# Rigid body physics (machine learning environment)

• I
• AI_Messiah
In summary, the conversation discusses the reason for objects falling over due to lack of support for their center of mass, and the desire to simulate this motion using a 3D mesh character. The conversation also touches on the use of machine learning and equations, such as gravity, in the simulation process.
AI_Messiah
TL;DR Summary
how would I simulate rotation on a mesh character when there is nothing to hold it's center of mass.
I guess that the reason that objects fall over is that there is nothing to hold it's center of mass. So what I want to do is to work out how fast in Delta intervals it would rotate and fall over.

AI_Messiah said:
Summary:: how would I simulate rotation on a mesh character when there is nothing to hold it's center of mass.

I guess that the reason that objects fall over is that there is nothing to hold it's center of mass. So what I want to do is to work out how fast in Delta intervals it would rotate and fall over.
Welcome to PF.

What's a mesh character? Is this for a computer simulation of a person for CGI?

What kind of simulation software do you have available for these simulations? Or do you mean just simulating motion of a 3D object using equations (and some simple plotting software like Excel)?

This is for use in a machine learning environment where a mesh will have to learn to walk around and keep it's balance. So like my post says I will get the center of mass and check it's vertices and see if there is a part touching the ground on 4 sides past it's center of mass. Changes to the mesh will happen in timeframes. So I guess this will be dependent on things like my equation for gravity?

## 1. What is a rigid body in physics?

A rigid body is an object that maintains its shape and size even when subjected to external forces. In other words, the distance between any two points on the object remains constant, and the object does not deform under the influence of forces.

## 2. How is rigid body physics used in machine learning environments?

Rigid body physics is used in machine learning environments to simulate the movement and interactions of physical objects. This allows for more realistic and dynamic simulations, which can be used for training and testing machine learning algorithms.

## 3. What are the basic principles behind rigid body physics?

The basic principles behind rigid body physics include Newton's laws of motion, conservation of energy, and conservation of momentum. These principles govern the movement and interactions of rigid bodies in a physical environment.

## 4. What are some common applications of rigid body physics in machine learning?

Rigid body physics is commonly used in machine learning for tasks such as object manipulation, motion planning, and robot control. It can also be applied to virtual reality and video game environments to create more realistic and interactive simulations.

## 5. What are the limitations of using rigid body physics in machine learning environments?

One limitation of using rigid body physics in machine learning environments is that it can be computationally expensive, especially when simulating large numbers of objects. Additionally, rigid body physics may not accurately model all types of physical interactions, such as fluid dynamics or soft body deformations.

• Other Physics Topics
Replies
6
Views
427
• Other Physics Topics
Replies
2
Views
1K
• Other Physics Topics
Replies
22
Views
3K
• Mechanics
Replies
8
Views
1K
• Classical Physics
Replies
8
Views
1K
• Classical Physics
Replies
3
Views
912
• Other Physics Topics
Replies
2
Views
1K
• Other Physics Topics
Replies
1
Views
1K
• General Math
Replies
5
Views
1K
• Other Physics Topics
Replies
10
Views
3K