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IKDP: Inverse Kinematics through Diffusion Process

2024-10-20 · Hao-Tang Tsui, Yu-Rou Tuan, Hong-Han Shuai

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

📄 PDF Abstract BibTeX arXiv:2410.15341

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Tasks

DenoisingPosition

Methods 이 논문이 사용한 방법론

Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…
Attention 설명 없음
Diffusion Diffusion models generate samples by gradually removing noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound…

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