The Impact of Motor Action on Language Acquisition and Action-Verb Learning in a Robot
Zakaria LEMHAOURI, Laura Cohen, Ann Nowé, Lola Canamero
AI summary
Problem
How do early motor actions qualitatively and quantitatively influence language development, particularly the acquisition of action verbs which typically lags behind noun learning in humans?
Approach
A developmental robotics architecture where a simulated robot learns language through embodied interactions with a virtual caregiver, using reinforcement learning and recurrent neural networks to map gestures, speech, and sensory feedback to object names and action verbs.
Key results
- Gesture-enabled robots expanded vocabulary size more than speech-only counterparts
- Action verb acquisition was significantly facilitated by motor capabilities
- Verb learning lagged behind noun learning, replicating human developmental trajectories
- Reinforcement learning successfully grounded vocabulary in sensory data and goal achievement
Why it matters
Provides a validated computational framework for developmental robotics that informs the design of socially assistive robots and advances embodied theories of language acquisition.
Abstract
In humans, the acquisition of a new motor skill is associated with the development of a wide range of cognitive areas and can create contexts in which new cognitive capacities develop. Motor development is linked to language development in infants, as crawling and walking promote active exploration of the environment, while manipulating objects and pointing draw the caregiver’s attention and help establish joint atten- tion. Together, these motor experiences broaden communication contexts and support the learning of nouns (object-based words) and verbs (action-based words). However, many questions remain unanswered about how children’s actions influence language de- velopment, qualitatively and quantitatively, and how they help the acquisition of different types of words, particularly the learning of verbs. In this paper, we propose a robot architecture to study how gestures can affect early language learning. The architecture follows the developmental robotics paradigm, i.e. inspired by the way human children develop and acquire language according to multiple developmental theories. The experimental results demonstrate that enabling the robot to produce gestures expands its vocabulary size and facilitates the acquisition of verbs. These results are in line with the finding that verb learning lags behind noun learning since the acquisition of verbs depends more on motor abilities and requires the maturation of motor development.