mgualti / seq6dofmanip Goto Github PK

This is code for reproducing the simulated experiments in the paper, "Learning Manipulation Skills Via Hierarchical Spatial Attention", by Marcus Gualtieri and Robert Platt. See my website, http://www.ccs.neu.edu/home/mgualti for details. The code is similar to that in the earlier 6-DoF version, https://github.com/mgualti/DeepRLManip, but this version is much faster and should be easier to get started with.

Getting started:

1. Pick a domain. From simplest to most complex: "Tabular Pegs on Disks", "Upright Pegs on Disks", "Pegs on Disks", and "Bottles on Coasters".
2. Non-tabular domains require Keras with Tensorflow (https://www.tensorflow.org/install), PointCloudsPython (https://github.com/mgualti/PointCloudsPython), and OpenRAVE (https://github.com/rdiankov/openrave). The code has been tested with Tensorflow 2.0. These instructions for installing OpenRAVE were helpful: https://scaron.info/teaching/installing-openrave-on-ubuntu-16.04.html. Last time I checked, the main branch of OpenRAVE was sufficient. I plot the results using Matlab, but this is not a strict requirement.
3. For "Pegs on Disks" and "Bottles on Coasters", generate object meshes using the scripts named python/generate_*.py. Make sure to adjust the paths in the scripts. "Bottles on Coasters" requires 3DNet Cat-10, which can be downloaded from https://www.acin.tuwien.ac.at/en/vision-for-robotics/software-tools/3dnet-dataset/.
4. From the domain's main directory, run "python/train.py train_params". This will run the experiment with the parameter file train_params.py. Some paths may need adjusted in the parameter file.
5. In the parameter file, adjust the visualization/plotting parameters to see if the simulation is working.

Highlights:

• Grasp and place checks, including the antipodal conditition, are in the rl_environment_[domain].py.
• HSA is implemented in the files starting with rl_agent.

Data: Trained model files and result files are in a separate repository. Let me know if there is an interest.

Robot experiments: The ROS nodes and motion planning code is all in a separte repository. Let me know if there is an interest in using this code as well.