Feature Disentanglement of Robot Trajectories

• URL: http://arxiv.org/abs/2112.03164v1
• Date: Mon, 6 Dec 2021 16:52:55 GMT
• Title: Feature Disentanglement of Robot Trajectories
• Authors: Matias Valdenegro-Toro, Daniel Harnack, Hendrik W\"ohrle
• Abstract summary: Disentagled representation learning promises advances in unsupervised learning, but they have not been evaluated in robot-generated trajectories. We evaluate three disentangling VAEs 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.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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 ($\beta$-VAE, Decorr VAE, and a new $\beta$-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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