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

Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

113
Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
113
Introduction to Learning01:18

Introduction to Learning

699
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
699
Steps in the Modeling Process01:14

Steps in the Modeling Process

475
Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
Attention is the first necessary component for observational learning. It involves focusing on what the model is doing and saying. For example, if you decide to take a drawing class to enhance your skills, you need to pay close attention to the instructor's words and hand movements. The characteristics of the model significantly...
475
Stereotype Content Model02:16

Stereotype Content Model

15.1K
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...
15.1K
Observational Learning01:12

Observational Learning

625
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
625
Associative Learning01:27

Associative Learning

865
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
865

You might also read

Related Articles

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

Sort by
Same authorSame journal

Editorial: Robotic applications for a sustainable future.

Frontiers in robotics and AI·2026
Same author

Increased choroidal thickness in a patient with acquired hyperopia and choroidal folds syndrome.

American journal of ophthalmology case reports·2023
Same author

VIP-2 -High-Sensitivity Tests on the Pauli Exclusion Principle for Electrons.

Entropy (Basel, Switzerland)·2020
Same author

On the Importance of Electron Diffusion in a Bulk-Matter Test of the Pauli Exclusion Principle.

Entropy (Basel, Switzerland)·2020
Same author

Dynamic Camera Reconfiguration with Reinforcement Learning and Stochastic Methods for Crowd Surveillance.

Sensors (Basel, Switzerland)·2020
Same author

Transgranular Cracking in a Liquid Zn Embrittled High Strength Steel.

Scripta materialia·2020

Related Experiment Video

Updated: Nov 19, 2025

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

12.0K

Toward an Interactive Reinforcement Based Learning Framework for Human Robot Collaborative Assembly Processes.

Sharath Chandra Akkaladevi1,2, Matthias Plasch1, Sriniwas Maddukuri1

  • 1Profactor GmbH, Steyr-Gleink, Steyr, Austria.

Frontiers in Robotics and AI
|January 27, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces Interactive Reinforcement Learning to reduce programming effort in human-robot collaboration for manufacturing assembly. The system intuitively learns assembly tasks through user guidance, enabling efficient mass customization.

Keywords:
cognitionhuman robot collaborationinteractive reinforcement learningknowledge modelingreasoning

More Related Videos

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
11:01

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

Published on: November 24, 2015

13.5K
Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy
13:44

Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy

Published on: August 8, 2011

14.3K

Related Experiment Videos

Last Updated: Nov 19, 2025

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

12.0K
SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
11:01

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

Published on: November 24, 2015

13.5K
Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy
13:44

Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy

Published on: August 8, 2011

14.3K

Area of Science:

  • Robotics
  • Human-Robot Interaction
  • Artificial Intelligence

Background:

  • Manufacturing is shifting towards mass customization, increasing the need for flexible human-robot collaboration.
  • Reducing programming effort for expert users in industrial human-robot interaction remains a significant challenge.
  • Natural modes of communication are crucial for intuitive robot programming in evolving manufacturing environments.

Purpose of the Study:

  • To propose and evaluate an Interactive Reinforcement Learning approach for learning collaborative assembly processes.
  • To reduce the programming effort required by expert users through natural interaction modes.
  • To develop a framework enabling robots to learn complex assembly tasks intuitively.

Main Methods:

  • Modeling simple tasks using task-based formalism.
  • Utilizing a GUI for user interaction, where users select actions to guide the robot.
  • Incorporating user/robot capabilities and object affordances to refine action proposals.
  • Employing a goal-based hierarchy and action prerequisites to further reduce action choices.

Main Results:

  • The framework successfully learns a complete collaborative assembly process.
  • The learning approach is demonstrated to be intuitive for users.
  • The system effectively reduces the number of proposed actions by considering various constraints.
  • The framework supports different users in teaching diverse assembly processes.

Conclusions:

  • Interactive Reinforcement Learning offers an effective solution for intuitive human-robot collaborative assembly.
  • The proposed method significantly lowers the programming burden in industrial settings.
  • The system's ability to learn from user interaction facilitates adaptable and customized manufacturing processes.