emerging in late-20 th-century Japan--provides a methodology for translating subjective affective needs into concrete design elements [2]. Initially applied in automotive and consumer electronics, Kansei methods now extend to industrial, service, and companion robots through integration with empirical techniques including semantic differential, factor analysis, and the KJ method [3; 4; 5]. Complementing these approaches, physiological measurements (e.g., eye-tracking, EEG) have strengthened styling research. Studies correlating eye-tracking data with subjective evaluations during robot observation successfully extract critical design elements and provide objective foundations for optimization [6; 7; 8], overcoming limitations of questionnaire-only methods. Recent work integrates Extenics theory--a formal framework for modeling and resolving contradictions--into affective design. [PERSON] et al. [9] combined memetics with Extenics to build matter-element models, while [PERSON] et al. [10] quantified regional cultural symbols using Extenics. Applications in industrial robotics include [PERSON]'s Kansei-Extenics approach for glue-dispensing robots [11] and [PERSON] et al.'s Extenics-based framework for companion robots [12]. These studies demonstrate Extenics' value in improving lexicon extraction accuracy for affective design.  
In morphological solution generation, shape grammar has gained traction as a formal generative design method. First proposed by [PERSON] and [PERSON] (1972) [13; 14], it defines initial shapes and transformational rules (translation, scaling, rotation, etc.) to systematically generate design solutions conforming to stylistic constraints. Its strengths in capturing specific styles and enabling automated variant derivation have enabled applications in architecture, traditional crafts, and industrial design. Within affective design, scholars have encoded emotional imagery-correlated geometric features into shape rules, enabling directed generation from affective semantics [15].  
However, constructing targeted shape grammar rules for complex mechatronic products like industrial robots--based precisely on user perceptual preferences (via Extenics quantification) and visual attention foci (via eye-tracking)--remains underexplored. This gap is particularly acute for cleaning robots, which operate directly among humans yet receive scant attention regarding affective needs despite their impact on user acceptance and brand image. Addressing this gap, our study proposes an integrated methodology for industrial cleaning robot affective styling. Building on Extenics semantic quantification, eye-tracking analysis, and shape grammar generation mechanisms, we establish a complete workflow from user insight to optimized solution generation.