Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Observational Learning

Observational Learning

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 because...

You might also read

Related Articles

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

Sort by

Same author

Is problematic internet use always problematic? An experience-sampling study of compulsive and avoidance-driven internet behaviors and momentary mood.

Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors·2026

Same author

Multiclass Linear Perceptrons With Multiplicative Margins.

Neural computation·2026

Same author

Graph vector function architecture.

Neural networks : the official journal of the International Neural Network Society·2025

Same author

Combined Individual Experience and Accelerometry Measurement of Upper Limb Use in Daily Activities in Real Time After Stroke.

Sensors (Basel, Switzerland)·2025

Same author

Feedback-integrated prompt optimiser for problem formulation.

Scientific reports·2025

Same author

Reconstructing impaired language using generative AI for people with aphasia.

Scientific reports·2025

Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 24, 2026

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

Hardware Efficient Direct Policy Imitation Learning for Robotic Navigation in Resource-Constrained Settings.

Vidura Sumanasena¹, Heshan Fernando², Daswin De Silva¹

¹Research Centre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3083, Australia.

Sensors (Basel, Switzerland)

|January 11, 2024

Summary

This summary is machine-generated.

This study introduces a lightweight Direct Policy Learning (DPL) method for training mobile robots, enhancing navigation and safety. The approach achieves expert-level performance quickly in both simulation and real-world settings.

Keywords:

autonomous navigation direct policy learning imitation learning mobile robots

More Related Videos

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Utilizing a Reconfigurable Maze System to Enhance the Reproducibility of Spatial Navigation Tests in Rodents

Utilizing a Reconfigurable Maze System to Enhance the Reproducibility of Spatial Navigation Tests in Rodents

Published on: December 2, 2022

Related Experiment Videos

Last Updated: Jun 24, 2026

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Utilizing a Reconfigurable Maze System to Enhance the Reproducibility of Spatial Navigation Tests in Rodents

Utilizing a Reconfigurable Maze System to Enhance the Reproducibility of Spatial Navigation Tests in Rodents

Published on: December 2, 2022

Area of Science:

Robotics
Artificial Intelligence
Machine Learning

Background:

Direct Policy Learning (DPL) is key for efficient mobile robot training.
Real-world DPL faces challenges with human expertise and performance measurement.
Resource constraints limit deep learning in autonomous systems.

Purpose of the Study:

To develop a lightweight DPL approach for mobile robot navigation.
To integrate a safety policy for enhanced robot and environmental protection.
To evaluate the method's effectiveness in simulation and real-world scenarios.

Main Methods:

Implemented a lightweight DPL framework for navigational tasks.
Integrated a safety policy with the primary navigational policy.
Conducted evaluations in both simulated and physical environments.

Main Results:

The lightweight DPL approach demonstrated improved performance over hardware-intensive methods.
Achieved expert-level performance within 15 training iterations.
Showcased efficient transferability from simulation to real-world applications.

Conclusions:

The proposed lightweight DPL method is effective for training mobile robots.
It offers a practical solution for resource-constrained autonomous systems.
The approach enhances safety and accelerates learning in navigational tasks.