---
name: mujoco-scenes-a59844d03dea
description: Package for creating different scenes in MuJoCo. Compose environments with objects, terrains, and obstacles for robot training.
version: 1.0.0
category: robotics-simulation
author: K-Scale Labs
source: kscalelabs/mujoco-scenes
license: MIT
trit: 0
trit_label: ERGODIC
color: "#9FD875"
verified: false
featured: false
---

# MuJoCo Scenes Skill

**Trit**: 0 (ERGODIC - coordination/infrastructure)
**Color**: #9FD875 (Soft Green)
**URI**: skill://mujoco-scenes#9FD875

## Overview

Package for composing MuJoCo scenes with objects, terrains, and obstacles. Enables diverse environment generation for robot training.

## Usage

```python
from mujoco_scenes import SceneBuilder, Terrain, Object

# Build a training scene
scene = SceneBuilder()

# Add terrain
scene.add_terrain(
    Terrain.FLAT,
    size=(10, 10),
    friction=1.0,
)

# Add obstacles
scene.add_object(
    Object.BOX,
    pos=(2, 0, 0.5),
    size=(0.5, 0.5, 0.5),
    color=(1, 0, 0, 1),
)

scene.add_object(
    Object.SPHERE,
    pos=(-1, 2, 0.3),
    radius=0.3,
    mass=0.5,
)

# Add terrain variations
scene.add_terrain(
    Terrain.STAIRS,
    pos=(5, 0, 0),
    step_height=0.15,
    step_count=5,
)

# Export to MJCF
mjcf = scene.to_mjcf()
```

## Terrain Types

```
┌─────────────────────────────────────────────────────────────┐
│                     TERRAIN TYPES                            │
├─────────────────────────────────────────────────────────────┤
│                                                              │
│  FLAT        ═══════════════════════════                    │
│                                                              │
│  STAIRS      ┌─┐                                            │
│            ┌─┘ └─┐                                          │
│          ┌─┘     └─┐                                        │
│                                                              │
│  RAMP        ╱╲                                             │
│             ╱  ╲                                            │
│                                                              │
│  ROUGH      ∿∿∿∿∿∿∿∿∿∿∿∿∿∿∿                                │
│  (heightfield)                                               │
│                                                              │
│  GAPS       ═══   ═══   ═══   ═══                           │
│                                                              │
└─────────────────────────────────────────────────────────────┘
```

## Domain Randomization

```python
from mujoco_scenes import DomainRandomizer

randomizer = DomainRandomizer(
    terrain_roughness=(0.0, 0.1),
    friction_range=(0.5, 1.5),
    object_position_noise=0.2,
    lighting_variation=True,
)

# Generate randomized scenes
for i in range(100):
    scene = randomizer.generate()
    scene.save(f"scene_{i}.mjcf")
```

## Integration with KSIM

```python
from ksim import RLTask
from mujoco_scenes import SceneBuilder

class WalkingWithObstacles(RLTask):
    def build_scene(self):
        scene = SceneBuilder()
        scene.add_terrain(Terrain.FLAT)
        scene.add_random_obstacles(count=10)
        return scene.to_mjcf()
```

## GF(3) Triads

This skill acts as the **ERGODIC (0)** coordinator:

```
ksim-rl (-1) ⊗ kos-firmware (+1) ⊗ mujoco-scenes (0) = 0 ✓
evla-vla (-1) ⊗ kos-firmware (+1) ⊗ mujoco-scenes (0) = 0 ✓
urdf2mjcf (-1) ⊗ kos-firmware (+1) ⊗ mujoco-scenes (0) = 0 ✓
kbot-humanoid (-1) ⊗ kos-firmware (+1) ⊗ mujoco-scenes (0) = 0 ✓
zeroth-bot (-1) ⊗ kos-firmware (+1) ⊗ mujoco-scenes (0) = 0 ✓
```

## Related Skills

- `ksim-rl` (-1): Uses scenes for training
- `kos-firmware` (+1): Robot firmware
- `urdf2mjcf` (-1): Model conversion
- `kbot-humanoid` (-1): K-Bot robot

## References

```bibtex
@misc{mujocoscenes2024,
  title={MuJoCo Scenes: Environment Composition for Robot Training},
  author={K-Scale Labs},
  year={2024},
  url={https://github.com/kscalelabs/mujoco-scenes}
}
```


---
Source attribution: plurigrid
Original project: https://github.com/plurigrid/asi/blob/293ce9f76e3abcd5ba18db284688bc0d9ac6691d/plugins/asi/skills/mujoco-scenes/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2025 SkillCreator.ai

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: plurigrid
Original project: https://github.com/plurigrid/asi/blob/293ce9f76e3abcd5ba18db284688bc0d9ac6691d/plugins/asi/skills/mujoco-scenes/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Development & Engineering
Roles: Data Scientist, ML / AI Engineer
Specialties: API
Tools: Ksim
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 3bba6b587a309aa11d5693f3852596b90dc601ff07724d1675cffc7a3ec7799e