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Learning algorithms

173 bytes added, 25 April
→‎Learning algorithms
= Learning algorithms =Learning algorithms allow to train humanoids to perform different skills such as manipulation or locomotion. Below is an overview of general approaches to training machine learning models for humanoid robots with example [[applications]]. Typically you need a simulator, training framework and machine learning method to train end to end behaviors.
== Physics engines ==
Physics engines are software libraries designed to simulate physical systems in a virtual environment. They are crucial in a variety of fields such as video games, animation, robotics, and engineering simulations. These engines handle the mathematics involved in simulating physical processes like motion, collisions, and fluid dynamics.
For a much more comprehensive overview see [https://simulately.wiki/docs/ Simulately].
===PhysX===
MuJoCo (Multi-Joint dynamics with Contact) is a physics engine designed for research in robotics and biomechanics. It's known for its speed, accuracy, and ease of use, making it popular for simulating complex systems with robotics and articulated structures.
===Bullet===
Bullet is a physics engine supporting real-time collision detection and multi-physics simulation for VR, games, visual effects, robotics, machine learning
===[[VSim]]===
==Training frameworks==
Popular training frameworks are listed here with example applications.
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