TAMP-xArm

This repository implement PDDLStream for xArm7 and offer parallel reinforcement learning environment on Isaac Sim.

Getting Started

Prerequisited

• NVIDIA Docker
• NVIDIA RTX GPU
• NVIDIA Driver 515.xx

Installation

1. Clone the repository

$ git clone --recursive [email protected]/makolon/tamp-xarm.git

2. Build docker image

$ cd tamp-xarm/docker/docker_xarm/
$ ./build.sh

3. Run docker container

$ cd tamp-xarm/docker/docker_xarm/
$ ./run.sh

4. Compile FastDownward

$ cd tamp-xarm/xarm_tamp/pddlstream/downward/
$ git submodule update --init --recursive
$ python3 build.py
$ cd ./builds/
$ ln -s release release32

Usage

Simulation

You can test PDDLStream on 3D pybullet environment including cooking, holding block task.

$ cd tamp-xarm/xarm_tamp/tampkit
$ python3 tamp_planner.py --problem <problem_name>

Setup IKfast

Compile IKfast

Build & run docker for openrave that contain IKfast scripts.

$ cd tamp-xarm/docker/docker_openrave/
$ ./build.sh
$ ./run.sh

Then, execute ikfast scripts that can automatically create cpp IK solver and copy and plaste the appropriate scripts to <ik_solver>.cpp.

$ ./exec_openrave.sh

After that process, you can call IK solver in python script by executing the following commands.

$ cd tamp-xarm/xarm_tamp/tampkit/sim_tools/pybullet/ikfast/xarm/
$ python3 setup.py

Create xArm Collada Model

If you don't have xarm collada model, you have to run the following commands in docker_openrave container.
Terminal 1.

$ cd /ikfast/
$ roscore

Terminal 2.

$ cd /ikfast
$ export MYROBOT_NAME='xarm'
$ rosrun collada_urdf urdf_to_collada "$MYROBOT_NAME".urdf "$MYROBOT_NAME".dae

Then, you can see the generated xArm collada model using following commands.

$ openrave-robot.py "$MYROBOT_NAME".dae --info links
$ openrave "$MYROBOT_NAME".dae

For more informations, please refer to the following document.