First Do Not Fall: Learning to Exploit a Wall With a Damaged Humanoid Robot
Timothée Anne, Eloïse Dalin, Ivan Bergonzani, Serena Ivaldi, Jean-Baptiste Mouret
Abstract
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 letter 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 (1.75 m, 100 kg, 30 degrees of freedom) to avoid more than 75% of the avoidable falls and can work on the real robot.
BibTeX
@inproceedings{ral2022_firstdonotfallle,
title = {First Do Not Fall: Learning to Exploit a Wall With a Damaged Humanoid Robot},
author = {Timothée Anne and Eloïse Dalin and Ivan Bergonzani and Serena Ivaldi and Jean-Baptiste Mouret},
booktitle = {RA-L 2022},
year = {2022}
}