tomevault-io K-Scale Robotics Skill Collection
Trit: 0 (ERGODIC - coordination/infrastructure) Color: #5B8DEE (Sky Blue) URI: skill://kscale#5B8DEE
Overview
This skill indexes the K-Scale Labs robotics ecosystem - a comprehensive open-source stack for building, training, and deploying humanoid robots. The collection follows GF(3) triadic organization with kos-firmware (+1) as the primary generator and mujoco-scenes (0) as the coordinator.
Skill Inventory
┌────────────────────────────────────────────────────────────────────┐
│ K-SCALE SKILL ECOSYSTEM │
├────────────────────────────────────────────────────────────────────┤
│ │
│ PLUS (+1) - Generation/Construction │
│ ┌─────────────────────────────────────────────────────────────┐ │
│ │ kos-firmware #79ED91 Robot firmware & gRPC services │ │
│ └─────────────────────────────────────────────────────────────┘ │
│ │
│ ERGODIC (0) - Coordination/Infrastructure │
│ ┌─────────────────────────────────────────────────────────────┐ │
│ │ mujoco-scenes #9FD875 Scene composition for MuJoCo │ │
│ └─────────────────────────────────────────────────────────────┘ │
│ │
│ MINUS (-1) - Analysis/Verification │
│ ┌─────────────────────────────────────────────────────────────┐ │
│ │ ksim-rl #3A2F9E RL training for locomotion │ │
│ │ evla-vla #DBA51D Vision-language-action model │ │
│ │ urdf2mjcf #4615B7 URDF to MJCF conversion │ │
│ │ kbot-humanoid #5B45C2 K-Bot robot specifications │ │
│ │ zeroth-bot #8CC136 3D-printed humanoid platform │ │
│ │ kscale-actuator #B9172E Robstride motor control │ │
│ │ entropy-sim2real #E85B8E Entropy-driven sim2real │ │
│ └─────────────────────────────────────────────────────────────┘ │
│ │
└────────────────────────────────────────────────────────────────────┘
GF(3) Balance Analysis
Current State: UNBALANCED (trit sum = -6)
| Trit | Count | Skills |
|---|---|---|
| +1 (PLUS) | 1 | kos-firmware |
| 0 (ERGODIC) | 1 | mujoco-scenes |
| -1 (MINUS) | 7 | ksim-rl, evla-vla, urdf2mjcf, kbot-humanoid, zeroth-bot, kscale-actuator, entropy-sim2real |
Balanced Triads
The primary balanced triad anchors the ecosystem:
ksim-rl (-1) ⊗ kos-firmware (+1) ⊗ mujoco-scenes (0) = 0 ✓
Pattern: Each MINUS skill can form a balanced triad by reusing the (kos-firmware, mujoco-scenes) pair:
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 ✓
kscale-actuator (-1) ⊗ kos-firmware (+1) ⊗ mujoco-scenes (0) = 0 ✓
entropy-sim2real (-1) ⊗ kos-firmware (+1) ⊗ mujoco-scenes (0) = 0 ✓
Recommendations for Balance
To achieve independent balanced triads (no skill reuse), add PLUS (+1) skills:
- kscale-deploy (+1): Deployment automation, OTA updates
- kscale-onboard (+1): On-robot compute orchestration
- kscale-teleop (+1): Teleoperation and remote control
- kinfer-runtime (+1): On-device inference runtime
Architecture
┌─────────────────────┐
│ TRAINING │
│ ┌───────────────┐ │
│ │ ksim-rl │ │
│ │ (PPO, AMP) │ │
│ └───────────────┘ │
│ │ │
│ ▼ │
│ ┌───────────────┐ │
│ │ mujoco-scenes │ │
│ │ (environments)│ │
│ └───────────────┘ │
└─────────┬───────────┘
│
┌──────────────────────┼──────────────────────┐
│ │ │
▼ ▼ ▼
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ MODELS │ │ PERCEPTION │ │ TRANSFER │
│ ┌─────────────┐ │ │ ┌─────────────┐ │ │ ┌─────────────┐ │
│ │ urdf2mjcf │ │ │ │ evla-vla │ │ │ │entropy-sim2r│ │
│ │ kbot-humanoi│ │ │ │ (VLA) │ │ │ │ (domain │ │
│ │ zeroth-bot │ │ │ └─────────────┘ │ │ │ random.) │ │
│ └─────────────┘ │ └─────────────────┘ │ └─────────────┘ │
└─────────────────┘ └─────────────────┘
│ │
└──────────────────────┬──────────────────────┘
│
▼
┌─────────────────────┐
│ DEPLOYMENT │
│ ┌───────────────┐ │
│ │ kos-firmware │ │
│ │ (+1) │ │
│ └───────────────┘ │
│ │ │
│ ▼ │
│ ┌───────────────┐ │
│ │kscale-actuator│ │
│ │ (CAN motors) │ │
│ └───────────────┘ │
└─────────────────────┘
Usage
Training Pipeline
# Full K-Scale training pipeline
from ksim import PPOTask
from ksim.robots.kbot import KBotConfig
from mujoco_scenes import SceneBuilder, Terrain
class KBotWalkingTask(PPOTask):
robot = KBotConfig(model_path="kbot.mjcf")
def build_scene(self):
scene = SceneBuilder()
scene.add_terrain(Terrain.FLAT)
scene.add_random_obstacles(count=5)
return scene.to_mjcf()
# Entropy-driven domain randomization
physics_randomizers = [
StaticFrictionRandomizer(scale=0.5),
MassMultiplicationRandomizer(scale=0.2),
]
# Train
task = KBotWalkingTask()
task.run_training(num_envs=4096)
Deployment Pipeline
from pykos import KosClient
async def deploy_policy():
async with KosClient("kbot.local:50051") as client:
# Load trained policy
await client.policy.load("walking_v1.onnx")
# Start control loop
await client.policy.start()
# Monitor
while True:
state = await client.actuator.get_actuators_state()
print(f"Position: {state.positions}")
Key Contributors
- codekansas (Ben Bolte): Core architecture across ksim, kos
- budzianowski: EdgeVLA, dataset standardization
- nfreq: KOS PolicyService, calibration
- WT-MM: Visualization, integration
- b-vm: Randomizers, disturbances
Repository Links
| Skill | Repository |
|---|---|
| ksim-rl | kscalelabs/ksim |
| kos-firmware | kscalelabs/kos |
| evla-vla | kscalelabs/evla |
| urdf2mjcf | kscalelabs/urdf2mjcf |
| kbot-humanoid | kscalelabs/kbot |
| zeroth-bot | kscalelabs/zeroth-bot |
| mujoco-scenes | kscalelabs/mujoco-scenes |
| kscale-actuator | kscalelabs/actuator |
Related Skills
jaxlife-open-ended(+1): Open-ended evolution for behavior discoveryergodicity(0): Ergodic theory foundationswobble-dynamics(0): Perturbation response analysisstability(-1): Dynamical system stability analysis
References
@misc{kscale2024,
title={K-Scale Labs Open Source Robotics Stack},
author={K-Scale Labs},
year={2024},
url={https://github.com/kscalelabs}
}
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