GCP Compute Engine with NVIDIA GPU + NVIDIA Isaac Sim
The Robotics & Simulation Lab brings robotics into the cloud. Instead of needing a physical robot on day one, you work in high-fidelity simulators — NVIDIA Isaac Sim for photorealistic environments and Gazebo for lightweight ROS 2 development. The simulated robots have the same sensors (cameras, LiDAR, IMU), the same motor controllers, and the same ROS 2 interfaces as their physical counterparts. Train a perception model, develop a navigation stack, or teach a robot arm to grasp objects with reinforcement learning — all in simulation before touching real hardware.
Start the lab. A GPU instance with Isaac Sim, Gazebo, and ROS 2 Humble launches. A VNC/web session gives you the graphical interface.
Open a simulation scenario — warehouse with mobile robots, tabletop with a robot arm, or outdoor environment with a drone.
Write ROS 2 nodes in the integrated terminal. Run perception, planning, or control algorithms while the simulator provides sensor data.
For RL exercises, launch training in the simulator. The agent interacts with the environment thousands of times to learn optimal behavior.
Run evaluation episodes. Measure success rates, collision counts, path efficiency, and task completion time.
Save trained policies and perception models. Transfer to the Edge AI Lab for deployment on physical Jetson hardware.
Other AI Labs environments students typically use alongside this one.
Single-GPU environment for training deep learning models, running computer vision pipelines, and experimenting with neural network architect…
Explore lab →Environment for optimizing and deploying AI models on edge hardware. Includes model compression tools, TensorRT optimization, and remote acc…
Explore lab →GPU-accelerated environment for image and video processing. Pre-loaded with large-scale vision datasets, annotation tools, and state-of-the-…
Explore lab →Enroll in a course that uses this lab, or visit our Houston center for a hands-on demo.