google-deepmind · aajin126 · Mar 30, 2025 · Mar 30, 2025 · Apr 6, 2025 · Apr 7, 2025
diff --git a/.gitignore b/.gitignore
@@ -33,3 +33,4 @@ MJDATA.TXT
 MJMODEL.TXT
 # Python
 python/dist/
+.env
diff --git a/README.md b/README.md
@@ -1,261 +1,12 @@
-<h1>
-  <a href="#"><img alt="MuJoCo MPC" src="docs/assets/banner.png" width="100%"></a>
-</h1>
+### Robot Model
+- In assets/X7S
 
-<p>
-  <a href="https://github.com/google-deepmind/mujoco_mpc/actions/workflows/build.yml?query=branch%3Amain" alt="GitHub Actions">
-    <img src="https://img.shields.io/github/actions/workflow/status/google-deepmind/mujoco_mpc/build.yml?branch=main">
-  </a>
-  <a href="https://github.com/google-deepmind/mujoco_mpc/blob/main/LICENSE" alt="License">
-    <img src="https://img.shields.io/github/license/google-deepmind/mujoco_mpc">
-  </a>
-</p>
+### When writing robot code
+- Wirte in mujoco_mpc/demos (e.g. mujoco_mpc/demos/upload_x7s.py)
 
-**MuJoCo MPC (MJPC)** is an interactive application and software framework for
-real-time predictive control with [MuJoCo](https://mujoco.org/), developed by
-Google DeepMind.
-
-MJPC allows the user to easily author and solve complex robotics tasks, and
-currently supports multiple shooting-based planners. Derivative-based methods include iLQG and
-Gradient Descent, while derivative-free methods include a simple yet very competitive planner
-called Predictive Sampling.
-
-- [Overview](#overview)
-- [Graphical User Interface](#graphical-user-interface)
-- [Installation](#installation)
-  - [macOS](#macos)
-  - [Ubuntu](#ubuntu)
-  - [Build Issues](#build-issues)
-- [Predictive Control](#predictive-control)
-- [Contributing](#contributing)
-- [Known Issues](#known-issues)
-- [Citation](#citation)
-- [Acknowledgments](#acknowledgments)
-- [License and Disclaimer](#license-and-disclaimer)
-
-## Overview
-
-To read the paper describing this software package, please see our
-[preprint](https://arxiv.org/abs/2212.00541).
-
-For a quick video overview of MJPC, click below.
-
-[![Video](http://img.youtube.com/vi/Bdx7DuAMB6o/hqdefault.jpg)](https://dpmd.ai/mjpc)
-
-For a longer talk at the MIT Robotics Seminar in December 2022 describing our results, click
-below.
-
-[![2022Talk](http://img.youtube.com/vi/2xVN-qY78P4/hqdefault.jpg)](https://www.youtube.com/watch?v=2xVN-qY78P4)
-
-A more recent, December 2023 talk at the IEEE Technical Committee on Model-Based Optimization
-is available here:
-
-[![2023Talk](https://img.youtube.com/vi/J-JO-lgaKtw/hqdefault.jpg)](https://www.youtube.com/watch?v=J-JO-lgaKtw&t=0s)
-
-### Example tasks
-
-Quadruped task:
-
-[![Quadruped](http://img.youtube.com/vi/esLuwaWz4oE/hqdefault.jpg)](https://www.youtube.com/watch?v=esLuwaWz4oE)
-
-
-Bimanual manipulation:
-
-[![Bimanual](http://img.youtube.com/vi/aCNCKVThKIE/hqdefault.jpg)](https://www.youtube.com/watch?v=aCNCKVThKIE)
-
-
-Rubik's cube 10-move unscramble:
-
-[![Unscramble](http://img.youtube.com/vi/ZRRvVWV-Muk/hqdefault.jpg)](https://www.youtube.com/watch?v=ZRRvVWV-Muk)
-
-Humanoid motion-capture tracking:
-
-[![Tracking](http://img.youtube.com/vi/tEBVK-MO1Sw/hqdefault.jpg)](https://www.youtube.com/watch?v=tEBVK-MO1Sw)
-
-## Graphical User Interface
-
-For a detailed dive of the graphical user interface, see the
-[MJPC GUI](docs/GUI.md) documentation.
-
-## Installation
-MJPC is tested with [Ubuntu 20.04](https://releases.ubuntu.com/focal/) and [macOS-12](https://www.apple.com/by/macos/monterey/). In principle, other versions and Windows operating system should work with MJPC, but these are not tested.
-
-### Prerequisites
-Operating system specific dependencies:
-
-#### macOS
-Install [Xcode](https://developer.apple.com/xcode/).
-
-Install `ninja` and `zlib`:
-```sh
-brew install ninja zlib
-```
-
-#### Ubuntu 20.04
-```sh
-sudo apt-get update && sudo apt-get install cmake libgl1-mesa-dev libxinerama-dev libxcursor-dev libxrandr-dev libxi-dev ninja-build zlib1g-dev clang-12
-```
-
-### Clone MuJoCo MPC
-```sh
-git clone https://github.com/google-deepmind/mujoco_mpc
-```
-
-### Build and Run MJPC GUI application
-1. Change directory:
-```sh
-cd mujoco_mpc
-```
-
-2. Create and change to build directory:
-```sh
-mkdir build
-cd build
-```
-
-3. Configure:
-
-#### macOS-12
-```sh
-cmake .. -DCMAKE_BUILD_TYPE:STRING=Release -G Ninja -DMJPC_BUILD_GRPC_SERVICE:BOOL=ON
-```
-
-#### Ubuntu 20.04
-```sh
-cmake .. -DCMAKE_BUILD_TYPE:STRING=Release -G Ninja -DCMAKE_C_COMPILER:STRING=clang-12 -DCMAKE_CXX_COMPILER:STRING=clang++-12 -DMJPC_BUILD_GRPC_SERVICE:BOOL=ON
+### required 
+```bash
+pip install ultralytics
+pip install openai
+pip install python-dotenv
 ```
-**Note: gRPC is a large dependency and can take 10-20 minutes to initially download.**
-
-4. Build
-```sh
-cmake --build . --config=Release
-```
-
-6. Run GUI application
-```sh
-cd bin
-./mjpc
-```
-
-### Build and Run MJPC GUI application using VSCode
-We recommend using [VSCode](https://code.visualstudio.com/) and 2 of its
-extensions ([CMake Tools](https://marketplace.visualstudio.com/items?itemName=ms-vscode.cmake-tools)
-and [C/C++](https://marketplace.visualstudio.com/items?itemName=ms-vscode.cpptools))
-to simplify the build process.
-
-1. Open the cloned directory `mujoco_mpc`.
-2. Configure the project with CMake (a pop-up should appear in VSCode)
-3. Set compiler to `clang-12`.
-4. Build and run the `mjpc` target in "release" mode (VSCode defaults to
-   "debug"). This will open and run the graphical user interface.
-
-### Build Issues
-If you encounter build issues, please see the
-[Github Actions configuration](https://github.com/google-deepmind/mujoco_mpc/blob/main/.github/workflows/build.yml).
-This provides the exact setup we use for building MJPC for testing with Ubuntu 20.04 and macOS-12.
-
-# Python API
-We provide a simple Python API for MJPC. This API is still experimental and expects some more experience from its users. For example, the correct usage requires that the model (defined in Python) and the MJPC task (i.e., the residual and transition functions defined in C++) are compatible with each other. Currently, the Python API does not provide any particular error handling for verifying this compatibility and may be difficult to debug without more in-depth knowledge about MuJoCo and MJPC.
-
-## Installation
-
-### Prerequisites
-1. Build MJPC (see instructions above).
-
-2. Python 3.10
-
-3. (Optionally) Create a conda environment with **Python 3.10**:
-```sh
-conda create -n mjpc python=3.10
-conda activate mjpc
-```
-
-4. Install MuJoCo
-```sh
-pip install mujoco
-```
-
-### Install API
-Next, change to the python directory:
-```sh
-cd python
-```
-
-Install the Python module:
-```sh
-python setup.py install
-```
-
-Test that installation was successful:
-```sh
-python "mujoco_mpc/agent_test.py"
-```
-
-Example scripts are found in `python/mujoco_mpc/demos`. For example from `python/`:
-```sh
-python mujoco_mpc/demos/agent/cartpole_gui.py
-```
-will run the MJPC GUI application using MuJoCo's passive viewer via Python.
-
-### Python API Installation Issues
-If your installation fails or is terminated prematurely, we recommend deleting the MJPC build directory and starting from scratch as the build will likely be corrupted. Additionally, delete the files generated during the installation process from the `python/` directory.
-
-## Predictive Control
-
-See the [Predictive Control](docs/OVERVIEW.md) documentation for more
-information.
-
-## Contributing
-
-See the [Contributing](docs/CONTRIBUTING.md) documentation for more information.
-
-## Known Issues
-
-MJPC is not production-quality software, it is a **research prototype**. There
-are likely to be missing features and outright bugs. If you find any, please
-report them in the [issue tracker](https://github.com/google-deepmind/mujoco_mpc/issues).
-Below we list some known issues, including items that we are actively working
-on.
-
-- We have not tested MJPC on Windows, but there should be no issues in
-  principle.
-- Task specification, in particular the setting of norms and their parameters in
-  XML, is a bit clunky. We are still iterating on the design.
-- The Gradient Descent search step is proportional to the scale of the cost
-  function and requires per-task tuning in order to work well. This is not a bug
-  but a property of vanilla gradient descent. It might be possible to ameliorate
-  this with some sort of gradient normalisation, but we have not investigated
-  this thoroughly.
-
-## Citation
-
-If you use MJPC in your work, please cite our accompanying [preprint](https://arxiv.org/abs/2212.00541):
-
-```bibtex
-@article{howell2022,
-  title={{Predictive Sampling: Real-time Behaviour Synthesis with MuJoCo}},
-  author={Howell, Taylor and Gileadi, Nimrod and Tunyasuvunakool, Saran and Zakka, Kevin and Erez, Tom and Tassa, Yuval},
-  archivePrefix={arXiv},
-  eprint={2212.00541},
-  primaryClass={cs.RO},
-  url={https://arxiv.org/abs/2212.00541},
-  doi={10.48550/arXiv.2212.00541},
-  year={2022},
-  month={dec}
-}
-```
-
-## Acknowledgments
-
-The main effort required to make this repository publicly available was
-undertaken by [Taylor Howell](https://thowell.github.io/) and the Google
-DeepMind Robotics Simulation team.
-
-## License and Disclaimer
-
-All other content is Copyright 2022 DeepMind Technologies Limited and licensed
-under the Apache License, Version 2.0. A copy of this license is provided in the
-top-level LICENSE file in this repository. You can also obtain it from
-https://www.apache.org/licenses/LICENSE-2.0.
-
-This is not an officially supported Google product.
diff --git a/assets/X7S/CMakeLists.txt b/assets/X7S/CMakeLists.txt
@@ -0,0 +1,14 @@
+cmake_minimum_required(VERSION 2.8.3)
+
+project(X7S)
+
+find_package(catkin REQUIRED)
+
+catkin_package()
+
+find_package(roslaunch)
+
+foreach(dir config launch meshes urdf)
+	install(DIRECTORY ${dir}/
+		DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION}/${dir})
+endforeach(dir)
diff --git a/assets/X7S/config/joint_names_X7S.yaml b/assets/X7S/config/joint_names_X7S.yaml
@@ -0,0 +1 @@
+controller_joint_names: ['', 'joint1', 'joint2', 'joint3', 'joint4', 'joint5', 'joint6', 'joint7', 'joint8', 'joint9', 'joint10', 'joint11', 'joint12', 'joint13', 'joint14', 'joint15', 'joint16', 'joint17', 'joint18', 'joint19', 'joint20', 'joint21', 'joint22', ]
diff --git a/assets/X7S/export.log b/assets/X7S/export.log
diff --git a/assets/X7S/launch/display.launch b/assets/X7S/launch/display.launch
@@ -0,0 +1,20 @@
+<launch>
+  <arg
+    name="model" />
+  <param
+    name="robot_description"
+    textfile="$(find X7S)/urdf/X7S.urdf" />
+  <node
+    name="joint_state_publisher_gui"
+    pkg="joint_state_publisher_gui"
+    type="joint_state_publisher_gui" />
+  <node
+    name="robot_state_publisher"
+    pkg="robot_state_publisher"
+    type="robot_state_publisher" />
+  <node
+    name="rviz"
+    pkg="rviz"
+    type="rviz"
+    args="-d $(find X7S)/urdf.rviz" />
+</launch>
diff --git a/assets/X7S/launch/gazebo.launch b/assets/X7S/launch/gazebo.launch
@@ -0,0 +1,20 @@
+<launch>
+  <include
+    file="$(find gazebo_ros)/launch/empty_world.launch" />
+  <node
+    name="tf_footprint_base"
+    pkg="tf"
+    type="static_transform_publisher"
+    args="0 0 0 0 0 0 base_link base_footprint 40" />
+  <node
+    name="spawn_model"
+    pkg="gazebo_ros"
+    type="spawn_model"
+    args="-file $(find X7S)/urdf/X7S.urdf -urdf -model X7S"
+    output="screen" />
+  <node
+    name="fake_joint_calibration"
+    pkg="rostopic"
+    type="rostopic"
+    args="pub /calibrated std_msgs/Bool true" />
+</launch>
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1 @@
		controller_joint_names: ['', 'joint1', 'joint2', 'joint3', 'joint4', 'joint5', 'joint6', 'joint7', 'joint8', 'joint9', 'joint10', 'joint11', 'joint12', 'joint13', 'joint14', 'joint15', 'joint16', 'joint17', 'joint18', 'joint19', 'joint20', 'joint21', 'joint22', ]