A Data-driven Contact Estimation Method for Wheeled-Biped Robots
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929683794231296 |
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| author | Gökbakan, Ü. Bora Dümbgen, Frederike Caron, Stéphane |
| author_facet | Gökbakan, Ü. Bora Dümbgen, Frederike Caron, Stéphane |
| contents | Contact estimation is a key ability for limbed robots, where making and breaking contacts has a direct impact on state estimation and balance control. Existing approaches typically rely on gate-cycle priors or designated contact sensors. We design a contact estimator that is suitable for the emerging wheeled-biped robot types that do not have these features. To this end, we propose a Bayes filter in which update steps are learned from real-robot torque measurements while prediction steps rely on inertial measurements. We evaluate this approach in extensive real-robot and simulation experiments. Our method achieves better performance while being considerably more sample efficient than a comparable deep-learning baseline. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_12345 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Data-driven Contact Estimation Method for Wheeled-Biped Robots Gökbakan, Ü. Bora Dümbgen, Frederike Caron, Stéphane Robotics Probability Contact estimation is a key ability for limbed robots, where making and breaking contacts has a direct impact on state estimation and balance control. Existing approaches typically rely on gate-cycle priors or designated contact sensors. We design a contact estimator that is suitable for the emerging wheeled-biped robot types that do not have these features. To this end, we propose a Bayes filter in which update steps are learned from real-robot torque measurements while prediction steps rely on inertial measurements. We evaluate this approach in extensive real-robot and simulation experiments. Our method achieves better performance while being considerably more sample efficient than a comparable deep-learning baseline. |
| title | A Data-driven Contact Estimation Method for Wheeled-Biped Robots |
| topic | Robotics Probability |
| url | https://arxiv.org/abs/2410.12345 |