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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Updated: Sep 23, 2025

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Artificial Intelligence-Based Robotic Technique for Reusable Waste Materials.

Pravin R Kshirsagar1, Neeraj Kumar2, Ahmed H Almulihi3

  • 1Department of Artificial Intelligence, G. H. Raisoni College of Engineering, Nagpur 440016, India.

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|May 16, 2022
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Summary
This summary is machine-generated.

This study introduces a reusable industrial robot arm for automated waste sorting and material recovery. The system efficiently grasps and categorizes recyclables, optimizing resource management and reducing landfill waste.

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Area of Science:

  • Environmental Science
  • Robotics
  • Artificial Intelligence

Background:

  • Effective waste management is crucial for environmental preservation and resource efficiency.
  • Reusable items and recycling reduce raw material dependence and landfill burden.
  • Automated sorting systems can enhance the efficiency of waste recovery processes.

Purpose of the Study:

  • To develop a reusable industrial robot arm for automated waste grasping and sorting.
  • To integrate gripping, motion control, and material categorization into a unified system architecture.
  • To evaluate the performance of the automated waste sorting system.

Main Methods:

  • A reusable industrial robot arm was designed for grasping and sorting waste materials.
  • An artificial intelligence technique, a customized LeNet model, was employed for object material classification (cartons and plastics).
  • The system's movement, gripping, and categorization processes were optimized for efficiency.

Main Results:

  • The developed system demonstrated successful grasping and categorization of waste items.
  • The customized LeNet model achieved effective classification of cartons and plastics.
  • System performance was evaluated based on success rates and computation time.

Conclusions:

  • The automated reusable robot arm system shows significant potential for improving waste management and resource recovery.
  • AI-powered material classification enhances sorting accuracy and efficiency.
  • Further optimization of movement and control systems can maximize the utility of such robotic solutions in waste management.