Which RL algorithms are included in KSIM-RL?

The library provides high-performance implementations of Proximal Policy Optimization (PPO) and Adversarial Motion Priors (AMP).

Does it support hardware acceleration?

Yes, it leverages JAX and MuJoCo's MJX engine to enable massive parallelization of environment simulations on GPU and TPU hardware.

Can I define custom robot models in KSIM-RL?

Yes, it uses MJCF (MuJoCo XML) formats and provides abstract task classes to easily integrate custom robot URDFs and joint configurations.

What is KSIM-RL used for?

KSIM-RL is a library specifically designed for training reinforcement learning policies for humanoid robots, focusing on locomotion, gait, and manipulation tasks.

How does KSIM-RL help with sim-to-real transfer?

It includes built-in domain randomization tools and modular reward structures that help bridge the gap between virtual simulations and physical robot performance.

KSIM Humanoid RL Training

Name: KSIM Humanoid RL Training
Author: plurigrid

byplurigrid

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Data Science & ML

Trains humanoid locomotion and manipulation policies using JAX-accelerated MuJoCo simulations and advanced RL algorithms.

About

KSIM-RL is a specialized reinforcement learning library developed by K-Scale Labs for humanoid robotics. Built on the MuJoCo physics engine and JAX, it facilitates high-throughput training of complex behaviors like bipedal walking and manipulation. The library includes production-ready implementations of PPO and AMP, a modular system for defining rewards and observations, and built-in domain randomization tools specifically designed to simplify the sim-to-real transfer process for physical robot hardware.

Key Features

Integrated domain randomization for robust sim-to-real transfer
Modular reward and observation system for custom tasks
Adversarial Motion Priors (AMP) for natural humanoid movement
JAX-accelerated MJX simulation for high-throughput training
Native support for Proximal Policy Optimization (PPO)
2 GitHub stars

Use Cases

Developing natural humanoid walking and running gaits
Optimizing control policies for real-world hardware deployment
Simulating complex robotic manipulation tasks in MuJoCo

About

Key Features

Integrated domain randomization for robust sim-to-real transfer
Modular reward and observation system for custom tasks
Adversarial Motion Priors (AMP) for natural humanoid movement
JAX-accelerated MJX simulation for high-throughput training
Native support for Proximal Policy Optimization (PPO)
2 GitHub stars

Use Cases

Developing natural humanoid walking and running gaits
Optimizing control policies for real-world hardware deployment
Simulating complex robotic manipulation tasks in MuJoCo