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IKDP: Inverse Kinematics through Diffusion Process

Hao-Tang Tsui, Yu-Rou Tuan, Hong-Han Shuai

TL;DR

This project will show how to use the Conditional Denoising Diffusion Probabilistic Model to integrate the solution of calculating IK, the inverse kinematics method.

Abstract

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

IKDP: Inverse Kinematics through Diffusion Process

TL;DR

This project will show how to use the Conditional Denoising Diffusion Probabilistic Model to integrate the solution of calculating IK, the inverse kinematics method.

Abstract

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

Paper Structure

This paper contains 19 sections, 5 equations, 8 figures, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Inverse Kinematics Method (IK)
  4. Denoising Diffusion Probabilistic Models (DDPM)
  5. Methods
  6. Ideas
  7. Propose
  8. Loss Function
  9. Distance of ${t}$
  10. Loss of ${\theta}$
  11. Model Architecture
  12. Training Process
  13. Experiments
  14. Visualization
  15. Experiments with different joints
  16. ...and 4 more sections

Figures (8)

  • Figure 1: Pose Estimation Calculation for the depth of joints
  • Figure 2: Robotic arm vector schematic diagram
  • Figure 3: Pose Estimation Calculation for the depth of joints
  • Figure 4: Process of training conditional DDPM model
  • Figure 5: Pose Estimation Calculation for the depth of joints
  • ...and 3 more figures