# mjlab: A Lightweight Framework for GPU-Accelerated Robot Learning

arXiv: https://arxiv.org/abs/2601.22074 · HTML: https://mlsanity.com/paper/2601.22074/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2026.

## Built on by (most cited first, 26 of 26)

- [SceneBot: Contact-Prompted General Humanoid Whole Body Tracking with Scene-Interaction](https://mlsanity.com/paper/2606.27581/) (2026)
- [Accelerating and Scaling MPC-Guided Reinforcement Learning for Humanoid Locomotion and Manipulation](https://mlsanity.com/paper/2606.05687/) (2026)
- [HARBOR: A Harness Framework for Agentic Robot Reinforcement Learning](https://mlsanity.com/paper/2606.08610/) (2026)
- [PAC-MAN: Perception-Aware CBF-RL for Whole-Body Safety in Humanoid Dodgeball](https://mlsanity.com/paper/2607.28623/) (2026)
- [HANDOFF: Humanoid Agentic Task-Space Whole-Body Control via Distilled Complementary Teachers](https://mlsanity.com/paper/2606.06493/) (2026)
- [Articulated-Body Dynamics Network: Dynamics-Grounded Prior for Robot Learning](https://mlsanity.com/paper/2603.19078/) (2026)
- [MARCH: Model-Assisted Reinforcement Learning for the Perceptive Control of Humanoids over Sparse Footholds](https://mlsanity.com/paper/2606.10288/) (2026)
- [ContactExplorer: Contact Coverage-Guided Exploration for General-Purpose Dexterous Manipulation](https://mlsanity.com/paper/2603.10971/) (2026)
- [Web-Gewu: A Browser-Based Interactive Playground for Robot Reinforcement Learning](https://mlsanity.com/paper/2604.17050/) (2026)
- [Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration](https://mlsanity.com/paper/2605.12084/) (2026)
- [Predictive Style Matching: Natural and Robust Humanoid Locomotion](https://mlsanity.com/paper/2606.07083/) (2026)
- [TaskNPoint: How to Teach Your Humanoid to Hit a Backhand in Minutes](https://mlsanity.com/paper/2606.26215/) (2026)
- [Learning Reusable Hybrid Motion Priors for Humanoid Locomotion from Motion Imitation](https://mlsanity.com/paper/2607.24083/) (2026)
- [Shooting for Contact: Contact-Implicit Multiple Shooting for Dynamic Motion Retargeting](https://mlsanity.com/paper/2608.03116/) (2026)
- [Learning Loco-Manipulation From SMPC Demonstrations With Sparse Offline-to-Online RL](https://mlsanity.com/paper/2608.12063/) (2026)
- [Towards Professional Tennis Styles for Humanoid Robots with Adaptive Motion Planning and Tracking](https://mlsanity.com/paper/2608.20087/) (2026)
- [UniExo: Unified Multi-Skill Policies for Musculoskeletal Locomotion and Co-Adaptive Exoskeleton Control](https://mlsanity.com/paper/2609.19690/) (2026)
- [ForgetMimic: Motion Unlearning for Reinforcement Learning Humanoid Control](https://mlsanity.com/paper/2609.28378/) (2026)
- [Grasp2Twist: Learning Bimanual Dexterous Jar Opening by Reinforcement Learning](https://mlsanity.com/paper/2609.32064/) (2026)
- [InterEvolve: Test-Time Evolution of Reward Programs for Humanoid Loco-Manipulation](https://mlsanity.com/paper/2610.02196/) (2026)
- [Humanoid Rickshaw Pulling: Whole-Body Locomotion under Coupled Wheeled Loads](https://mlsanity.com/paper/2610.04238/) (2026)
- [CoDance: Learning Reactive and Compliant Human-Humanoid Interaction from Video](https://mlsanity.com/paper/2610.05324/) (2026)
- [MimicX: Policy-in-the-Loop Supervision Refinement for Video-Driven Humanoid Motion Tracking](https://mlsanity.com/paper/2610.09055/) (2026)
- [Workhorse: Learning Robust Whole-Body Humanoid Loco-Manipulation from Human Data](https://mlsanity.com/paper/2610.09117/) (2026)
- [AlphaNIST: Emergent Assembly Skills from Binary Task Rewards](https://mlsanity.com/paper/2610.13354/) (2026)
- [Modeling and Understanding Neural Weights of Humanoid Policies with Diffusion](https://mlsanity.com/paper/2610.20606/) (2026)

## Abstract (arXiv)

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.