npx claudepluginhub plurigrid/asi --plugin asiThis skill uses the workspace's default tool permissions.
**Trit**: 0 (ERGODIC - coordination/infrastructure)
Indexes K-Scale Labs robotics skills for humanoid robot development, RL training, sim-to-real transfer, and deployment. Organizes 9 skills in GF(3) triadic structure.
Generates high-quality 3D human and humanoid robot motions from text prompts using Kimodo kinematic diffusion model with constraints like keyframes and end-effectors. Exports to NPZ, MuJoCo qpos, or AMASS.
Guides training RL agents with Stable Baselines3 (PPO, SAC, DQN, TD3, DDPG, A2C), custom Gym environments, callbacks for monitoring, vectorized envs for parallel training, and deep RL workflows.
Share bugs, ideas, or general feedback.
Trit: 0 (ERGODIC - coordination/infrastructure) Color: #9FD875 (Soft Green) URI: skill://mujoco-scenes#9FD875
Package for composing MuJoCo scenes with objects, terrains, and obstacles. Enables diverse environment generation for robot training.
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 │
├─────────────────────────────────────────────────────────────┤
│ │
│ FLAT ═══════════════════════════ │
│ │
│ STAIRS ┌─┐ │
│ ┌─┘ └─┐ │
│ ┌─┘ └─┐ │
│ │
│ RAMP ╱╲ │
│ ╱ ╲ │
│ │
│ ROUGH ∿∿∿∿∿∿∿∿∿∿∿∿∿∿∿ │
│ (heightfield) │
│ │
│ GAPS ═══ ═══ ═══ ═══ │
│ │
└─────────────────────────────────────────────────────────────┘
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")
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()
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 ✓
ksim-rl (-1): Uses scenes for trainingkos-firmware (+1): Robot firmwareurdf2mjcf (-1): Model conversionkbot-humanoid (-1): K-Bot robot@misc{mujocoscenes2024,
title={MuJoCo Scenes: Environment Composition for Robot Training},
author={K-Scale Labs},
year={2024},
url={https://github.com/kscalelabs/mujoco-scenes}
}