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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
OpenAI Gym environments for an open-source quadruped robot (SpotMicro)
| Date | Stars |
|---|---|
| 2026-07-24 | 1101 |
| 2026-07-25 | 1101 |
| 2026-07-28 | 1101 |
| 2026-07-30 | 1101 |
| 2026-08-06 | 1101 |
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# Rex: an open-source quadruped robot  The goal of this project is to train an open-source 3D printed quadruped robot exploring `Reinforcement Learning` and `OpenAI Gym`. The aim is to let the robot learns domestic and generic tasks in the simulations and then successfully transfer the knowledge (`Control Policies`) on the real robot without any other manual tuning. This project is mostly inspired by the incredible works done by Boston Dynamics. ## Related repositories [rexctl](https://github.com/nicrusso7/rexctl) - A CLI application to bootstrap and control Rex robot running the trained `Control Policies`. [rex-models](https://github.com/nicrusso7/rex-models) - A web URDF visualizer. Collection of Rex robot models. # Rex-gym: OpenAI Gym environments and tools This repository contains a collection of `OpenAI Gym Environments` used to train Rex, the Rex URDF model, the learning agent implementation (PPO) and some scripts to start the training session and visualise the learned `Control Polices`. This CLI application allows batch training, policy reproduction and single training rendered sessions. # Installation Create a `Python 3.7` virtual environment, e.g. using `Anaconda` ``` conda create -n rex python=3.7 anaconda conda activate rex ``` ## PyPI package Install the public `rex-gym` package: ``` pip install rex_gym ``` ## Install from source Clone this repository and run from the root of the project: ``` pip install . ``` # CLI usage Run ``` rex-gym --help ``` to display the available commands and ``` rex-gym COMMAND_NAME --help ``` to show the help message for a specific command. Use the `--arg` flag to eventually set the simulation arguments. For a full list check out the [environments parameters](#environments-parameters). To switch between the `Open Loop` and the `Bezier controller (inverse kinematics)` modes, just append either the `--open-loop` or `--inverse-kinematics` flags. ``` rex-gym COMMAND_NAME -ik rex-gym COMMAND_NAME -ol ``` For more info about the modes check out [the learning approach](#learning-approach). ## Policy player: run a pre-trained agent To start a pre-trained agent (play a learned `Control Policy`): ``` rex-gym policy --env ENV_NAME ``` ## Train: Run a single training simulation To start a single agent rendered session (`agents=1`, `render=True`): ``` rex-gym train --playground True --env ENV_NAME --log-dir LOG_DIR_PATH ``` ## Train: Start a new batch training simulation To start a new batch training session: ``` rex-gym train --env ENV_NAME --log-dir LOG_DIR_PATH ``` # Robot platform ## Mark 1 The robot used for this first version is the [Spotmicro](https://www.thingiverse.com/thing:3445283) made by [Deok-yeon Kim](https://www.thingiverse.com/KDY0523/about). I've printed the components using a Creality Ender3 3D printer, with PLA and TPU+.  The hardware used is listed in this [wiki](https://github.com/nicrusso7/rexctl/wiki/Mark-I). The idea is to extend the robot adding components like a robotic arm on the top of the rack and a LiDAR sensor in the next versions alongside fixing some design issue to support a better (and easier) calibration and more reliable servo motors. # Simulation model ## Base model Rex is a 12 joints robot with 3 motors (`Shoulder`, `Leg` and `Foot`) for each leg. The robot `base` model is imported in `pyBullet` using an [URDF file](rex_gym/util/pybullet_data/assets/urdf/rex.urdf). The servo motors are modelled in the `model/motor.py` class.  ## Robotic arm The `arm` model has the open source 6DOF robotic arm [Poppy Ergo Jr](https://github.com/poppy-project/poppy-ergo-jr) equipped on the top of the rack.  To switch between `base` and `arm` models use the `--mark` flag. # Learning approach This library uses the `Proximal P
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matched fp:780e7e002a52c0b4, topic:reinforcement-learning, topic:openai-gym, readme:reinforcement learning
matched fp:780e7e002a52c0b4, topic:tensorflow
matched fp:780e7e002a52c0b4, topic:robotics