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Related Concept Videos

Observational Learning01:12

Observational Learning

586
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...
586

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Related Experiment Video

Updated: Nov 10, 2025

Robotic Sensing and Stimuli Provision for Guided Plant Growth
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Autonomous Learning of New Environments with a Robotic Team Employing Hyper-Spectral Remote Sensing, Comprehensive

David J Lary1, David Schaefer1, John Waczak1

  • 1Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA.

Sensors (Basel, Switzerland)
|April 3, 2021
PubMed
Summary
This summary is machine-generated.

Autonomous robots quickly learn new environments using machine learning. This technology enables rapid mapping of environmental composition, even with small-scale variations, and supports satellite data applications.

Keywords:
UAVautonomoushyper-spectral imagingmachine learningrobot teamrobotic boat

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Area of Science:

  • Robotics
  • Environmental Science
  • Machine Learning

Background:

  • Autonomous systems are increasingly used for environmental monitoring.
  • Scalable multi-robot systems are needed for comprehensive data acquisition.
  • Machine learning offers potential for rapid environmental characterization.

Purpose of the Study:

  • To demonstrate an autonomous robotic team capable of rapid environmental learning.
  • To showcase a flexible and scalable paradigm for multi-robot, multi-sensor teams.
  • To apply this approach to aquatic environment characterization and remote sensing data product creation.

Main Methods:

  • Deployment of an autonomous robotic team for environmental data collection.
  • Utilizing machine learning algorithms for rapid learning from acquired data.
  • Integration of larger autonomous robots with smaller, deployable robots (walking robot, robotic hover-board).

Main Results:

  • The robotic team rapidly learned characteristics of previously unseen environments.
  • Thousands of training data points were acquired in minutes for aquatic environment characterization.
  • Machine learning enabled the generation of wide-area composition maps.
  • Smaller robots revealed significant small-scale spatial variability.

Conclusions:

  • The demonstrated autonomous robotic team paradigm is effective for rapid environmental learning and mapping.
  • The system is scalable and applicable to satellite calibration/validation and new remote sensing data products.
  • Observed small-scale variability highlights the importance of multi-scale robotic sensing.