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JRM Vol.31 No.3 pp. 493-499
doi: 10.20965/jrm.2019.p0493
(2019)

Paper:

Trajectory Prediction with a Conditional Variational Autoencoder

Thibault Barbié, Takaki Nishio, and Takeshi Nishida

Department of Mechanical and Control Engineering, Kyushu Institute of Technology
1-1 Sensui, Tobata, Kitakyushu, Fukuoka 804-8550, Japan

Received:
December 5, 2018
Accepted:
April 15, 2019
Published:
June 20, 2019
Keywords:
trajectory prediction, machine learning, conditional variational autoencoder
Abstract
Trajectory Prediction with a Conditional Variational Autoencoder

Trajectory prediction using a CVAE

Conventional motion planners do not rely on previous experience when presented with a new problem. Trajectory prediction algorithms solve this problem using a pre-existing dataset at runtime. We propose instead using a conditional variational autoencoder (CVAE) to learn the distribution of the motion dataset and hence to generate trajectories for use as priors within the traditional motion planning approaches. We demonstrate, through simulations and by using an industrial robot arm with six degrees of freedom, that our trajectory prediction algorithm generates more collision-free trajectories compared to the linear initialization, and reduces the computation time of optimization-based planners.

Cite this article as:
T. Barbié, T. Nishio, and T. Nishida, “Trajectory Prediction with a Conditional Variational Autoencoder,” J. Robot. Mechatron., Vol.31, No.3, pp. 493-499, 2019.
Data files:
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Last updated on Sep. 19, 2019