This project is a Matlab implementation of Trottier, L. et al. (2016) Dictionary Learning for Robotic Grasp Recognition and Detection.

The Cornell dataset and the sub-sampled Washington RGBD dataset used in the experiments of Trottier, L. et al. (2016) is already available in folder data.

To install:

1. Install the toolboxes by running file setup.m
2. Add current folder and all sub-folders to path with addpath(genpath('.'));

File recognitionMain.m, available in folder recognition, is a detailed example on how to perform dictionary learning, sparse coding and SVM training with cross-validation.

To see all available options for additional tweaking, see file loadOverallParameters.m in folder opts.

Folder tasks/recognition contains all recognition experiments used to produce the results presented in Trottier, L. et al. (2016). For instance, running taskRpOmp.m perform 5-5 nested cross-validation using normalized random patches as dictionary and Orthogonal Matching Pursuit as features.

By default, the script is verbose. Consider setting opts.util.verbose = false to turn off verbose mode.