Legged Robot State Estimation With Invariant Extended Kalman Filter Using Neural Measurement Network
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866912084724285440 |
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| author | Youm, Donghoon Oh, Hyunsik Choi, Suyoung Kim, Hyeongjun Hwangbo, Jemin |
| author_facet | Youm, Donghoon Oh, Hyunsik Choi, Suyoung Kim, Hyeongjun Hwangbo, Jemin |
| contents | This paper introduces a novel proprioceptive state estimator for legged robots that combines model-based filters and deep neural networks. Recent studies have shown that neural networks such as multi-layer perceptron or recurrent neural networks can estimate the robot states, including contact probability and linear velocity. Inspired by this, we develop a state estimation framework that integrates a neural measurement network (NMN) with an invariant extended Kalman filter. We show that our framework improves estimation performance in various terrains. Existing studies that combine model-based filters and learning-based approaches typically use real-world data. However, our approach relies solely on simulation data, as it allows us to easily obtain extensive data. This difference leads to a gap between the learning and the inference domain, commonly referred to as a sim-to-real gap. We address this challenge by adapting existing learning techniques and regularization. To validate our proposed method, we conduct experiments using a quadruped robot on four types of terrain: \textit{flat}, \textit{debris}, \textit{soft}, and \textit{slippery}. We observe that our approach significantly reduces position drift compared to the existing model-based state estimator. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_00366 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Legged Robot State Estimation With Invariant Extended Kalman Filter Using Neural Measurement Network Youm, Donghoon Oh, Hyunsik Choi, Suyoung Kim, Hyeongjun Hwangbo, Jemin Robotics Artificial Intelligence This paper introduces a novel proprioceptive state estimator for legged robots that combines model-based filters and deep neural networks. Recent studies have shown that neural networks such as multi-layer perceptron or recurrent neural networks can estimate the robot states, including contact probability and linear velocity. Inspired by this, we develop a state estimation framework that integrates a neural measurement network (NMN) with an invariant extended Kalman filter. We show that our framework improves estimation performance in various terrains. Existing studies that combine model-based filters and learning-based approaches typically use real-world data. However, our approach relies solely on simulation data, as it allows us to easily obtain extensive data. This difference leads to a gap between the learning and the inference domain, commonly referred to as a sim-to-real gap. We address this challenge by adapting existing learning techniques and regularization. To validate our proposed method, we conduct experiments using a quadruped robot on four types of terrain: \textit{flat}, \textit{debris}, \textit{soft}, and \textit{slippery}. We observe that our approach significantly reduces position drift compared to the existing model-based state estimator. |
| title | Legged Robot State Estimation With Invariant Extended Kalman Filter Using Neural Measurement Network |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2402.00366 |