---
title: "MuJoCo"
description: "2 posts about MuJoCo, summarised, each linking to the original."
---

# MuJoCo
> 2 posts about MuJoCo, summarised, each linking to the original.

## Articles

### [Gemini 4 Argon for Robotics](https://yomu.fyi/post/gemini-4-argon-for-robotics.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Kanishka Rao
- Published: Oct 13, 2026
- Type: Announcement

Gemini 4 Argon is a new model that achieves a step change in physical understanding, code generation, and visual-semantic reasoning for robotics. It scores 87% on the Embodied Reasoning (ERQA) benchmark using pure reasoning, outperforming other frontier models. The model can be applied across the entire robotics lifecycle, including robot control, Real2Sim reconstruction, physical understanding, and research self-improvement. Examples include controlling a Spot quadruped to practice foot-pedal trash bin disposal in MuJoCo before executing, zero-shot control of a robotic arm, steering a video world model to rearrange Scrabble tiles, reconstructing simulation twins from robot camera feeds or hand-drawn sketches, and reproducing robotics papers as self-contained MuJoCo WebAssembly browser labs with a single prompt. Argon also performs agentic multimodal hindsight labeling for dexterity understanding and dense value functions, and can self-improve by diagnosing failures and optimizing speed, bridging coding, visual perception, physics simulation, and embodied control toward physical AGI.


### [How to Use NVIDIA Warp and MjWarp to Accelerate Robotics Simulation and Learning Workflows](https://yomu.fyi/post/how-to-use-nvidia-warp-and-mjwarp-to-accelerate-robotics-simulation-an.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Johnny Nuñez Cano, Asier Arranz, Rishabh Chadha, Ben Oliveri
- Published: Sep 23, 2026
- Type: Tutorial

NVIDIA Warp is a Python framework for writing GPU-accelerated kernels that compiles statically typed Python to CUDA. MuJoCo Warp (MJWarp) implements MuJoCo physics on Warp, enabling batched simulation of hundreds to thousands of independent robot environments on NVIDIA GPUs. The core API transition is small: mjw.put\_model and mjw.put\_data replace mujoco.MjModel and mujoco.MjData, and mjw.step advances the entire batch. Proper throughput measurement requires GPU synchronization and warm-up to avoid timing queue latency instead of actual GPU work. The post demonstrates moving an SO-101 follower arm from a single CPU world to 2,048 parallel MJWarp environments, validating parity and measuring aggregate world-steps per second.