KSIM-RL Skill

Trit: -1 (MINUS - analysis/verification) Color: #3A2F9E (Deep Purple) URI: skill://ksim-rl#3A2F9E

Overview

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.

Core Architecture

┌─────────────────────────────────────────────────────────────────┐
│                        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        │ │
│  └─────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────┘

Key Features

• JAX-Accelerated: Uses MJX for parallel environment simulation on GPU/TPU
• PPO Training: Proximal Policy Optimization with configurable hyperparameters
• AMP Support: Adversarial Motion Priors for realistic humanoid locomotion
• Modular Rewards: Composable reward functions for gait, velocity, energy
• Domain Randomization: Built-in randomizers for sim-to-real transfer

API Usage

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,
)

GF(3) Triads

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

Key Contributors

• codekansas (Ben Bolte): Core architecture, PPO, rewards
• b-vm: Randomizers, disturbances, policy training
• carlosdp: Adaptive KL, action scaling
• WT-MM: Visualization, markers

Related Skills

• kos-firmware

  (+1): Robot firmware and gRPC services

• mujoco-scenes

  (0): Scene composition for MuJoCo

• evla-vla

  (-1): Vision-language-action models

• urdf2mjcf

  (-1): URDF to MJCF conversion

• ktune-sim2real

  (-1): Servo tuning for sim2real

References

@misc{ksim2024,
  title={K-Sim: RL Training for Humanoid Locomotion},
  author={K-Scale Labs},
  year={2024},
  url={https://github.com/kscalelabs/ksim}
}

SDF Interleaving

This skill connects to Software Design for Flexibility (Hanson & Sussman, 2021):

Primary Chapter: 5. Evaluation

Concepts: eval, apply, interpreter, environment

GF(3) Balanced Triad

ksim-rl (○) + SDF.Ch5 (−) + [balancer] (+) = 0

Skill Trit: 0 (ERGODIC - coordination)

Secondary Chapters

• Ch2: Domain-Specific Languages

Connection Pattern

Evaluation interprets expressions. This skill processes or generates evaluable forms.