--- name: ksim-rl-a55466fbf814 description: 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. version: 1.0.0 category: robotics-rl author: K-Scale Labs source: kscalelabs/ksim license: MIT trit: -1 trit_label: MINUS color: "#3A2F9E" verified: false featured: true --- # 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} } ``` --- Source attribution: plurigrid Original project: https://github.com/plurigrid/asi/blob/293ce9f76e3abcd5ba18db284688bc0d9ac6691d/plugins/asi/skills/ksim-rl/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2025 SkillCreator.ai Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: plurigrid Original project: https://github.com/plurigrid/asi/blob/293ce9f76e3abcd5ba18db284688bc0d9ac6691d/plugins/asi/skills/ksim-rl/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering, Education & Research, Product Management Roles: Backend Developer, Data Scientist, ML / AI Engineer, Product Manager, Researcher Specialties: API, Backend Tools: Jax, Mujoco Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 2595039f01110be282492cba822e80d2567dc6b63b5204a34094d0d60b393433