Feature Disentanglement of Robot Trajectories
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Abstract (arXiv)
Modeling trajectories generated by robot joints is complex and required for high level activities like trajectory generation, clustering, and classification. Disentagled representation learning promises advances in unsupervised learning, but they have not been evaluated in robot-generated trajectories. In this paper we evaluate three disentangling VAEs (β-VAE, Decorr VAE, and a new β-Decorr VAE) on a dataset of 1M robot trajectories generated from a 3 DoF robot arm. We find that the decorrelation-based formulations perform the best in terms of disentangling metrics, trajectory quality, and correlation with ground truth latent features. We expect that these results increase the use of unsupervised learning in robot control.
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References · 5
- 2018Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representationscited by 941
- 2018Isolating Sources of Disentanglement in Variational Autoencoderscited by 167
- 2018Disentangled Sequential Autoencodercited by 120
- 2015Reducing Overfitting in Deep Networks by Decorrelating Representationscited by 94
- 2017Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Datacited by 93
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