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

Evolutionary Psychology01:20

Evolutionary Psychology

1.1K
Evolutionary psychology explores the origins of human behavior and mental processes by framing them within the context of natural selection, a theory famously propounded by Charles Darwin. This field asserts that many behaviors common across human societies — ranging from instinctive fear reactions to complex social interactions — arose as evolutionary adaptations. These adaptations enhanced the survival and reproductive success of our ancestors, thereby becoming embedded in the...
1.1K
Criticisms of the Evolutionary Perspective01:23

Criticisms of the Evolutionary Perspective

390
In a study where individuals posing as strangers offered compliments and proposed casual sex to students, the responses differed significantly based on gender. Not a single woman accepted the proposal, while 70% of the men agreed. This outcome provides a useful scenario to explore through the lens of evolutionary psychology and social learning theory, highlighting the diverse perspectives on human sexual behaviors.
Evolutionary psychology provides one explanation for these findings, suggesting...
390
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.7K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.7K
Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

280
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...
280
Introduction to Learning01:18

Introduction to Learning

1.3K
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...
1.3K
Observational Learning01:12

Observational Learning

1.1K
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...
1.1K

You might also read

Related Articles

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

Sort by
Same author

Experimental Assessment of the Effect of Temperature in the Range of 20-80 °C on Structural Behaviour of NSM CFRP Reinforced Concrete Slabs.

Materials (Basel, Switzerland)·2026
Same author

Assessing Inflammatory Bowel Disease Care Quality in Portugal: A Nationwide Gastroenterologist Survey.

GE Portuguese journal of gastroenterology·2026
Same author

Catch-Up Growth in Twins: The Influence of Chorionicity and Zygosity from Birth to School Age.

Twin research and human genetics : the official journal of the International Society for Twin Studies·2026
Same author

WildDrone: autonomous drone technology for monitoring wildlife populations.

Frontiers in robotics and AI·2026
Same author

miR-21-5p dysregulation is associated with gut microbiota dysbiosis and pro-oncogenic markers in primary sclerosing cholangitis with concomitant inflammatory bowel disease.

Experimental and molecular pathology·2025
Same author

The Effect of Neck-Specific Exercise with or Without a Behavioral Approach in Chronic Whiplash-Associated Disorders: A Systematic Review and Meta-Analysis.

Muscles (Basel, Switzerland)·2025

Related Experiment Video

Updated: Feb 25, 2026

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents
06:25

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

Published on: May 16, 2025

1.5K

Evolutionary online behaviour learning and adaptation in real robots.

Fernando Silva1,2,3, Luís Correia2, Anders Lyhne Christensen1,3,4

  • 1Bio-inspired Computation and Intelligent Machines Lab, 1649-026 Lisboa, Portugal.

Royal Society Open Science
|August 10, 2017
PubMed
Summary
This summary is machine-generated.

This study demonstrates evolving neural network controllers on real robots within an hour, enabling autonomous adaptation to tasks and hardware failures. This online evolution approach accelerates robotic learning and resilience in physical systems.

Keywords:
fault tolerancelearningonline evolutionreal robots

More Related Videos

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.9K
Designing and Implementing Nervous System Simulations on LEGO Robots
10:34

Designing and Implementing Nervous System Simulations on LEGO Robots

Published on: May 25, 2013

15.7K

Related Experiment Videos

Last Updated: Feb 25, 2026

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents
06:25

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

Published on: May 16, 2025

1.5K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.9K
Designing and Implementing Nervous System Simulations on LEGO Robots
10:34

Designing and Implementing Nervous System Simulations on LEGO Robots

Published on: May 25, 2013

15.7K

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Evolutionary Computation

Background:

  • Online evolution offers autonomous learning and adaptation for robots.
  • Previous studies were limited to simulation due to long hardware evolution times.
  • Real-world robotic evolution requires efficient and timely methods.

Purpose of the Study:

  • To evolve neural network controllers on real robotic hardware.
  • To investigate the impact of simulation accuracy on real-world performance.
  • To demonstrate the adaptive capabilities of online evolution in physical systems.

Main Methods:

  • Evolving neural network-based controllers directly on physical robots.
  • Utilizing both random and pre-evolved (in simulation) solutions.
  • Testing controllers on single-robot and collective robotics tasks.
  • Introducing hardware faults and task requirement changes to assess adaptation.

Main Results:

  • Capable controllers were evolved on real hardware in under one hour.
  • More accurate simulations correlated with higher-performing controllers.
  • Online evolution on real robots proved meaningful, even with simulation discrepancies.
  • Demonstrated robots overcoming simultaneous motor faults and adapting to new task requirements.

Conclusions:

  • Online evolution is a viable and timely method for developing controllers on real robots.
  • Simulation accuracy influences, but does not solely determine, real-world performance.
  • Real-world online evolution enables significant adaptive capabilities, including fault tolerance and task modification.