What types of terrain can I generate with this skill?

You can generate flat ground, stairs, ramps, rough heightfields, and terrains with gaps to test various aspects of robot mobility and navigation.

Does it support domain randomization for AI training?

Yes, it includes a DomainRandomizer utility for varying friction, terrain roughness, lighting, and object positions to help create robust AI models.

Is it compatible with other robotics development tools?

Yes, it is specifically designed to integrate with the KSIM framework and works in tandem with skills like urdf2mjcf and kos-firmware.

How do I export the scenes for use in MuJoCo?

The skill provides a .to_mjcf() method that automatically exports your programmatically composed scene into the standard MuJoCo XML (MJCF) format.

MuJoCo Scene Builder

Name: MuJoCo Scene Builder
Author: plurigrid

byplurigrid

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数据科学与机器学习

Composes complex 3D environments and terrains for robotic simulation and reinforcement learning training.

关于

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.

主要功能

Native integration with KSIM and RLTask frameworks for training
Automated domain randomization for friction, lighting, and object positions
Diverse terrain generation including stairs, ramps, and rough heightfields
Programmatic scene composition via a high-level SceneBuilder API
Seamless export to MJCF (MuJoCo XML) format for physics simulation
2 GitHub stars

使用场景

Generating randomized obstacle courses for testing collision avoidance algorithms
Developing high-fidelity simulation environments for reinforcement learning research
Creating procedural terrains for training bipedal robot walking controllers

关于

主要功能

Native integration with KSIM and RLTask frameworks for training
Automated domain randomization for friction, lighting, and object positions
Diverse terrain generation including stairs, ramps, and rough heightfields
Programmatic scene composition via a high-level SceneBuilder API
Seamless export to MJCF (MuJoCo XML) format for physics simulation
2 GitHub stars

使用场景

Generating randomized obstacle courses for testing collision avoidance algorithms
Developing high-fidelity simulation environments for reinforcement learning research
Creating procedural terrains for training bipedal robot walking controllers