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rail_semantic_grasping_offline

This package contains the semantic grasping dataset SG14000, and the code for the Semantic Grasp Network in the paper CAGE: Context-Aware Grasping Engine, currently available on arxiv.

The code can be used to collect semantic grasp labels from users and test our proposed Semantic Grasp Network and other baselines on the labeled data.

This package is a stand-alone package, so it is separated from the code that extracts semantic information from grasps and executes semantic grasps on the Fetch robot. A separate package will be released for these functions.

Data

All data is stored in /data. The structure of the folder is:

-data
 -Objects_Description.md
 -labeled
  -YEAR_MONTH_DAY_HOUR_MINUTE
 -unlabeled
  -YEAR_MONTH_DAY_HOUR_MINUTE
 -base_features
  -YEAR_MONTH_DAY_HOUR_MINUTE

Folders with the same date and time as the name in labeled, unlabeled, and base_features contain data for the same experiment.

SG14000 Dataset

The SG14000 dataset is in the /data/labeled/2019_08_19_11_08 folder. The folder has 44 pickle files for the 44 objects in the dataset. Each file is a pickled ROS message described in /msg/SemanticObjectList.msg. Each SemanticObjectList message only contains one semantic object defined in /msg/SemanticObject.msg. Furthermore, each SemanticObject message contains a list of grasps in the field named labeled_grasps. These grasps are the labeled semantic grasps of the SG14000 dataset. As defined in SemanticGrasp.msg, each semantic grasp has a pose, a grasp affordance and a grasp material (semantic representation of grasps introduced in the paper), a task (which we use to describe both the manipulation task and the object state), and a label (which indicates whether this grasp is suitable for the context).

Detailed description for all objects can be found in /data/Object_Description.md.

Unlabeled Data

To label semantic grasps yourself, you can use the data in data/unlabled/2019_08_19_11_08. The folder also contains 44 pickle files for the 44 objects. The only difference from the data described above is each SemanticObject message now includes a list of unlabeled grasps in the field grasps. To collect grasp labels with the provided code, please see the instructions in the Code section. Remember to rename the folder name to avoid rewriting the SG14000 dataset.

Base Features

One of the baselines of the paper uses base features such as image gradients and RGBD descriptors to ground semantic grasps. For each object in /data/labeled/2019_08_19_11_08, there is a corresponding pickle file in /data/base_features/2019_08_19_11_08. Each pickle file contains a list of base features, defined in /msg/BaseFeatures.msg, for each grasp.

Code

ROS Package

This package was originally developed with ROS melodic. However, it can be compiled and used in ROS kinetic by adding the line add_compile_options(-std=c++11) to the top-level CMakeLists.txt of the workspace.

Collect Labels

The steps to collect your own labels for semantic grasps:

  1. run roscore
  2. run rosrun rail_semantic_grasping grasp_collection_node.py
  3. run rviz
  4. follow the prompt and visualization in rviz

Compute Base Features

The steps to compute base features:

  1. run roscore
  2. run rosrun rail_semantic_grasping base_features_computation_node to start the node for computing object base features
  3. run rosrun rail_semantic_grasping base_features_collection_node.py to save base features to pickle files

Test CAGE and other baselines

Code for both our Semantic Grasp Network and baselines is in /main. Scripts in this folder can all be run directly in python shell. No rosrun is needed. However, make sure you source your catkin space because the code still depends on message types define in ros.

To run various models on the experiments described in the paper, check out /main/run_experiments.py.

The code mainly has three components: /main/DataReader.py reads the grasps in pickle files and prepares data splits for different experiments; /main/algorithms has implementations for different algorithms; Metrics.py scores algorithms based on their predictions.

About

Pytorch code and SG14000 dataset for our ICRA 2020 paper "Context Aware Grasping Engine"

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