walker_planner

Planner for Wheeled Walker.

Setup

1. Clone this repo (unified_planner branch) in catkin_ws/src

Also clone following :

git clone https://github.com/shivamvats/smpl.git -b mrmha
git clone https://github.com/aurone/leatherman
git clone https://github.com/SBPL-Cruz/wheeled_walker

In separate folder :

git clone https://github.com/shivamvats/sbpl -b mrmha
mkdir build
mkdir install
cd build
cmake -DCMAKE_INSTALL_PREFIX=../install ..
make install

4. Change ~/lolocal in smpl/smpl/CmakeLists.txt and smpl/smpl/smpl-config.cmake.in to install path inside sbpl folder created above

5. Install also :

sudo apt-get install ros-kinetic-trac-ik 

6. Build only our package: catkin build walker_planner

Rviz

1. Start RVIZ
2. Open the config in the repo proj.rviz
3. Once this open, map, goal states while generating goals and generate plan can be visualized

Generating Traj

Working directory : walker_planner

1. Generate map in ~/.ros/-multi_room_map.env. Copy this to env folder and rename to proj_env.env :

roslaunch walker_planner generate_map.launch
cp ~/.ros/-multi_room_map.env env/proj_env.env

2. Generate start/goal pairs. Generates 500 start/goal pairs in ~/.ros/goal_* ~/.ros/start_*. Copy these to environments folder

roslaunch walker_planner generate_start_goals.launch 
cp ~/.ros/goal_* experiments
cp ~/.ros/start_states.txt experiments/

3. Change number of paths you need to generate plans for (start/goal pairs) in config/walker_right_arm.yaml in the end_planning_episode variable. Set to 499 to generate for all start/goal pairs. By default this is set to 0 to visualize plan for the first start/goal pair only
4. Run planner and verify in RVIZ : roslaunch walker_planner mrmhaplanner.launch

Creating Dataset

1. Go to cvae folder
2. Run the following to generate clean dataset for env '1' located in 'data/train'

source activate.sh
python create_data.py --env 0

3. Run following to generate clean dataset for all env located in 'data/train'

source activate.sh
python create_data.py

Training CVAE

1. Go to cvae folder.
2. Run following command to run for base cvae

python run.py --dataset_root ../data/train_clean --num_epochs 50 --dataset_type base