Wednesday, February 25, 2015

Beyond Random Motor Babbling: How to explore when learning behaviors

Humans can learn or improve skills by practicing, e.g., tennis strokes improve as you spend time whacking balls against a wall.  This process is likely to involve exploration of a problem space, in the case of tennis, trying different the actuator values to develop appropriate responses to a range of different ball trajectories and speeds.

In robot skill learning, an important question is 'how do we do this exploration?'  Many papers use random motor babbling [refs], typically within a safe envelope of values and most commonly with a flat probability distribution within each range [refs].  It is highly unlikely that this is how humans and animals explore.

It has been recognised for some time [ref] that system noise can provide sufficient variability to support exploration leading to the identification of effective solutions.  Pinheiro et al. [1] just showed that such variability is also a sufficient requirement for learning new skills in humans.
Think more about using this to inspire new, biologically more plausible, exploration strategies for robot learning.

[1] João de Paula Pinheiro, Pricila Garcia Marques, Go Tani and Umberto Cesar Corrêa (2015) Diversification of motor skills rely upon an optimal amount of variability of perceptive and motor task demands.  In Adaptive Behavior, (only online so far ).

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