Enables interactive design, execution, and analysis of SUMO traffic simulations through natural language, providing tools for scenario generation, policy experimentation, result analysis, and visualization.
Enables AI interaction with embodied humans, autonomous vehicles, and drones through the HUTB simulator. Supports voice/chat control, weather conditions, perspective switching, and recording features.
Enables a language model to perceive a robot's surroundings, navigate to referenced objects, count instances, and stop when finished, via tools for viewing, grounding, driving, and counting.
Connects LLMs to Eclipse SUMO traffic simulation, enabling AI agents to automate traffic network generation, demand modeling, signal optimization, simulation execution, and real-time TraCI control through natural language.
Enables AI tools like Windsurf and Claude to control NVIDIA Isaac Sim and Isaac Lab through natural language, providing tools for scene inspection, prim management, physics simulation, and robot spawning.