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Related Experiment Video

Updated: May 12, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

A causal AI and explainable optimization framework for social robot design.

Zongming Liu1, Xinan Liang1, Fengqi Yang2

  • 1School of Art and Design, Shaanxi University of Science and Technology, Xian 710021, China.

Iscience
|May 11, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an integrated framework for smart product design, using semiotic architecture product design (SAPAD) and dual machine learning (DML) to map human-machine requirements. The approach optimizes design parameters for enhanced user experience in products like companion robots.

Keywords:
Applied sciencesEngineeringNetwork

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Last Updated: May 12, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Area of Science:

  • Human-Computer Interaction
  • Product Design
  • Artificial Intelligence

Background:

  • Mapping human-machine requirements in smart product design is complex.
  • Existing methods lack a unified approach for integrating user behavior, demand analysis, and design optimization.
  • Smart products, like home companion robots, require sophisticated design frameworks to meet user needs effectively.

Purpose of the Study:

  • To propose an integrated framework for mapping human-machine requirements in smart product design.
  • To enhance user experience in smart products by systematically analyzing and prioritizing user demands.
  • To provide a transparent decision-support process for complex human-machine system design.

Main Methods:

  • Utilized Semiotic Architecture Product Design (SAPAD) for deconstructing user behavior and generating demand hypotheses.
  • Employed Dual Machine Learning (DML) for causal inference to identify core demands impacting user experience.
  • Integrated Hesitant Fuzzy Quality Function Deployment (HFQFD) to manage evaluation uncertainty and convert demands into design parameters.
  • Developed a hybrid multi-objective optimization approach using Cuckoo Catfish Optimizer (CCO), Least Squares Support Vector Machine (LSSVM), and Non-Dominated Sorting Genetic Algorithm II (NSGA-II).
  • Applied Shapley Additive exPlanations (SHAP) for quantifying parameter contributions to conflicting requirements.

Main Results:

  • The framework successfully deconstructs user behavior and identifies critical demands influencing user experience.
  • Validated demands were effectively translated into optimized design parameters using the hybrid optimization approach.
  • Shapley Additive exPlanations (SHAP) provided clear insights into the marginal contribution of each parameter.
  • The integrated approach demonstrated effectiveness in a case study involving home companion robots.

Conclusions:

  • The proposed integrated framework offers a robust solution for the challenges in mapping human-machine requirements for smart products.
  • This methodology facilitates transparent decision-making by linking user behavior analysis to design parameter optimization.
  • The study provides a valuable tool for designers and engineers working on complex human-machine systems, particularly in the domain of intelligent robotics.