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

Nonconscious Mimicry01:13

Nonconscious Mimicry

4.9K
Nonconscious mimicry occurs when individuals alter their mannerisms to match the behaviors and expressions of those nearby, without intention.
4.9K
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
Modeling and Similitude01:12

Modeling and Similitude

451
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
451
Purposive Learning01:22

Purposive Learning

297
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
297
Elaborative Rehearsals01:07

Elaborative Rehearsals

193
Elaborative rehearsal is a crucial cognitive strategy that strengthens information encoding in long-term memory by making meaningful connections between new data and pre-existing knowledge. This approach contrasts with maintenance rehearsal, which involves simple repetition without delving into the significance of the information. While maintenance rehearsal might temporarily keep information active in short-term memory, it is less effective for long-term retention.
The effectiveness of...
193
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

You might also read

Related Articles

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

Sort by
Same author

Transfer learning in robotics: From promises to practice through the emerging role of foundation models.

Science robotics·2026
Same author

Cross-robot behavior adaptation through intention alignment.

Science robotics·2026
Same author

Challenging Deep Learning Methods for EEG Signal Denoising under Data Corruption.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

MoleQCage: Geometric High-Throughput Screening for Molecular Caging Prediction.

Journal of chemical information and modeling·2024
Same author

Shaping high-performance wearable robots for human motor and sensory reconstruction and enhancement.

Nature communications·2024
Same author

Editorial: Sensorimotor Foundations of Social Cognition.

Frontiers in human neuroscience·2022

Related Experiment Video

Updated: Nov 19, 2025

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

11.2K

Imitating by Generating: Deep Generative Models for Imitation of Interactive Tasks.

Judith Bütepage1, Ali Ghadirzadeh1,2, Özge Öztimur Karadaǧ1,3

  • 1Robotics, Perception and Learning, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden.

Frontiers in Robotics and AI
|January 27, 2021
PubMed
Summary

Robots can learn to imitate human social behaviors through a novel deep learning framework. This system uses imitation learning and a probabilistic model for better prediction and adaptation in human-robot interactions.

Keywords:
deep learninggenerative modelshuman-robot interactionimitation learningsensorimotor coordinationvariational autoencoders

More Related Videos

Automated Interactive Video Playback for Studies of Animal Communication
07:21

Automated Interactive Video Playback for Studies of Animal Communication

Published on: February 9, 2011

13.9K
Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm
06:35

Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm

Published on: April 28, 2016

34.8K

Related Experiment Videos

Last Updated: Nov 19, 2025

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

11.2K
Automated Interactive Video Playback for Studies of Animal Communication
07:21

Automated Interactive Video Playback for Studies of Animal Communication

Published on: February 9, 2011

13.9K
Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm
06:35

Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm

Published on: April 28, 2016

34.8K

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Human-Robot Interaction

Background:

  • Coordinated actions require continuous sensorimotor signal exchange.
  • Humans learn interactive skills through imitation and active engagement.
  • Predicting and adapting to interaction partners is crucial for seamless collaboration.

Purpose of the Study:

  • To develop a deep learning framework for robots to learn interactive tasks from human demonstrations.
  • To enable robots to learn through both observational and kinesthetic methods.
  • To enhance human-robot interaction by teaching robots social task imitation.

Main Methods:

  • A deep learning framework with components for motion embedding, human motion prediction, and robot trajectory generation.
  • A novel probabilistic latent variable model for motion prediction in latent space, avoiding regression to the mean.
  • Collection of human-human and human-robot interaction data for four social tasks: hand-shake, hand-wave, parachute fist-bump, and rocket fist-bump.

Main Results:

  • Experimental validation of the proposed framework on interactive social tasks.
  • Demonstration of the effectiveness of predictive and adaptive components in imitation learning.
  • Successful imitation of human behavior in human-robot interaction settings.

Conclusions:

  • The proposed deep learning framework enables robots to effectively imitate human social behaviors.
  • Predictive and adaptive capabilities are essential for robots to learn complex interactive tasks.
  • The novel probabilistic latent variable model improves motion prediction accuracy in human-robot interaction.