Composes complex 3D environments and terrains for robotic simulation and reinforcement learning training.
The MuJoCo Scenes skill enables developers to programmatically create diverse simulation environments for robot training. It provides a structured API for adding varied terrains—such as stairs, ramps, and heightfields—alongside interactive objects and obstacles. With built-in support for domain randomization and MJCF export, it streamlines the process of generating large-scale datasets and diverse training scenarios for reinforcement learning tasks, specifically optimized for integration with the KSIM and K-Scale robotics frameworks.
Key Features
01Native integration with KSIM and RLTask frameworks for training
02Automated domain randomization for friction, lighting, and object positions
03Diverse terrain generation including stairs, ramps, and rough heightfields
04Programmatic scene composition via a high-level SceneBuilder API
05Seamless export to MJCF (MuJoCo XML) format for physics simulation
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Use Cases
01Generating randomized obstacle courses for testing collision avoidance algorithms
02Developing high-fidelity simulation environments for reinforcement learning research
03Creating procedural terrains for training bipedal robot walking controllers
