IKDP: Inverse Kinematics through Diffusion Process

TL;DR

This paper introduces IKDP, a novel approach using Conditional Denoising Diffusion Probabilistic Models with self-attention and Transformer architectures to solve inverse kinematics problems in robotics.

Contribution

It presents a new diffusion-based method for inverse kinematics, integrating probabilistic modeling with deep learning techniques.

Findings

Achieves accurate inverse kinematics solutions.

Outperforms traditional Jacobian inverse methods.

Demonstrates effectiveness on robotic arm benchmarks.

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