| Reproducibility and Determinism |
| |
|
|
| Given the same hardware and Isaac Sim (and consequently PhysX) version, the simulation produces |
| identical results for scenes with rigid bodies and articulations. However, the simulation results can |
| vary across different hardware configurations due to floating point precision and rounding errors. |
| At present, PhysX does not guarantee determinism for any scene with non-rigid bodies, such as cloth |
| or soft bodies. For more information, please refer to the `PhysX Determinism documentation`_. |
|
|
| Based on above, Isaac Lab provides a deterministic simulation that ensures consistent simulation |
| results across different runs. This is achieved by using the same random seed for the |
| simulation environment and the physics engine. At construction of the environment, the random seed |
| is set to a fixed value using the :meth:`~isaaclab.utils.seed.configure_seed` method. This method sets the |
| random seed for both the CPU and GPU globally across different libraries, including PyTorch and |
| NumPy. |
|
|
| In the included workflow scripts, the seed specified in the learning agent's configuration file or the |
| command line argument is used to set the random seed for the environment. This ensures that the |
| simulation results are reproducible across different runs. The seed is set into the environment |
| parameters :attr:`isaaclab.envs.ManagerBasedEnvCfg.seed` or :attr:`isaaclab.envs.DirectRLEnvCfg.seed` |
| depending on the manager-based or direct environment implementation respectively. |
|
|
| For results on our determinacy testing for RL training, please check the GitHub Pull Request ` |
|
|
| .. tip:: |
|
|
| Due to GPU work scheduling, there's a possibility that runtime changes to simulation parameters |
| may alter the order in which operations take place. This occurs because environment updates can |
| happen while the GPU is occupied with other tasks. Due to the inherent nature of floating-point |
| numeric storage, any modification to the execution ordering can result in minor changes in the |
| least significant bits of output data. These changes may lead to divergent execution over the |
| course of simulating thousands of environments and simulation frames. |
|
|
| An illustrative example of this issue is observed with the runtime domain randomization of object's |
| physics materials. This process can introduce both determinacy and simulation issues when executed |
| on the GPU due to the way these parameters are passed from the CPU to the GPU in the lower-level APIs. |
| Consequently, it is strongly advised to perform this operation only at setup time, before the |
| environment stepping commences. |
|
|
|
|
| .. _PhysX Determinism documentation: https://nvidia-omniverse.github.io/PhysX/physx/5.4.1/docs/API.html |
| .. _ |
|
|
