Physically Consistent Online Inertial Adaptation for Humanoid Loco-manipulation
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911875769303040 |
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| author | Foster, James McCrory, Stephen DeBuys, Christian Bertrand, Sylvain Griffin, Robert |
| author_facet | Foster, James McCrory, Stephen DeBuys, Christian Bertrand, Sylvain Griffin, Robert |
| contents | The ability to accomplish manipulation and locomotion tasks in the presence of significant time-varying external loads is a remarkable skill of humans that has yet to be replicated convincingly by humanoid robots. Such an ability will be a key requirement in the environments we envision deploying our robots: dull, dirty, and dangerous. External loads constitute a large model bias, which is typically unaccounted for. In this work, we enable our humanoid robot to engage in loco-manipulation tasks in the presence of significant model bias due to external loads. We propose an online estimation and control framework involving the combination of a physically consistent extended Kalman filter for inertial parameter estimation coupled to a whole-body controller. We showcase our results both in simulation and in hardware, where weights are mounted on Nadia's wrist links as a proxy for engaging in tasks where large external loads are applied to the robot. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_07901 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Physically Consistent Online Inertial Adaptation for Humanoid Loco-manipulation Foster, James McCrory, Stephen DeBuys, Christian Bertrand, Sylvain Griffin, Robert Robotics The ability to accomplish manipulation and locomotion tasks in the presence of significant time-varying external loads is a remarkable skill of humans that has yet to be replicated convincingly by humanoid robots. Such an ability will be a key requirement in the environments we envision deploying our robots: dull, dirty, and dangerous. External loads constitute a large model bias, which is typically unaccounted for. In this work, we enable our humanoid robot to engage in loco-manipulation tasks in the presence of significant model bias due to external loads. We propose an online estimation and control framework involving the combination of a physically consistent extended Kalman filter for inertial parameter estimation coupled to a whole-body controller. We showcase our results both in simulation and in hardware, where weights are mounted on Nadia's wrist links as a proxy for engaging in tasks where large external loads are applied to the robot. |
| title | Physically Consistent Online Inertial Adaptation for Humanoid Loco-manipulation |
| topic | Robotics |
| url | https://arxiv.org/abs/2405.07901 |