NVIDIA Isaac Gym

Import a robot arm model into Isaac Gym.

Define a reward function based on successful object placement.

Train the robot arm using reinforcement learning algorithms.

Evaluate the trained policy in simulation.

Transfer the trained policy to a real robot arm.

Create a simulated environment with obstacles and landmarks.

Implement a navigation algorithm for the mobile robot.

Train the robot to navigate the environment using reinforcement learning.

Evaluate the navigation performance in simulation.

Deploy the navigation policy on a real mobile robot.

Import a quadruped robot model into Isaac Gym.

Define a reward function based on locomotion speed and stability.

Train the robot to walk or run using reinforcement learning.

Evaluate the gait performance in simulation.

Implement the gait controller on a real quadruped robot.

Create a parameterized model of a robotic gripper in Isaac Gym.

Define a performance metric based on grasping success rate.

Optimize the gripper design using reinforcement learning or evolutionary algorithms.

Evaluate the optimized design in simulation.

Fabricate and test the optimized gripper design in the real world.

Import robot and product models into Isaac Gym.

Define a reward function based on correct assembly steps.

Train the robot using reinforcement learning algorithms.

Evaluate the trained policy in simulation.

Transfer the trained policy to a real robot assembly line.