Multi-Object Grasping in the Plane

We consider the problem where multiple rigid convex polygonal objects rest in randomly placed positions and orientations on a planar surface visible from an overhead camera. The objective is to efficiently grasp and transport all objects into a bin. Specifically, we explore multi-object push-grasps where multiple objects are pushed together before the grasp can occur. We provide necessary conditions for multi-object push-grasps and apply these to filter inadmissible grasps in a novel multi-object grasp planner. We find that our planner is 19 times faster than a Mujoco simulator baseline. We also propose a picking algorithm that uses both single- and multi-object grasps to pick objects. In physical grasping experiments, compared to a single-object picking baseline, we find that the multi-object grasping system achieves 13.6% higher grasp success and is 59.9% faster.

Here, we provide the source code for our implementation.

More information can be found in our paper ISRR 2022

Getting started

1. Create and activate a virtual environment (Code was tested with Ubuntu16.04 and python3.5)
	$ virtualenv -p /usr/bin/python3.5 venv ; source venv/bin/activate

2. Install Physics Simulator Mujoco and dm_control in virtual env
	Follow instructions from Deepmind's dm_control project
	https://github.com/deepmind/dm_control.                                    

3. Install other required python packages
	$  pip install -r requirements.txt

4. Clone this repo/ Download and extract zip file.
	$ git clone https://github.com/wisdomagboh/multi-object-grasping.git

5. Run setup.py to place custom domains into 'suite'
	$  python3.5 setup/setup.py

Running experiments

1. Simulation experiments
	$ bash run_exps.sh

2. Generate simulation results
	$ python sim_plots_summary.py

3. Physical picking experiments - launch two ros nodes.
	$ python3.5 rw_planner.py
	$ python3.5 arm_motion_generator.py 'grasp_type (MOG or SOG)' 'scene_number'

4. Generate physical experimental results
	$ python rw_table_data.py

Website

ISRR 2022

Have a question?

For all queries please contact Wisdom Agboh (wisdomagboh@gmail.com)

License

This project is licensed under the MIT License - see the LICENSE.md file for details.