npx claudepluginhub plurigrid/asi --plugin asiThis skill uses the workspace's default tool permissions.
**Trit**: -1 (MINUS - analysis/verification)
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.
Guides training RL agents with Stable Baselines3 algorithms (PPO, SAC, DQN, TD3, A2C) using Gymnasium environments, custom env creation, callbacks, and optimization.
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: -1 (MINUS - analysis/verification) Color: #3A2F9E (Deep Purple) URI: skill://ksim-rl#3A2F9E
KSIM is K-Scale Labs' reinforcement learning library for humanoid robot locomotion and manipulation. Built on MuJoCo for physics simulation and JAX for hardware-accelerated training.
┌─────────────────────────────────────────────────────────────────┐
│ KSIM ARCHITECTURE │
├─────────────────────────────────────────────────────────────────┤
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────────────────┐ │
│ │ RLTask │ │ PPOTask │ │ AMPTask │ │
│ │ (abstract) │──│ (PPO impl) │──│ (Adversarial Motion) │ │
│ └─────────────┘ └─────────────┘ └─────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────┐ │
│ │ PhysicsEngine │ │
│ │ ┌───────────────┐ ┌───────────────────────────────┐ │ │
│ │ │ MujocoEngine │ │ MjxEngine (JAX-accelerated) │ │ │
│ │ └───────────────┘ └───────────────────────────────┘ │ │
│ └─────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────┐ │
│ │ Environment Components │ │
│ │ • Actuators: Position, Velocity, Torque control │ │
│ │ • Observations: Joint states, IMU, local view │ │
│ │ • Rewards: Velocity tracking, gait, energy, stability │ │
│ │ • Terminations: Fall detection, boundary violations │ │
│ └─────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────┘
import ksim
from ksim import PPOTask, MjxEngine
from ksim.tasks.humanoid import HumanoidWalkingTask
# Define custom task
class KBotWalkingTask(PPOTask):
model_path = "kbot.mjcf"
# Observations
observations = [
ksim.JointPosition(),
ksim.JointVelocity(),
ksim.IMUAngularVelocity(),
ksim.BaseOrientation(),
]
# Rewards
rewards = [
ksim.LinearVelocityReward(scale=1.0),
ksim.GaitPhaseReward(scale=0.5),
ksim.EnergyPenalty(scale=-0.01),
]
# Actuators
actuators = [
ksim.PositionActuator(
joint_name=".*",
kp=100.0,
kd=10.0,
action_scale=0.5,
)
]
# Train
task = KBotWalkingTask()
task.run_training(
num_envs=4096,
num_steps=1000000,
learning_rate=3e-4,
)
This skill participates in balanced triads:
ksim-rl (-1) ⊗ kos-firmware (+1) ⊗ mujoco-scenes (0) = 0 ✓
ksim-rl (-1) ⊗ kos-firmware (+1) ⊗ urdf2mjcf (0) = needs balancing
kos-firmware (+1): Robot firmware and gRPC servicesmujoco-scenes (0): Scene composition for MuJoCoevla-vla (-1): Vision-language-action modelsurdf2mjcf (-1): URDF to MJCF conversionktune-sim2real (-1): Servo tuning for sim2real@misc{ksim2024,
title={K-Sim: RL Training for Humanoid Locomotion},
author={K-Scale Labs},
year={2024},
url={https://github.com/kscalelabs/ksim}
}