Robot Planning, Control, and Deployment (rpc)

RPC is a Modular Framework for Robot Planning, Control, and Deployment. It is designed to integrate multiple physics-based simulators, planning and control modules, visualization tools, plotting and logging utilities, and operator interfaces for robotic systems.
If you find our work useful in your research, please consider the following citation.

📦 Mandatory Dependencies

The controller has been tested on Ubuntu 18.04, Ubuntu 20.04, Ubuntu 22.04, and Mac OSX Sonoma. It builds on the shoulders of the following software:

• anaconda: For Pybullet simulator
  
• python dependencies:

$ conda env create -f rpc.yml

• pinocchio: Rigid body dynamics library

🗞️ Optional Dependencies

🚶 MPC for Locomotion

• hpipm-cpp: C++ wrapper for HPIPM (QP solver). Note that blasfo, hpipm, and hpipm-cpp wrapper should be installed

🤏 Teleoperation for Manipulation

• Teleoperation: Please follow the instructions for installation and usage

🧰 Utilities for Visualization, Plotting, Logging and Operator Interfaces

• MatLogger2: logging numeric data (cpp to MAT-files)
• zmq: socket communication protocol
• protobuf: structured data serialization
• conan: package manager for C/C++ (for Foxglove)
• Foxglove: websocket & schema protocols for robot visualization and parameter operations

💻 Usage

(1) PyBullet

• Source conda environment:
  

$ conda activate rpc

• Compile:
  

$ mkdir build && cd build
$ cmake ..
$ make -j4

• Run simulation:
  

$ python simulator/pybullet/draco_main.py

(2) MuJoCo

• Compile:
  

$ mkdir build && cd build
$ cmake ..
$ make -j4

• Run simulation:
  

$ ./bin/run_draco

Keyboard Input

• Please see the example implementation for DRACO 3

📺 Visualization

(1) Foxglove UI (optional)

Build

• Source conda environment:
  

$ conda activate rpc

• Compile:
  

$ mkdir -p ~/.conan2/profiles/ && cp .github/conan_profile ~/.conan2/profiles/default
$ conan install conanfile.txt --build=missing
$ cd build
$ cmake .. -DBUILD_WITH_ZMQ_PROTOBUF=ON -DBUILD_WITH_FOXGLOVE=ON
$ make -j4

Run

$ conda env create -f visualize.yml
$ conda activate visualize
$ python UI/foxglove/UI_launcher.py --visualizer=foxglove

• Access Foxglove server via either:
  

  1. Account in Foxglove webservice
  2. Foxglove application

• To set up the Foxglove URDF visualizer, please refer to the Readme in the UI folder

(2) Meshcat Visualizer (optional)

Run

$ conda env create -f visualize.yml
$ conda activate visualize
$ python UI/foxglove/UI_launcher.py --visualizer=meshcat

🤖 Hardware Usage

• Please refer to this repository using rpc library

📖 Citation

@INPROCEEDINGS{10871130,
  author={Bang, Seung Hyeon and Gonzalez, Carlos and Moore, Gabriel and Kang, Dong Ho and Seo, Mingyo and Gupta, Ryan and Sentis, Luis},
  booktitle={2025 IEEE/SICE International Symposium on System Integration (SII)}, 
  title={RPC: A Modular Framework for Robot Planning, Control, and Deployment}, 
  year={2025},
  volume={},
  number={},
  pages={1142-1148},
  keywords={Legged locomotion;Software architecture;Scalability;Software algorithms;Debugging;System integration;Software;Planning;Robots;Testing},
  doi={10.1109/SII59315.2025.10871130}}