Teaching a robot to navigate an obstacle course requires more than a walking policy. It must perceive its surroundings, maintain balance, and adjust its actions when obstacles block the path ahead. Developing these capabilities connects several stages: creating assets and an environment, using physics simulation to model how the robot interacts with its environment, and training and evaluating policies in simulation.

NVIDIA RTX Spark serves as a local development hub, supporting AI-assisted planning and policy validation. NVIDIA Isaac Lab provides the robot-learning framework for reinforcement learning in the cloud, while Newton models physical behavior, and NVIDIA Omniverse libraries generate simulated visual observations through RTX rendering. Trained policies return to RTX Spark for local evaluation in NVIDIA Isaac Sim .

This post walks through the workflow using Microduck as a reference robot, with Hermes, NVIDIA Nemotron, and Astra assisting with planning and preparation.…