RAMP

Vasileios Vasilopoulos, Suveer Garg, Pedro Piacenza, Jinwook Huh, Volkan Isler

This package includes the code for RAMP: Hierarchical Reactive Motion Planning for Manipulation Tasks Using Implicit Signed Distance Functions, developed by Samsung Research (Samsung AI Center - New York) and presented at IROS 2023.

For simulation demonstrations, we use the Isaac Gym physics simulation environment from NVIDIA, as well as modified environment generation code from SceneCollisionNet, included in the sim_env folder. For the computation of differentiable forward kinematics, the package uses pytorch-kinematics.

Installation and running

Environment setup

1. Create a python virtual environment inside the repo, source it and update pip:

    python3 -m venv pyvenv
    source pyvenv/bin/activate
    pip install --upgrade pip
    

1. Install the requirements. When installing PyTorch, make sure that the PyTorch version matches your installed CUDA version by updating --extra-index-url, for example: https://download.pytorch.org/whl/cu118 for CUDA 11.8. You can check your CUDA version by running: nvcc --version.

    pip install -r requirements.txt
	

1. Install PyTorch3D with GPU support:

    pip install git+https://github.com/facebookresearch/pytorch3d.git@stable
	

1. Download Isaac Gym and copy the isaacgym folder into extern (Prerequisites:Ubuntu 18.04, or 20.04. Python 3.6, 3.7, or 3.8).

Setup all packages

Install all packages by running:

pip install -e .

Run without any arguments

Run the simulation:

python -m test_ramp_simulation

Run with optional arguments

Optionally, you can visualize the start (green) and goal (red) sphere markers with the --markers flag and/or you can specify an experiment to run with the --experiment flag. For demonstration purposes, we have included 5 static environment scenarios (numbered 0-4) and 5 dynamic environment scenarios (numbered 10-14). The full list of all available arguments is included near the top of the main script.

python -m test_ramp_simulation --markers True --experiment 10

Known Issues

1. If you see the error WARNING: lavapipe is not a conformant vulkan implementation, testing use only., try the following command:

    export VK_ICD_FILENAMES=/usr/share/vulkan/icd.d/nvidia_icd.json
    

Use Outside of Isaac Gym Environment - API Description

In this package, RAMP is wrapped around an Isaac-Gym simulation environment. To use the algorithm for your own application:

1. You need to copy over to your project the mppi_planning (which includes the trajectory generator) and trajectory_following (which includes the trajectory follower) folders.
2. You need to instantiate a TrajectoryPlanner (see trajectory_planning) class, for example:

```python # Robot parameters JOINT_LIMITS = [ np.array([-2.8973, -1.7628, -2.8973, - 3.0718, -2.8973, -0.0175, -2.8973]), np.array([2.8973, 1.7628, 2.8973, - 0.0698, 2.8973, 3.7525, 2.8973]) ]

LINK_FIXED = 'panda_link0'
LINK_EE = 'panda_hand'

LINK_SKELETON = [
    'panda_link1',
    'panda_link3',
    'panda_link4',
    'panda_link5',
    'panda_link7',
    'panda_hand',
]

robot_urdf_location = 'resources/panda/panda.urdf'
scene_urdf_location = 'resources/environment/environment.urdf'

# Instantiate trajectory planner
self.trajectory_planner = TrajectoryPlanner(
    joint_limits=JOINT_LIMITS,
    robot_urdf_location=robot_urdf_location,
    scene_urdf_location=scene_urdf_location,
    link_fixed=LINK_FIXED,

File truncated at 100 lines see the full file