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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| Vydané v: | IEEE-RAS International Conference on Humanoid Robots (Print) s. 629 - 636 |
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| Hlavní autori: | , , |
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| Jazyk: | English |
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01.11.2016
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| 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. |
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| 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 |
| Author_xml | – sequence: 1 surname: Nutan Chen fullname: Nutan Chen email: nutan.chen@gmail.com organization: Fac. for Inf., Tech. Univ. Munchen, Munich, Germany – sequence: 2 givenname: Maximilian surname: Karl fullname: Karl, Maximilian email: karlma@in.tum.de organization: Fac. for Inf., Tech. Univ. Munchen, Munich, Germany – sequence: 3 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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