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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...
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Using Virtual Reality to Transfer Motor Skill Knowledge from One Hand to Another
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Learning Transferable Push Manipulation Skills in Novel Contexts.

Rhys Howard1, Claudio Zito2

  • 1Cognitive Robotics Group, Oxford Robotics Institute, Oxford, United Kingdom.

Frontiers in Neurorobotics
|June 24, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for robots to predict push manipulation outcomes in new situations. The approach uses internal models to learn object interactions, improving robotic adaptability and prediction accuracy.

Keywords:
forward models for physical interactionlearning transferable skillspredictionpush manipulationrobotics

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Area of Science:

  • Robotics
  • Machine Learning
  • Physics Simulation

Background:

  • Robots struggle with predicting outcomes in novel manipulation tasks.
  • Current models lack adaptability to new object properties and environmental conditions.

Purpose of the Study:

  • To develop transferable forward models for robot push manipulation.
  • To enable robots to predict interaction outcomes in unseen contexts.
  • To enhance prediction accuracy using available environmental information.

Main Methods:

  • Learning a parametric internal model for push interactions.
  • Factorizing learning into local contact and motion models.
  • Developing unbiased and biased predictors by adjusting parameter distributions.

Main Results:

  • Demonstrated effectiveness in simulation with a Pioneer 3-DX robot.
  • Validated with a proof of concept on a real robot.
  • Achieved reliable predictions matching physics simulator outcomes across diverse objects.

Conclusions:

  • The proposed internal model enables robots to predict push outcomes in novel contexts.
  • Adjustable predictors (biased/unbiased) enhance adaptability and accuracy.
  • This approach significantly improves robotic manipulation capabilities.