# Robot Motion Control Task
## Overview

**Robotmctask** is a comprehensive C++ library designed for robot motion control task development. It provides APIs that enable robot developers to build sophisticated robot applications with integrated AI inference engines and EtherCAT protocol support.

# Key Features:

- **PLCOpen Compliance:** Support for PLCOpen motion control function blocks
- **Multi-Task Support:** Concurrent handling of multiple motion control tasks
- **Real-Time Integration:** Seamless shared memory communication between real-time and non-real-time domains
- **AI-Powered Control:** Built-in  inference engine support for intelligent motion control
- **Simulation Ready:** Comprehensive motion control simulation capabilities

## Architecture Overview

The architecture is as following:

<p>
    <img src="images/arch.png" alt="Robot Motion Control Architecture" title="Robot Motion Control Architecture">
</p>

Two key blocks have been introduced to support the core architecture:

* **libectask** is a dynamic library that provides a set of public APIs for EtherCAT real-time task creation. Key capabilities include:
    - **EtherCAT Integration:** Creates motion function blocks to operate EtherCAT slaves using ENI configurations
    - **Network Topology Support:** Handles slave configurations and network topology definitions
    - **Flexible Memory Management:** Supports custom shared memory callbacks for data restructuring between real-time and non-real-time domains
    - **AI Pipeline Integration:** Enables AI inference callback registration within real-time tasks

* **libinference** is a shared library offering advanced inference capabilities with:
    - **Modern C++ Interface:** New C++ classes for streamlined inference implementation
    - **Reinforcement Learning Support：** Integrated RL classes optimized for robotic applications. To facilitate quick demonstrations of code functionality, provide a stable-standing ONNX model based on real robot. This model maintains whole-body balance by controlling the lower limbs while the upper body performs hand movements. Such a model enables humanoid robots to remain stable through reinforcement learning when the upper limbs are engaged in VLA tasks.
    - **Performance Analytics:** Built-in statistical collection for performance monitoring
    - **Intel OpenVINO integration:** Default inference engine with extensible architecture
    - **Extensible Design:** Bases classes enabling custom AI model development

## Network Topology

The following diagram illustrates a typical fieldbus topology for humanoid robot applications: 

<p>
    <img src="images/Humanoid_Robot_Topology.png" alt="Humanoid Robot Network Topology" title="Humanoid Robot Network Topology">
</p>



## Getting Started

### Requirements
The software runs on standard PCs or servers. Since it is primarily developed in C++, porting to other operating systems is straightforward.

### Running 

Please check [README](./../README.md) file for details.

### Examples

One example using Robot Motion Control Task is provided.

* [mc_rl_sample](./../examples/mc_rl_sample.cpp):
A comprehensive demonstration showcasing:

    - **Multi-Topology Configuration:** Robot task setup with multiple topologies using different ENI files
    - **Distributed Control:** Three separate ectasks controlling distinct EtherCAT topologies:
        - Left/Right Arm Control
        - Leg control systems
    - **Dual Arm Integration:** Joint state publishing via shared memory for VLA(Vision-Language-Action) models or simulator integration
    - **Custom Motion Algorithms:** Leg control with registered callbacks for customized motion algorithms and inference pipelines

## License

The source code is licensed under Apache License . See [LICENSE](./../LICENSE) file for details.