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Related Experiment Video

Updated: May 27, 2026

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology
05:38

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology

Published on: June 29, 2021

Language bootstrapping: learning word meanings from perception-action association.

Giampiero Salvi1, Luis Montesano, Alexandre Bernardino

  • 1Department of Speech, Music, and Hearing, Kungliga Tekniska Högskolan (KTH), Stockholm, Sweden. giampi@kth.se

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|November 23, 2011
PubMed
Summary
This summary is machine-generated.

This study demonstrates how robots can learn word meanings through interaction and verbal cues, similar to infant language acquisition. This method enables robots to understand and perform tasks based on spoken commands, even with imperfect speech recognition.

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Published on: July 13, 2019

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Cognitive Science

Background:

  • Bootstrapping language acquisition in artificial systems is challenging.
  • Human infants learn language through interaction and associating words with environmental perceptions and actions.

Purpose of the Study:

  • To develop a method for robots to acquire word-to-meaning associations through manipulation tasks and verbal descriptions.
  • To ground verbal symbols in robot actions and environmental perceptions.

Main Methods:

  • Utilized an affordance network mapping robot actions, perceptions, and effects.
  • Extended the affordance model to incorporate spoken words and temporal co-occurrence.
  • Linked speech utterances to objects, actions, and effects during robot interaction.

Main Results:

  • The robot successfully formed word-to-meaning associations without grammatical structure or perfect recognition.
  • Learned associations were embedded in the robot's action understanding.
  • Enabled direct task instruction and context-aware speech recognition.

Conclusions:

  • The proposed approach facilitates robots learning language descriptors in their operational environments.
  • This method offers insights into the developmental process of language acquisition in human infants.