Search research articles

Related Concept Videos

Stereotype Content Model

Stereotype Content Model

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Did Down-Regulated Instincts Enable Human Gene-Culture Coevolution?

Evolutionary anthropology·2025

Same author

Artificial intelligence algorithm for real-time diagnostic assist in orofacial pain.

Journal of the American Dental Association (1939)·2025

Same author

A leadless pericardial pacemaker.

Heart rhythm·2025

Same author

The Utility of Calibrating Wearable Sensors before Quantifying Infant Leg Movements.

Sensors (Basel, Switzerland)·2024

Same author

Express Visuomotor Responses Reflect Knowledge of Both Target Locations and Contextual Rules during Reaches of Different Amplitudes.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2023

Same author

A hierarchical sensorimotor control framework for human-in-the-loop robotic hands.

Science robotics·2023

Same journal

Self-moving multi-sensor AI-based robotic technology for road crack inspection.

Frontiers in robotics and AI·2026

Same journal

Editorial: Intelligent micro- and nanorobotic systems for enhanced medical procedures.

Frontiers in robotics and AI·2026

Same journal

Editorial: Robotic applications for a sustainable future.

Frontiers in robotics and AI·2026

Same journal

Passive wheels on legged robots: a survey.

Frontiers in robotics and AI·2026

Same journal

Politeness cannot make up for robots' errors.

Frontiers in robotics and AI·2026

Same journal

Workers expect basic social skills but limited autonomy from future robots - a qualitative interview study and taxonomy for robot social skills.

Frontiers in robotics and AI·2026

See all related articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Sep 4, 2025

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

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

Published on: October 14, 2017

Developing Intelligent Robots that Grasp Affordance.

Gerald E Loeb¹

¹Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States.

Frontiers in Robotics and AI

|July 22, 2022

Summary

This summary is machine-generated.

This review explores artificial intelligence for robots to learn object classification through haptic exploration, mimicking infant development for enhanced capabilities in unstructured environments.

Keywords:

dexterity exploration haptics learning manipulation reflexes

More Related Videos

Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace

Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace

Published on: August 8, 2019

Author Spotlight: Enhancing Grasping Abilities for Hemiplegic Patients with Flexible Robotic Limbs

Author Spotlight: Enhancing Grasping Abilities for Hemiplegic Patients with Flexible Robotic Limbs

Published on: October 27, 2023

Related Experiment Videos

Last Updated: Sep 4, 2025

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

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

Published on: October 14, 2017

Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace

Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace

Published on: August 8, 2019

Author Spotlight: Enhancing Grasping Abilities for Hemiplegic Patients with Flexible Robotic Limbs

Author Spotlight: Enhancing Grasping Abilities for Hemiplegic Patients with Flexible Robotic Limbs

Published on: October 27, 2023

Area of Science:

Robotics and Artificial Intelligence
Cognitive Science
Haptic Perception

Background:

Robots and humans require object classification via haptic exploration for tasks in unstructured environments.
Current robotic strategies for acquiring these capabilities differ significantly from human development.

Purpose of the Study:

To review nascent technologies enabling convergence between human and robotic haptic object classification.
To explore bio-inspired hierarchical control schemes for robotic manipulation and learning.

Main Methods:

Review of novel artificial intelligence for sensory-based object classification during active exploration.
Discussion of bio-inspired hierarchical control schemes, analogous to the spinal cord's self-organization.
Exploration of in silico development for robotic learning, simulating fetal and infant stages.

Main Results:

Novel AI classifies objects based on sensory percepts and optimizes exploratory actions.
Object representation through collective manipulation experience aids in discovering causality and affordances.
Bio-inspired hierarchical control offers smoother transitions for exploratory, executive, and reflexive movements.

Conclusions:

Robots need extended active exploration and manipulation, akin to infant development, to generalize knowledge beyond explicit training.
Implementing bio-inspired, self-organizing control middleware can adapt to robotic musculoskeletal mechanics.
In silico development and cloning into physical robots offer a strategy to transcend human performance in robotic learning and manipulation.