Brain-Body-Task Co-Adaptation can Improve Autonomous Learning and Speed
  of Bipedal Walking
Darío Urbina-Meléndez Hesam Azadjou Francisco J. Valero-Cuevas
Inspired by animals that co-adapt their brain and body to interact with the
environment, we present a tendon-driven and over-actuated (i.e., n joint, n+1
actuators) bipedal robot that (i) exploits its backdrivable mechanical
properties to manage body-environment interactions without explicit control,
and (ii) uses a simple 3-layer neural network to learn to walk after only 2
minutes of 'natural' motor babbling (i.e., an exploration strategy that is
compatible with leg and task dynamics; akin to childsplay). This brain-body
collaboration first learns to produce feet cyclical movements 'in air' and,
without further tuning, can produce locomotion when the biped is lowered to be
in slight contact with the ground. In contrast, training with 2 minutes of
'naive' motor babbling (i.e., an exploration strategy that ignores leg task
dynamics), does not produce consistent cyclical movements 'in air', and
produces erratic movements and no locomotion when in slight contact with the
ground. When further lowering the biped and making the desired leg trajectories
reach 1cm below ground (causing the desired-vs-obtained trajectories error to
be unavoidable), cyclical movements based on either natural or naive babbling
presented almost equally persistent trends, and locomotion emerged with naive
babbling. Therefore, we show how continual learning of walking in unforeseen
circumstances can be driven by continual physical adaptation rooted in the
backdrivable properties of the plant and enhanced by exploration strategies
that exploit plant dynamics. Our studies also demonstrate that the bio-inspired
codesign and co-adaptations of limbs and control strategies can produce
locomotion without explicit control of trajectory errors.