Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

The Representativeness Heuristic02:13

The Representativeness Heuristic

16.1K
The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
16.1K
Frustration and Conflict: Approach-Approach, Approach-Avoidance01:20

Frustration and Conflict: Approach-Approach, Approach-Avoidance

134
Frustration occurs when people are obstructed or prevented from achieving a desired goal or fulfilling a perceived need. For example, when someone's input is ignored in a discussion, it can lead to feelings of frustration. Conflict, however, arises from opposing interests, goals, or actions. Conflicts can take various forms based on the nature of these opposing desires or goals.
One common type of conflict is the Approach–Approach Conflict. In this case, a person faces two desirable...
134
Concepts and Prototypes01:24

Concepts and Prototypes

211
The human nervous system handles vast amounts of information by translating sensory stimuli into neural impulses, which the brain processes, creating thoughts expressed through language or stored as memories. The brain also synthesizes information from emotions and memories, which significantly influence thoughts and behaviors. This intricate process creates a comprehensive mental picture.
The brain organizes this information using concepts, which are mental categories grouping linguistic data,...
211
Stereotype Content Model02:16

Stereotype Content Model

14.8K
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...
14.8K
Schemas01:42

Schemas

11.9K
A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
11.9K
Information Processing Approach01:30

Information Processing Approach

138
The information-processing theory of cognitive development centers on fundamental mental processes, including attention, memory, and problem-solving skills. Researchers in this field examine how cognitive abilities, such as working memory, evolve and influence children's overall development. Studies indicate that children with stronger working memory tend to excel in reading comprehension, math, and problem-solving compared to peers with less efficient memory skills. Low working memory is...
138

You might also read

Related Articles

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

Sort by
Same author

A Study on the Role of Affective Feedback in Robot-Assisted Learning.

Sensors (Basel, Switzerland)·2023
See all related articles

Related Experiment Video

Updated: Aug 30, 2025

Methods for Presenting Real-world Objects Under Controlled Laboratory Conditions
06:54

Methods for Presenting Real-world Objects Under Controlled Laboratory Conditions

Published on: June 21, 2019

6.0K

An Approach to Task Representation Based on Object Features and Affordances.

Paul Gajewski1, Bipin Indurkhya2

  • 1Institute of Computer Science, AGH University of Science and Technology, 30-059 Krakow, Poland.

Sensors (Basel, Switzerland)
|August 26, 2022
PubMed
Summary

This study introduces a novel knowledge representation scheme for service robots, enabling skill generalization and explainability without prior object knowledge. This approach enhances robot learning and human interaction for reliable task execution.

Keywords:
computer visionexplainabilityrobot perceptionscene understandingtask understanding

More Related Videos

Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace
09:11

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

Published on: August 8, 2019

5.8K
Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings
07:08

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Published on: August 1, 2018

8.4K

Related Experiment Videos

Last Updated: Aug 30, 2025

Methods for Presenting Real-world Objects Under Controlled Laboratory Conditions
06:54

Methods for Presenting Real-world Objects Under Controlled Laboratory Conditions

Published on: June 21, 2019

6.0K
Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace
09:11

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

Published on: August 8, 2019

5.8K
Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings
07:08

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Published on: August 1, 2018

8.4K

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Vision

Background:

  • Service robots require reliable task execution, human learning capabilities, and plan explainability.
  • Existing methods for robot knowledge representation and extraction are often inadequate.

Purpose of the Study:

  • To introduce a knowledge representation scheme for enhanced skill generalization and explainability in multi-purpose service robots.
  • To develop techniques for extracting robot knowledge from raw data without requiring prior object information or 3D models.

Main Methods:

  • Developed a novel knowledge representation scheme for robot scene understanding and task execution.
  • Implemented techniques for extracting this knowledge from raw sensory data.
  • Created a modular system with a computer vision system and a task reasoning module.

Main Results:

  • The proposed knowledge representation facilitates skill generalization and explainability.
  • The system successfully learned from a few demonstrations for tasks like item hanging and stacking.
  • The approach does not require prior object knowledge or 3D models.

Conclusions:

  • The novel knowledge representation scheme enhances robot reliability, learning, and human interaction.
  • The modular architecture allows for easy integration of new recognition and reasoning routines.
  • This research advances the development of more capable and understandable service robots.