Isaac Gym - Preview Release

NVIDIA’s physics simulation environment for reinforcement learning research.

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End-to-End GPU accelerated

Physics simulation in Isaac Gym runs on the GPU, storing results in PyTorch GPU tensors

Thousand of Environments

Using the Isaac Gym tensor-based APIs, observations and rewards can be calculated on the GPU in PyTorch, enabling thousands of environments to run in parallel on a single workstation


Isaac Gym features include:

  • Support for importing URDF and MJCF files with automatic convex decomposition of imported 3D meshes for physical simulation
  • GPU accelerated tensor API for evaluating environment state and applying actions
  • Support for a variety of environment sensors - position, velocity, force, torque, etc
  • Runtime domain randomization of physics parameters
  • Jacobian / inverse kinematics support

The core functionality of Isaac Gym will be made available as part of the NVIDIA Omniverse Platform and Isaac Sim robotics simulator in the future. Until then we are making this preview available to facilitate the work of researchers and academics who want to explore the potential of GPU-based reinforcement learning.


A variety of examples and GPU accelerated training environments are also available:

Additional Resources

DevTalk Forum     Isaac Sim

Note that limited support will be available for this preview prior to the release of tensor-based Gym API support in Omniverse.

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