IKDP: Inverse Kinematics through Diffusion Process

Fuente: arXiv
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Auteurs principaux: Tsui, Hao-Tang, Tuan, Yu-Rou, Shuai, Hong-Han
Format: Preprint
Publié: 2024
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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