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Bridging the Gap: Simulation Enhancement — Getting Started With Isaac Lab

Last updated: 12/12/2025

Title: Bridging the Gap: Simulation Enhancement#

URL Source: https://docs.nvidia.com/learning/physical-ai/getting-started-with-isaac-lab/latest/transferring-robot-learning-policies-from-simulation-to-reality/03-bridging-the-gap-simulation-enhancement/index.html

Published Time: Sat, 15 Nov 2025 05:02:09 GMT

Markdown Content: Domain Randomization#

Domain randomization is a key technique for expanding our simulation domain and improving system robustness. Here’s how we implement it:

Randomize Physical Parameters#

We modify various physical attributes of our simulated environment:

  • Body mass properties of the robot and manipulated objects

  • Joint parameters for different movement characteristics

  • Friction coefficients between the various surfaces

Note

While link length randomization is possible, it’s challenging to implement effectively with most simulators.

Randomize Shapes#

We vary the physical forms in our simulation:

  • Terrain variations to expose the robot to different ground conditions

  • Object shapes and sizes for grasping tasks, helping the robot learn general concepts rather than specific instances

Randomize Tasks#

We introduce variability in how tasks are performed:

  • Random initial conditions for robot joint states

  • Varied object positions and orientations

  • Diverse command sets, such as different target velocities

Add Perturbations#

To build robustness, we incorporate various disturbances:

  • Observation noise, particularly for naturally noisy signals like joint velocities, or data coming from an inertial measurement unit (IMU)

  • Physical pushes to develop recovery behaviors

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