🛰 LEMP: Learning-Enabled Motion Planning

Status: Active Development

LEMP (Learning-Enabled Motion Planning) is a light-weight framework that combines the power of machine learning with traditional motion planning techniques. With a focus on fast iteration, LEMP provides a rapid and agile solution solution for developing learning algorithms for motion planning tasks.

Installation

Create Conda Environment

$ conda create -n lemp python=3.8
$ conda activate lemp
$ conda install -c conda-forge jupyterlab numpy matplotlib
$ pip install pybullet Pillow scipy
# install torch following the instructions from the pytorch website, for example:
$ conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
# install torch-geometric following the instructions from the torch-geometric website, for example:
$ pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.1.0+cu118.html
$ pip install torch_geometric

Unzip the Datasets

cd data
unzip static.zip
unzip dynamic.zip

Quickstart

We provide a bunch of useful notebooks in examples.

Notebook	Description
bit_star_planner.ipynb	Example of the BIT* algorithm for planning.
dataset.ipynb	Saving and loading dataset for static obstacles.
dataset_dynamic.ipynb	Saving and loading Dataset for dynamic obstacles.
dynamic_gnn_planner.ipynb	Integration of GNN models with a dynamic planner.
grouping_robot.ipynb	Grouping multiple robot arms as one robot to plan
load_environment.ipynb	Visualization of trajectories in environments.
load_object.ipynb	Load objects / obstacles to the environment.
load_robot.ipynb	Load robot to the environment.
object_follow_trajectory.ipynb	Trajectory visualization for objects.
robot_follow_trajectory.ipynb	Trajectory visualization for robots.
rrt_star_planner.ipynb	Example of the RRT* algorithm for planning.
sipp_planner.ipynb	Example of the SIPP* algorithm for dynamic planning.
static_gnn_planner.ipynb	Integration of GNN models with a static planner.