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

Operant Conditioning Intervention01:24

Operant Conditioning Intervention

58
Operant conditioning serves as a foundational principle in therapeutic interventions aimed at modifying maladaptive behaviors. Central to this approach is the notion that behaviors, both adaptive and maladaptive, are learned through reinforcement. By analyzing the environmental factors that reinforce problematic behaviors, clinicians can design interventions to weaken these reinforcements and replace maladaptive behaviors with healthier alternatives.
In operant conditioning, behaviors that are...
58
Reinforcement01:23

Reinforcement

212
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
212
Behaviorism01:28

Behaviorism

2.3K
The field of behaviorism was pioneered by figures such as Ivan Pavlov, John B. Watson, and B.F. Skinner fundamentally shifted the focus of psychology to the observable and controllable aspects of human and animal behavior. This shift marked a critical evolution in the discipline, emphasizing scientific rigor and experimental methodology.
The core premise of behaviorism is its focus on observable behavior rather than internal thoughts or feelings. This approach argues that true scientific...
2.3K
Reinforcement Schedules01:24

Reinforcement Schedules

149
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
149
Observational Learning01:12

Observational Learning

182
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...
182
Behavior Modification01:21

Behavior Modification

175
Behavioral approaches have often been criticized for ignoring mental processes and focusing solely on observable behavior. However, these approaches provide an optimistic perspective for individuals seeking to change their behaviors. Rather than concentrating on intrinsic personality traits, behavioral approaches suggest that even longstanding habits can be modified by changing the reward contingencies that maintain them.
A real-world application of operant conditioning principles is applied...
175

You might also read

Related Articles

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

Sort by
Same author

Reinforcement learning in linear embedding space unlocks generalizable control across soft robot configurations.

Nature communications·2026
Same author

The earlier you know, the smoother you act: anticipatory control in solo and dyadic juggling.

Experimental brain research·2026
Same author

Exploration Strategies and Feature Prioritisation in Contour-based Haptic Perception of 2D Shape.

IEEE transactions on haptics·2026
Same author

Steering semi-flexible molecular diffusion model for structure-based drug design with reinforcement learning.

Science advances·2026
Same author

CDIR: LoRA-Inspired Attention for Efficient Composite Degradation Image Restoration.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Design of an automated cell batch microinjection system based on magnetic tweezers for zebrafish embryos.

Microsystems & nanoengineering·2026
Same journal

DSPE-ViT: a lightweight vision transformer with dynamic sparse positional encoding for dense small object detection in UAV imagery.

Frontiers in neurorobotics·2026
Same journal

ST-HONet: Spatio-Temporal Hierarchical Network for long-horizon bimanual visuomotor imitation.

Frontiers in neurorobotics·2026
Same journal

ST-HADP: Spatio-Temporal hierarchical attention diffusion policy for long-horizon generalizable bimanual visuomotor imitation.

Frontiers in neurorobotics·2026
Same journal

EQISP: efficient quantized image signal processing with multi-scale pyramid fusion for resource constrained embodied perception.

Frontiers in neurorobotics·2026
Same journal

Research on embodied agent multimodal perception and real-time path planning algorithms for complex unstructured environments.

Frontiers in neurorobotics·2026
Same journal

NL-YOLOv5: a model with a larger receptive field and the ability to globally acquire features.

Frontiers in neurorobotics·2026
See all related articles

Related Experiment Video

Updated: Jul 9, 2025

Investigating Motor Skill Learning Processes with a Robotic Manipulandum
07:52

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

8.8K

A human-centered safe robot reinforcement learning framework with interactive behaviors.

Shangding Gu1, Alap Kshirsagar2, Yali Du3

  • 1Department of Computer Science, Technical University of Munich, Munich, Germany.

Frontiers in Neurorobotics
|November 29, 2023
PubMed
Summary
This summary is machine-generated.

This paper introduces a human-centered framework for Safe Robot Reinforcement Learning (SRRL) to ensure robot safety in real-world applications. It proposes leveraging interactive behaviors for safer human-robot coexistence.

Keywords:
bi-direction informationinteractive behaviorssafe collaborationsafe explorationvalue alignment

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

13.2K
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

13.9K

Related Experiment Videos

Last Updated: Jul 9, 2025

Investigating Motor Skill Learning Processes with a Robotic Manipulandum
07:52

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

8.8K
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

13.2K
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

13.9K

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Human-Robot Interaction

Background:

  • Real-world deployment of Reinforcement Learning (RL) in robotics necessitates robust safety measures.
  • Safe Robot Reinforcement Learning (SRRL) is essential for enabling seamless human-robot coexistence.
  • Current SRRL approaches require enhancement to address the complexities of human-robot interaction.

Purpose of the Study:

  • To propose a human-centered SRRL framework.
  • To identify research gaps in safe exploration, safety value alignment, and safe collaboration.
  • To highlight the potential of interactive behaviors in advancing SRRL.

Main Methods:

  • Envisioning a three-stage SRRL framework: safe exploration, safety value alignment, and safe collaboration.
  • Examining existing research gaps within these stages.
  • Proposing the integration of interactive behaviors for bi-directional human-robot information transfer.

Main Results:

  • Identified research gaps in current SRRL methodologies.
  • Proposed interactive behaviors as a key enabler for SRRL.
  • Highlighted the importance of conversational AI like ChatGPT in SRRL.

Conclusions:

  • Interactive behaviors offer a promising direction for enhancing SRRL.
  • SRRL with interactive behaviors requires further research attention.
  • Open challenges include robustness, efficiency, transparency, and adaptability of interactive SRRL.