Variety Wins: Soccer-Playing Robots and Infant Walking.

Ori Ossmy,Karen E Adolph,Peter Stone,Justine E Hoch,Shohan Hasan,Patrick Macalpine

doi:10.3389/fnbot.2018.00019

Ori Ossmy, Karen E Adolph + Show 4 more

Open Access

https://doi.org/10.3389/fnbot.2018.00019

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Abstract

Although both infancy and artificial intelligence (AI) researchers are interested in developing systems that produce adaptive, functional behavior, the two disciplines rarely capitalize on their complementary expertise. Here, we used soccer-playing robots to test a central question about the development of infant walking. During natural activity, infants' locomotor paths are immensely varied. They walk along curved, multi-directional paths with frequent starts and stops. Is the variability observed in spontaneous infant walking a “feature” or a “bug?” In other words, is variability beneficial for functional walking performance? To address this question, we trained soccer-playing robots on walking paths generated by infants during free play and tested them in simulated games of “RoboCup.” In Tournament 1, we compared the functional performance of a simulated robot soccer team trained on infants' natural paths with teams trained on less varied, geometric paths—straight lines, circles, and squares. Across 1,000 head-to-head simulated soccer matches, the infant-trained team consistently beat all teams trained with less varied walking paths. In Tournament 2, we compared teams trained on different clusters of infant walking paths. The team trained with the most varied combination of path shape, step direction, number of steps, and number of starts and stops outperformed teams trained with less varied paths. This evidence indicates that variety is a crucial feature supporting functional walking performance. More generally, we propose that robotics provides a fruitful avenue for testing hypotheses about infant development; reciprocally, observations of infant behavior may inform research on artificial intelligence.

Highlights

Both infancy and artificial intelligence (AI) researchers are interested in developing systems that produce adaptive, functional behavior
Final values of the walking parameters (Table 1; see MacAlpine et al, 2012b for more details) indicate that training on varied paths leads to improvements in the optimization process in terms of stability, speed, and shifts in direction
We tested the functional utility of varied paths in infant walking using simulated soccer-playing robots, a model that shares many of the critical components of real-world infant walking

Summary

Introduction

Both infancy and artificial intelligence (AI) researchers are interested in developing systems that produce adaptive, functional behavior. Infancy researchers have the benefit of starting with infants—one of nature’s most flexible and generative learning machines. Infancy researchers work backward to reverse engineer infants’ underlying learning mechanisms and develop formal theories. These theories, are often difficult to test experimentally; controlled rearing environments and training regimens are notoriously slow, burdensome, and in some cases, outright impossible. We use the computational power of AI to test an otherwise intractable developmental question: What is the best way to learn a generative skill like walking?

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