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Unsupervised Learning of Hierarchical Models for Hand-Object Interactions (ICRA 2018)

Source Code of Clustering pipeline / ACA optimization / Inference labeling

Usage

$ git clone https://github.com/xiaozhuchacha/AtomicAction.git

Clustering

  • use 'main.m' as the entry in 'Clustering' folder to start clustering pipeline
  • sample hand data is placed in 'Clustering/hand_data'

ACA

  • use 'ACA.cpp' as the entry in 'ACA' folder to start clustering optimization
  • to compile: g++ ACA.cpp -o ACA
  • execute: ./ACA [NC] [data_name], eg. ./ACA 9 sample_hand_data

Inference

  • use 'AnnealGibbs.py' as the entry in 'Inference' folder to start Gibbs annealing
  • parser is placed in 'Inference/induced_grammar' to calculate prior
  • gaussians are placed in 'Inference/Gaussians' to calculate likelihood
  • use 'main.m' as the entry in 'Inference/Gaussians' to compute gauusian parameters w.r.t ground truth labeled data
  • labeled motion sequence as input can be found either in 'Clustering/hcluster' as hierarchical clustering result or 'ACA/ACAbin' as ACA optimization result
  • execute: python AnnealGibbs.py [--nlabel] [--data-name], eg. python AnnealGibbs.py --nlabel 6 --data-name sample_hand_data

Contact

Paper Link

  • see gloveaction2018icra.pdf

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Unsupervised Learning of Hierarchical Models for Hand-Object Interactions (ICRA 2018)

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