What Can Babies Teach Us About Making Robots?

October 23, 2018

How long before robots take their mastery of games like chess or go and generalize to other
arenas of engagement on Earth? Some scientists, namely developmental roboticists, explore
the underlying mechanisms which could allow lifelong, open-ended learning, similar to learning
found in humans. Robot’s ability to learn from their own experience holds the key to their
eventual realization of intelligence which surpasses humans.
One emerging theory about learning in humans posits that active learning involves comparing
our expectations about the world to the actual world as we experience it. Learning itself can be
considered a simple update of our expectations about the world to match the reality of
experience as it unfolds. Expectations about the world are a vital component to how humans
manage to survive and learn. For robots to have the correct expectations and be able to
generate new expectations on-the-fly, they have to be able to learn to build expectations from
scratch. Is there a better model system of an active learner than the human infant?
One initial attempt at creating baby-like robots, in order to explore how we might program
open-ended learning into machines, was conducted by Dr. Pierre-Yves Oudeyer, a former
computer research scientist for Sony, now the director of the Ensta-ParisTech FLOWERS team in
France. This study attempted to test a model of learning that functions, in a way, by monitoring
its own learning progress. If a task was too hard, the robots would not pursue the task. If the
task was too easy, the robot recognized the activity’s futility. If, however, the task was
intermediately difficult and the robot could monitor its own learning progress, the robot would
continue engaging with the activity. The robot could monitor its own ability to build better
expectations about the outcomes of its own engagement with a task. Perhaps, by utilizing the
framework outlined by Oudeyer for self-organization in learning (papers found here, and here),
we could begin to scale the open-endedness of learning in embodied machines up to human
levels and beyond.

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