mjlab: A Lightweight Framework for GPU-Accelerated Robot Learning

2026-01-29

AI Summary

This work proposes a lightweight, open-source framework to address the challenges in robot learning associated with complex simulation deployment, heavy dependencies, and inefficient GPU acceleration. The framework uniquely integrates Isaac Lab’s manager-style API with the GPU-accelerated MuJoCo Warp physics engine, enabling modular composition of observation, reward, and event logic. It features one-command installation, minimal dependencies, and direct access to native MuJoCo data structures, significantly lowering the barrier to experimental setup. Reference implementations for velocity tracking, motion imitation, and manipulation tasks are provided, demonstrating improved simulation efficiency and ease of use.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Low Level & Physics-based Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

We present mjlab, a lightweight, open-source framework for robot learning that combines GPU-accelerated simulation with composable environments and minimal setup friction. mjlab adopts the manager-based API introduced by Isaac Lab, where users compose modular building blocks for observations, rewards, and events, and pairs it with MuJoCo Warp for GPU-accelerated physics. The result is a framework installable with a single command, requiring minimal dependencies, and providing direct access to native MuJoCo data structures. mjlab ships with reference implementations of velocity tracking, motion imitation, and manipulation tasks.

Problem

• robot learning
• GPU-accelerated simulation
• lightweight framework
• composable environments
• physics simulation

Innovation

• GPU-accelerated simulation
• modular robot learning
• MuJoCo Warp
• lightweight framework
• composable environments