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Package Summary

The agile_grasp ROS package. AGILE stands for Antipodal Grasp Identification and LEarning. The package finds antipodal grasps in point clouds.

Package Summary

The agile_grasp ROS package. AGILE stands for Antipodal Grasp Identification and LEarning. The package finds antipodal grasps in point clouds.

Overview

This package localizes antipodal grasps in 3D point clouds. AGILE stands for Antipodal Grasp Identification and LEarning. The reference for this package is: Using Geometry to Detect Grasps.

The package already comes with pre-trained machine learning classifiers and can be used (almost) out-of-the-box, in particular with an Asus Xtion Pro range sensor.

For a complete grasping demo on a Baxter robot, check out our grasp_selection package.

The package contains two command line tools and one ROS node:

  1. train_svm: (command line tool) Train an SVM to localize grasps in point clouds.

  2. test_svm: (command line tool) Localize grasps in a .pcd file using a trained SVM.

  3. find_grasps: (ROS node) Localize grasps in a point cloud obtained from a range sensor.

Requirements

  1. Lapack (install in Ubuntu: $ sudo apt-get install liblapack-dev)

  2. OpenNI or a similar range sensor driver

Installation

From Source, ROS Indigo

  1. Open a terminal
  2. Navigate to the *src* folder of your ROS workspace: $ cd location_of_workspace/src

  3. Clone the repository: $ git clone https://github.com/atenpas/agile_grasp.git

  4. Navigate back to the root of your ROS workspace: $ cd ..

  5. Recompile your ROS workspace: $ catkin_make

From Source, ROS Hydro

Follow the instructions for ROS Indigo, except for Step (3):

$ git clone https://github.com/atenpas/agile_grasp.git -b hydro

Localize Grasps with a Robot

Localize grasps on a robot using a range sensor, such as a Kinect:

$ roslaunch agile_grasp single_camera_grasps.launch

Localize grasps on a robot

Two example ROS launch files, single_camera_grasps.launch and baxter_grasps.launch, are provided that illustrate how to use the find_grasps ROS node to localize grasps in a point cloud obtained from one or two range sensors.

Instructions for Asus Xtion Pro

  1. Start roscore: $ roscore

  2. Connect an Asus Xtion Pro range sensor
  3. Launch the Asus ROS driver: $ roslaunch openni2_launch openni2.launch

  4. Detect grasps: $ roslaunch agile_grasp single_camera_grasps.launch

  5. Start Rviz for visualization: $ rosrun rviz rviz

  6. Open the config file agile_grasp/rviz/single_camera.rviz in Rviz to visualize the grasps.

Launch File Parameters

The most important parameters to increase the number of grasps found are num_samples and workspace. A higher sample number means that a larger subset of points in the point cloud will be considered. A smaller workspace means that less samples are required to find grasps.

The most important parameters are listed below.

Remark: If you want to adjust the grasp parameters, you can do this from the launch file. For the available parameters, see src/nodes/find_grasps.cpp.

Localize Grasps in a Point Cloud File

Localize grasps in a point cloud stored in a .pcd file:

$ rosrun agile_grasp test_svm /home/userABC/data/input.pcd /home/userABC/ros_ws/src/agile_grasp/svm_032015_20_20_same

input output

Usage

$ rosrun agile_grasp test_svm pcd_filename svm_filename [num_samples] [num_threads] [min_handle_inliers]

This localizes grasps in the point cloud file pcd_filename using the SVM stored in the file svm_filename. The last three parameters are optional. num_samples sets the number of samples, num_threads sets the number of CPU threads used, and min_handle_inliers sets the minimum number of grasps required to have a cluster of grasps.

Notice: When no handles or not enough antipodal grasps are found, please increase the num_samples parameter. Another option is to modify the workspace limits in src/nodes/test.cpp (this requires recompiling the code).

output

Parameters

Training the SVM

To train the SVM to predict grasps, first create a directory that contains the .pcd files used for training.

Method A

Preparations:

Training:

$ rosrun agile_grasp train_svm num_files pcd_directory/obj svm_filename [plots_hands] [num_samples] [num_threads]

Method B

Preparations:

Example: files.txt

file1
file2
...

Example: workspace.txt

file1
file2
...

Training

$ rosrun agile_grasp train_svm num_files pcd_directory svm_filename [plots_hands] [num_samples] [num_threads]

Parameters

Citation

If you like this package and use it in your own work, please cite our ISRR2015 paper:

Andreas ten Pas and Robert Platt. Using Geometry to Detect Grasp Poses in 3D Point Clouds. International Symposium on Robotics Research (ISRR), Italy, September 2015.


2019-08-17 12:31