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Created page with "= Learning algorithms = Learning algorithms allow to train humanoids to perform different skills such as manipulation or locomotion. Below is an overview of general approaches..."
= 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.
== 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.
===PhysX===
PhysX is a physics engine by NVIDIA used primarily for video games and real-time simulations. It supports rigid body dynamics, cloth simulation, and particle effects, enhancing realism and interactivity in 3D environments.
===Mujoco===
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.
== Training frameworks ==
Popular training frameworks
===Isaac Gym===
Isaac Gym is NVIDIA's robotics simulation tool, part of the Isaac SDK. It leverages GPU acceleration to enable the simulation of thousands of robot bodies simultaneously, making it highly efficient for training machine learning models in robotics. It's designed to streamline robotics applications, focusing on reinforcement learning in a virtual environment.
===Gymnasium===
Gymnasium is an open-source toolkit for developing and comparing reinforcement learning algorithms. Originally developed by OpenAI as "Gym," it provides a standardized set of environments (like Atari games, robotic simulations, etc.) to test and benchmark AI algorithms. It's widely used in the AI research community to foster innovation and replication in RL studies.
== Training algorithms ==
===Imitation learning===
Imitation Learning is a technique where models learn to perform tasks by mimicking expert behaviors. This approach is often used when defining explicit reward functions is challenging. It accelerates learning by using pre-collected datasets of expert demonstrations, reducing the need for trial-and-error in initial learning phases.
===Reinforcement Learning===
Reinforcement Learning involves agents learning to make decisions by interacting with an environment to maximize cumulative rewards. It's foundational in fields where sequential decision-making is crucial, like gaming, autonomous vehicles, and robotics. RL uses methods like Q-learning and policy gradient to iteratively improve agent performance based on feedback from the environment.
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.
== 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.
===PhysX===
PhysX is a physics engine by NVIDIA used primarily for video games and real-time simulations. It supports rigid body dynamics, cloth simulation, and particle effects, enhancing realism and interactivity in 3D environments.
===Mujoco===
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.
== Training frameworks ==
Popular training frameworks
===Isaac Gym===
Isaac Gym is NVIDIA's robotics simulation tool, part of the Isaac SDK. It leverages GPU acceleration to enable the simulation of thousands of robot bodies simultaneously, making it highly efficient for training machine learning models in robotics. It's designed to streamline robotics applications, focusing on reinforcement learning in a virtual environment.
===Gymnasium===
Gymnasium is an open-source toolkit for developing and comparing reinforcement learning algorithms. Originally developed by OpenAI as "Gym," it provides a standardized set of environments (like Atari games, robotic simulations, etc.) to test and benchmark AI algorithms. It's widely used in the AI research community to foster innovation and replication in RL studies.
== Training algorithms ==
===Imitation learning===
Imitation Learning is a technique where models learn to perform tasks by mimicking expert behaviors. This approach is often used when defining explicit reward functions is challenging. It accelerates learning by using pre-collected datasets of expert demonstrations, reducing the need for trial-and-error in initial learning phases.
===Reinforcement Learning===
Reinforcement Learning involves agents learning to make decisions by interacting with an environment to maximize cumulative rewards. It's foundational in fields where sequential decision-making is crucial, like gaming, autonomous vehicles, and robotics. RL uses methods like Q-learning and policy gradient to iteratively improve agent performance based on feedback from the environment.