Dynamic movement primitives in latent space of time-dependent variational autoencoders

Dynamic movement primitives (DMPs) are powerful for the generalization of movements from demonstration. However, high dimensional movements, as they are found in robotics, make finding efficient DMP representations difficult. Typically, they are either used in configuration or Cartesian space, but b...

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Veröffentlicht in:IEEE-RAS International Conference on Humanoid Robots (Print) S. 629 - 636
Hauptverfasser: Nutan Chen, Karl, Maximilian, van der Smagt, Patrick
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.11.2016
Schlagworte:
Decoding
Gaussian distribution
Neural networks
Probabilistic logic
Switches
Training
Trajectory
ISSN:2164-0580
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Abstract Dynamic movement primitives (DMPs) are powerful for the generalization of movements from demonstration. However, high dimensional movements, as they are found in robotics, make finding efficient DMP representations difficult. Typically, they are either used in configuration or Cartesian space, but both approaches do not generalize well. Additionally, limiting DMPs to single demonstrations restricts their generalization capabilities. In this paper, we explore a method that embeds DMPs into the latent space of a time-dependent variational autoencoder framework. Our method enables the representation of high-dimensional movements in a low-dimensional latent space. Experimental results show that our framework has excellent generalization in the latent space, e.g., switching between movements or changing goals. Also, it generates optimal movements when reproducing the movements.
AbstractList Dynamic movement primitives (DMPs) are powerful for the generalization of movements from demonstration. However, high dimensional movements, as they are found in robotics, make finding efficient DMP representations difficult. Typically, they are either used in configuration or Cartesian space, but both approaches do not generalize well. Additionally, limiting DMPs to single demonstrations restricts their generalization capabilities. In this paper, we explore a method that embeds DMPs into the latent space of a time-dependent variational autoencoder framework. Our method enables the representation of high-dimensional movements in a low-dimensional latent space. Experimental results show that our framework has excellent generalization in the latent space, e.g., switching between movements or changing goals. Also, it generates optimal movements when reproducing the movements.
Author Nutan Chen
Karl, Maximilian
van der Smagt, Patrick
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  givenname: Maximilian
  surname: Karl
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  email: karlma@in.tum.de
  organization: Fac. for Inf., Tech. Univ. Munchen, Munich, Germany
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  givenname: Patrick
  surname: van der Smagt
  fullname: van der Smagt, Patrick
  organization: Fac. for Inf., Tech. Univ. Munchen, Munich, Germany
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Snippet Dynamic movement primitives (DMPs) are powerful for the generalization of movements from demonstration. However, high dimensional movements, as they are found...
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SubjectTerms Decoding
Gaussian distribution
Neural networks
Probabilistic logic
Switches
Training
Trajectory
Title Dynamic movement primitives in latent space of time-dependent variational autoencoders
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