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Journal Articles IEEE Robotics and Automation Letters Year : 2022

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.
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Dates and versions

hal-03740371 , version 1 (29-07-2022)

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Cite

Timothee Anne, Eloise Dalin, Ivan Bergonzani, Serena Ivaldi, Jean-Baptiste Mouret. First Do Not Fall: Learning to Exploit a Wall With a Damaged Humanoid Robot. IEEE Robotics and Automation Letters, 2022, 7 (4), pp.9028-9035. ⟨10.1109/LRA.2022.3188884⟩. ⟨hal-03740371⟩
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