  Humanoid robots could replace humans in hazardous situations but most of such
situations are equally dangerous for them, which means that they have a high
chance of being damaged and falling. We hypothesize that humanoid robots would
be mostly used in buildings, which makes them likely to be close to a wall. To
avoid a fall, they can therefore lean on the closest wall, as a human would do,
provided that they find in a few milliseconds where to put the hand(s). This
article introduces a method, called D-Reflex, that learns a neural network that
chooses this contact position given the wall orientation, the wall distance,
and the posture of the robot. This contact position is then used by a
whole-body controller to reach a stable posture. We show that D-Reflex allows a
simulated TALOS robot (1.75m, 100kg, 30 degrees of freedom) to avoid more than
75% of the avoidable falls and can work on the real robot.
