First do not fall: learning to exploit a wall with a damaged humanoid robot
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 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.
Keywords
Cite
@article{arxiv.2203.00316,
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},
journal= {arXiv preprint arXiv:2203.00316},
year = {2024}
}
Comments
Accepted in IEEE Robotics and Automation Letters, June, 2022 Video presenting the results: https://youtu.be/hbuWr-ZNAtg