RAMMP

RAMMP can be deployed in one of the following configurations:

• Two-machine setup
  • GPU Machine (GM): Runs compute-intensive components.
  • Controller Machine (CM): Handles controllers and emergency stops.
• Single-machine setup (SM): Runs all components on a single system.

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Requirements

• Operating System: Ubuntu 20.04
• Python: 3.10 or later
• ROS: Noetic (including rospy)

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Installation

The system uses two Conda environments:

• compute – for compute-intensive components
• controller – for control and hardware interfaces

In a two-machine setup, these environments typically live on separate machines (GM and CM).
In a single-machine setup, both environments can exist on the same machine.

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Compute Setup

1. Create the Conda environment:

   conda create -n compute python=3.10

2. Activate the environment:

   conda activate compute

3. Install PyTorch (CUDA 12.1):

   conda install pytorch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 \
       pytorch-cuda=12.1 numpy=1.26.4 numpy-base=1.26.4 \
       -c pytorch -c nvidia -c defaults

4. Install PyTorch3D:

   python -m pip install pytorch3d \
       -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/py310_cu121_pyt210/download.html

5. Clone the RAMMP repository:

   git clone https://github.com/empriselab/RAMMP
   cd RAMMP

6. Install additional dependencies:

   pip install --no-build-isolation chumpy

7. Install RAMMP (full installation):

   pip install -e ".[full]"

8. Install Pinocchio:

   conda install -c conda-forge "pinocchio=3.1.*"

9. Add the following to your ~/.bashrc:

   export LD_LIBRARY_PATH=/lib/x86_64-linux-gnu:/usr/lib/x86_64-linux-gnu:/lib:/usr/local/cuda-12.1/lib64:/opt/ros/noetic/lib:/opt/ros/noetic/lib/x86_64-linux-gnu
   export LD_PRELOAD="/lib/x86_64-linux-gnu/libffi.so.7 /lib/x86_64-linux-gnu/libtiff.so.5"

   Then reload:

   source ~/.bashrc

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Controller Setup

1. Create the Conda environment:

   conda create -n controller python=3.10

2. Activate the environment:

   conda activate controller

3. Install the Kortex API (after downloading the wheel file):

   python -m pip install kortex_api-2.6.0.post3-py3-none-any.whl

4. Clone the RAMMP repository:

   git clone https://github.com/empriselab/RAMMP
   cd RAMMP

5. Install RAMMP (controller-only installation):

   pip install -e ".[controller]"

6. Install Pinocchio:

   conda install -c conda-forge "pinocchio=3.1.*"

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Running

Drinking Demo

1. Run the arm controller server on the NUC:
   • ssh to the NUC: ssh emprise@192.168.1.3 with lab password
   • run the controller server:
     • conda activate controller
     • cd /home/emprise/RAMMP/src/rammp/control/robot_controller
     • python arm_server.py
2. Run bulldog on the NUC:
   • ssh to the NUC: ssh emprise@192.168.1.3 with lab password
   • run bulldog:
     • conda activate controller
     • cd /home/emprise/RAMMP/src/rammp/integration
     • ./run_bulldog.sh
3. Run a roscore on the compute system: roscore
4. Launch the roslaunch on compute system for camera / visualization / publish tfs:
   • conda activate compute
   • cd /home/rammp/rammp_ws/src/RAMMP/launch
   • roslaunch robot.launch
5. Run the watchdog / transfer button listener / other safety stuff:
   • conda activate compute
   • cd /home/rammp/rammp_ws/src/RAMMP/src/rammp/integration
   • ./launch_robot.sh
6. Run the drinking demo:
   • conda activate compute
   • source ~/rammp_ws/devel/setup.bash
   • cd /home/rammp/rammp_ws/src/RAMMP/src/rammp/integration
   • python run.py --user rammp --run_on_robot

Moving the robot to preset configurations

You can move the robot to preset configurations by running:

• cd /home/rammp/rammp_ws/src/RAMMP/src/rammp/control/robot_controller/preset_actions
• python retract.py