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Package for creating different scenes in MuJoCo. Compose environments with objects, terrains, and obstacles for robot training.
---
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}
}
```
---
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}
}
```
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.