First do not fall: learning to exploit a wall with a damaged humanoid robot
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866914741002174464 |
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| author | Anne, Timothée Dalin, Eloïse Bergonzani, Ivan Ivaldi, Serena Mouret, Jean-Baptiste |
| author_facet | Anne, Timothée Dalin, Eloïse Bergonzani, Ivan Ivaldi, Serena Mouret, Jean-Baptiste |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2203_00316 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | First do not fall: learning to exploit a wall with a damaged humanoid robot Anne, Timothée Dalin, Eloïse Bergonzani, Ivan Ivaldi, Serena Mouret, Jean-Baptiste Robotics Artificial Intelligence 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. |
| title | First do not fall: learning to exploit a wall with a damaged humanoid robot |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2203.00316 |