ksim-rl
RL training library for humanoid locomotion and manipulation built on MuJoCo and JAX. Provides PPO, AMP, and custom task abstractions for sim-to-real robotics policy training.
16 stars
Best use case
ksim-rl is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
RL training library for humanoid locomotion and manipulation built on MuJoCo and JAX. Provides PPO, AMP, and custom task abstractions for sim-to-real robotics policy training.
Teams using ksim-rl should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
$curl -o ~/.claude/skills/ksim-rl/SKILL.md --create-dirs "https://raw.githubusercontent.com/plurigrid/asi/main/plugins/asi/skills/ksim-rl/SKILL.md"
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/ksim-rl/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ksim-rl Compares
| Feature / Agent | ksim-rl | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
RL training library for humanoid locomotion and manipulation built on MuJoCo and JAX. Provides PPO, AMP, and custom task abstractions for sim-to-real robotics policy training.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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
```python
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
```bibtex
@misc{ksim2024,
title={K-Sim: RL Training for Humanoid Locomotion},
author={K-Scale Labs},
year={2024},
url={https://github.com/kscalelabs/ksim}
}
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