IKDP: Inverse Kinematics through Diffusion Process
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914980982423552 |
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| author | Tsui, Hao-Tang Tuan, Yu-Rou Shuai, Hong-Han |
| author_facet | Tsui, Hao-Tang Tuan, Yu-Rou Shuai, Hong-Han |
| contents | It is a common problem in robotics to specify the position of each joint of the robot so that the endpoint reaches a certain target in space. This can be solved in two ways, forward kinematics method and inverse kinematics method. However, inverse kinematics cannot be solved by an algorithm. The common method is the Jacobian inverse technique, and some people have tried to find the answer by machine learning. In this project, we will show how to use the Conditional Denoising Diffusion Probabilistic Model to integrate the solution of calculating IK. Index Terms: Inverse kinematics, Denoising Diffusion Probabilistic Model, self Attention, Transformer |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_15341 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | IKDP: Inverse Kinematics through Diffusion Process Tsui, Hao-Tang Tuan, Yu-Rou Shuai, Hong-Han Artificial Intelligence It is a common problem in robotics to specify the position of each joint of the robot so that the endpoint reaches a certain target in space. This can be solved in two ways, forward kinematics method and inverse kinematics method. However, inverse kinematics cannot be solved by an algorithm. The common method is the Jacobian inverse technique, and some people have tried to find the answer by machine learning. In this project, we will show how to use the Conditional Denoising Diffusion Probabilistic Model to integrate the solution of calculating IK. Index Terms: Inverse kinematics, Denoising Diffusion Probabilistic Model, self Attention, Transformer |
| title | IKDP: Inverse Kinematics through Diffusion Process |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2410.15341 |