Embodied AI on Producthunt daily

XLeRobot

Wed, 10 Sep 2025 15:27:30 +0800

Vector-Wangel/XLeRobot

XLeRobot 🤖

🚀 Bringing Embodied AI to Everyone - Cheaper Than an iPhone! 📱
💵 Starts from $660 cost and ⏰ <4hrs total assembly time!!

Built upon the giants: LeRobot, SO-100/SO-101, Lekiwi, Bambot

📰 News

2025-09-09: Developer Assembly kit ready for purchase in China. World-wide purchase link out in 2 days.
- Non-profit, only for more convenient accessiblity. I personally don’t earn any from this.
2025-09-09: Joined Embodied AI Home Robot Hackathon (Oct 25–26, Bay Area) held by SEEED x Nvidia x Huggingface as mentor! Register HERE.
2025-08-30: XLeRobot 0.3.0 Release with final outfit touch up and household chores showcase demos.
2025-07-30: Control XLeRobot in real life with keyboard/Xbox controller/Switch joycon in the wild anywhere. All bluetooth, no wifi needed and zero latency.
2025-07-08: Simulation with updated urdfs, control scripts (support Quest3 VR, keyboard, Xbox controller, switch joycon), support for new hardware and cameras, RL environment. Get started in 15 min.
2025-07-01: Documentation website out for more orgainized tutorials, demos and resources.
2025-06-13: XLeRobot 0.2.0 hardware setup, the 1st version fully capable for autonomous household tasks, starts from 660$.

💵 Total Cost 💵

[!NOTE] Cost excludes 3D printing, tools, shipping, and taxes.

Price (Buy all the parts yourself)	US	EU	CN
Basic (use your laptop, single RGB head cam)	~$660	~€680	~¥3999
↑ Stereo dual-eye RGB head cam	+$30	+€30	+¥199
+ RasberryPi	+$79	+€79	+¥399
↑ RealSense RGBD head cam	+$220	+€230	+¥1499

🚀 Get Started 🚀

[!NOTE] If you are totally new to programming, please spend at least a day to get yourself familiar with basic Python, Ubuntu and Github (with the help of Google and AI). At least you should know how to setup ubuntu system, git clone, pip install, use intepreters (VS Code, Cursor, Pycharm, etc.) and directly run commands in the terminals.

💵 Buy your parts: Bill of Materials
🖨️ Print your stuff: 3D printing
🔨 ~~Avengers~~: Assemble!
💻 Software: Get your robot moving!

Contribute

👋 Want to contribute to XLeRobot? Please refer to CONTRIBUTING.md for guidance on how to get involved!

Main Contributors

Gaotian/Vector Wang
Zhuoyi Lu: RL sim2real deploy, teleop on real robot (Xbox, VR, Joycon)
Nicole Yue: Documentation website setup
Yuesong Wang: Mujoco simulation

This is just a small brick in the pyramid, made possible by LeRobot, SO-100, Lekiwi, and Bambot. Thanks to all the talented contributors behind these detailed and professional projects.

Looking forward to collaborating with anyone interested in contributing to this project!

About me

Gaotian/Vector Wang

I am a CS graduate student at Rice University RobotPi Lab, focusing on robust object manipulation, where we propse virtual cages and funnels and physics-aware world models to close the Sim2real gap and achieve robust manipulation under uncertainties. One of my papers, Caging in Time, has recently been accepted by International Journal of Robotics Research (IJRR).

I built XLeRobot as a personal hobby to instantiate my research theory, also to provide a low-cost platform for people who are interested in robotics and embodied AI to work with.

Citation

If you want, you can cite this work with:

@misc{wang2025xlerobot,
    author = {Wang, Gaotian and Lu, Zhuoyi},
    title = {XLeRobot: A Practical Low-cost Household Dual-Arm Mobile Robot Design for General Manipulation},
    howpublished = "\url{https://github.com/Vector-Wangel/XLeRobot}",
    year = {2025}
}

—

🪧 Disclaimer 🪧

[!NOTE] If you build, buy, or develop a XLeRobot based on this repo, you will be fully responsible for all the physical and mental damages it does to you or others.

Genesis

Tue, 29 Jul 2025 15:36:08 +0800

Genesis-Embodied-AI/Genesis

[ [ [ [ [

Genesis

🔥 News

[2025-07-02] The development of Genesis is now officially supported by Genesis AI.
[2025-01-09] We released a detailed performance benchmarking and comparison report on Genesis, together with all the test scripts.
[2025-01-08] Released v0.2.1 🎊 🎉
[2025-01-08] Created Discord and Wechat group.
[2024-12-25] Added a docker including support for the ray-tracing renderer
[2024-12-24] Added guidelines for contributing to Genesis

What is Genesis?

Genesis is a physics platform designed for general-purpose Robotics/Embodied AI/Physical AI applications. It is simultaneously multiple things:

A universal physics engine re-built from the ground up, capable of simulating a wide range of materials and physical phenomena.
A lightweight, ultra-fast, pythonic, and user-friendly robotics simulation platform.
A powerful and fast photo-realistic rendering system.
A generative data engine that transforms user-prompted natural language description into various modalities of data.

Powered by a universal physics engine re-designed and re-built from the ground up, Genesis integrates various physics solvers and their coupling into a unified framework. This core physics engine is further enhanced by a generative agent framework that operates at an upper level, aiming towards fully automated data generation for robotics and beyond.

Note: Currently, we are open-sourcing the underlying physics engine and the simulation platform. Our generative framework is a modular system that incorporates many different generative modules, each handling a certain range of data modalities, routed by a high level agent. Some of the modules integrated existing papers and some are still under submission. Access to our generative feature will be gradually rolled out in the near future. If you are interested, feel free to explore more in the paper list below.

Genesis aims to:

Lower the barrier to using physics simulations, making robotics research accessible to everyone. See our mission statement.
Unify diverse physics solvers into a single framework to recreate the physical world with the highest fidelity.
Automate data generation, reducing human effort and letting the data flywheel spin on its own.

Project Page: https://genesis-embodied-ai.github.io/

Key Features

Speed: Over 43 million FPS when simulating a Franka robotic arm with a single RTX 4090 (430,000 times faster than real-time).
Cross-platform: Runs on Linux, macOS, Windows, and supports multiple compute backends (CPU, Nvidia/AMD GPUs, Apple Metal).
Integration of diverse physics solvers: Rigid body, MPM, SPH, FEM, PBD, Stable Fluid.
Wide range of material models: Simulation and coupling of rigid bodies, liquids, gases, deformable objects, thin-shell objects, and granular materials.
Compatibility with various robots: Robotic arms, legged robots, drones, soft robots, and support for loading MJCF (.xml), URDF, .obj, .glb, .ply, .stl, and more.
Photo-realistic rendering: Native ray-tracing-based rendering.
Differentiability: Genesis is designed to be fully differentiable. Currently, our MPM solver and Tool Solver support differentiability, with other solvers planned for future versions (starting with rigid & articulated body solver).
Physics-based tactile simulation: Differentiable tactile sensor simulation coming soon (expected in version 0.3.0).
User-friendliness: Designed for simplicity, with intuitive installation and APIs.

Quick Installation

Install PyTorch first following the official instructions.

Then, install Genesis via PyPI:

`1`	`pip install genesis-world # Requires Python>=3.10,<3.13;`

For the latest version to date, make sure that pip is up-to-date via pip install --upgrade pip, then run command:

`1`	`pip install git+https://github.com/Genesis-Embodied-AI/Genesis.git`

Note that the package must still be updated manually to sync with main branch.

Users seeking to edit the source code of Genesis are encourage to install Genesis in editable mode. First, make sure that genesis-world has been uninstalled, then clone the repository and install locally:

1
2
3

git clone https://github.com/Genesis-Embodied-AI/Genesis.git
cd Genesis
pip install -e ".[dev]"

Docker

If you want to use Genesis from Docker, you can first build the Docker image as:

`1`	`docker build -t genesis -f docker/Dockerfile docker`

Then you can run the examples inside the docker image (mounted to /workspace/examples):

xhost +local:root # Allow the container to access the display

docker run --gpus all --rm -it \
-e DISPLAY=$DISPLAY \
-v /dev/dri:/dev/dri \
-v /tmp/.X11-unix/:/tmp/.X11-unix \
-v $PWD:/workspace \
genesis

AMD users

AMD users can use Genesis using the docker/Dockerfile.amdgpu file, which is built by running:

`1`	`docker build -t genesis-amd -f docker/Dockerfile.amdgpu docker`

and can then be used by running:

docker run -it --network=host \
 --device=/dev/kfd \
 --device=/dev/dri \
 --group-add=video \
 --ipc=host \
 --cap-add=SYS_PTRACE \
 --security-opt seccomp=unconfined \
 --shm-size 8G \
 -v $PWD:/workspace \
 -e DISPLAY=$DISPLAY \
 genesis-amd

The examples will be accessible from /workspace/examples. Note: AMD users should use the vulkan backend. This means you will need to call gs.init(vulkan) to initialise Genesis.

Documentation

Comprehensive documentation is available in English, Chinese, and Japanese. This includes detailed installation steps, tutorials, and API references.

Contributing to Genesis

The Genesis project is an open and collaborative effort. We welcome all forms of contributions from the community, including:

Pull requests for new features or bug fixes.
Bug reports through GitHub Issues.
Suggestions to improve Genesis’s usability.

Refer to our contribution guide for more details.

Support

Report bugs or request features via GitHub Issues.
Join discussions or ask questions on GitHub Discussions.

License and Acknowledgments

The Genesis source code is licensed under Apache 2.0.

Genesis’s development has been made possible thanks to these open-source projects:

Taichi: High-performance cross-platform compute backend. Kudos to the Taichi team for their technical support!
FluidLab: Reference MPM solver implementation.
SPH_Taichi: Reference SPH solver implementation.
Ten Minute Physics and PBF3D: Reference PBD solver implementations.
MuJoCo: Reference for rigid body dynamics.
libccd: Reference for collision detection.
PyRender: Rasterization-based renderer.
LuisaCompute and LuisaRender: Ray-tracing DSL.

Associated Papers

Genesis is a large scale effort that integrates state-of-the-art technologies of various existing and on-going research work into a single system. Here we include a non-exhaustive list of all the papers that contributed to the Genesis project in one way or another:

Xian, Zhou, et al. “Fluidlab: A differentiable environment for benchmarking complex fluid manipulation.” arXiv preprint arXiv:2303.02346 (2023).
Xu, Zhenjia, et al. “Roboninja: Learning an adaptive cutting policy for multi-material objects.” arXiv preprint arXiv:2302.11553 (2023).
Wang, Yufei, et al. “Robogen: Towards unleashing infinite data for automated robot learning via generative simulation.” arXiv preprint arXiv:2311.01455 (2023).
Wang, Tsun-Hsuan, et al. “Softzoo: A soft robot co-design benchmark for locomotion in diverse environments.” arXiv preprint arXiv:2303.09555 (2023).
Wang, Tsun-Hsuan Johnson, et al. “Diffusebot: Breeding soft robots with physics-augmented generative diffusion models.” Advances in Neural Information Processing Systems 36 (2023): 44398-44423.
Katara, Pushkal, Zhou Xian, and Katerina Fragkiadaki. “Gen2sim: Scaling up robot learning in simulation with generative models.” 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024.
Si, Zilin, et al. “DiffTactile: A Physics-based Differentiable Tactile Simulator for Contact-rich Robotic Manipulation.” arXiv preprint arXiv:2403.08716 (2024).
Wang, Yian, et al. “Thin-Shell Object Manipulations With Differentiable Physics Simulations.” arXiv preprint arXiv:2404.00451 (2024).
Lin, Chunru, et al. “UBSoft: A Simulation Platform for Robotic Skill Learning in Unbounded Soft Environments.” arXiv preprint arXiv:2411.12711 (2024).
Zhou, Wenyang, et al. “EMDM: Efficient motion diffusion model for fast and high-quality motion generation.” European Conference on Computer Vision. Springer, Cham, 2025.
Qiao, Yi-Ling, Junbang Liang, Vladlen Koltun, and Ming C. Lin. “Scalable differentiable physics for learning and control.” International Conference on Machine Learning. PMLR, 2020.
Qiao, Yi-Ling, Junbang Liang, Vladlen Koltun, and Ming C. Lin. “Efficient differentiable simulation of articulated bodies.” In International Conference on Machine Learning, PMLR, 2021.
Qiao, Yi-Ling, Junbang Liang, Vladlen Koltun, and Ming Lin. “Differentiable simulation of soft multi-body systems.” Advances in Neural Information Processing Systems 34 (2021).
Wan, Weilin, et al. “Tlcontrol: Trajectory and language control for human motion synthesis.” arXiv preprint arXiv:2311.17135 (2023).
Wang, Yian, et al. “Architect: Generating Vivid and Interactive 3D Scenes with Hierarchical 2D Inpainting.” arXiv preprint arXiv:2411.09823 (2024).
Zheng, Shaokun, et al. “LuisaRender: A high-performance rendering framework with layered and unified interfaces on stream architectures.” ACM Transactions on Graphics (TOG) 41.6 (2022): 1-19.
Fan, Yingruo, et al. “Faceformer: Speech-driven 3d facial animation with transformers.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022.
Wu, Sichun, Kazi Injamamul Haque, and Zerrin Yumak. “ProbTalk3D: Non-Deterministic Emotion Controllable Speech-Driven 3D Facial Animation Synthesis Using VQ-VAE.” Proceedings of the 17th ACM SIGGRAPH Conference on Motion, Interaction, and Games. 2024.
Dou, Zhiyang, et al. “C· ase: Learning conditional adversarial skill embeddings for physics-based characters.” SIGGRAPH Asia 2023 Conference Papers. 2023.

… and many more on-going work.

Citation

If you use Genesis in your research, please consider citing:

@misc{Genesis,
  author = {Genesis Authors},
  title = {Genesis: A Generative and Universal Physics Engine for Robotics and Beyond},
  month = {December},
  year = {2024},
  url = {https://github.com/Genesis-Embodied-AI/Genesis}
}

Embodied AI on Producthunt daily

XLeRobot

Vector-Wangel/XLeRobot

XLeRobot 🤖

📰 News

💵 Total Cost 💵

🚀 Get Started 🚀

Contribute

About me

Citation

🪧 Disclaimer 🪧

Genesis

Genesis-Embodied-AI/Genesis

Genesis

🔥 News

Table of Contents

What is Genesis?

Key Features

Quick Installation

Docker

AMD users

Documentation

Contributing to Genesis

Support

License and Acknowledgments

Associated Papers

Citation