Abstract
In contrast with intensive studies for hardware development in soft robotics, approaches to construct a controller for soft robots has been relying on heuristics. One of the biggest reasons of this issue is that even reconstructing the state-space to describe the behavior is difficult. In this study, we propose a method to reconstruct state-space from movies using a convolutional autoencoder for robot control. In the proposed method, the process that reduces the number of dimensions of each frame in movies is regulated by additional losses making latent variables orthogonal each other and apt to model the forward dynamics. The proposed method was successfully validated through a simulation where a two links planar manipulator is modeled using the movie and controlled based on the forward model.
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Takahara, K., Ikemoto, S., Hosoda, K. (2019). Reconstructing State-Space from Movie Using Convolutional Autoencoder for Robot Control. In: Strand, M., Dillmann, R., Menegatti, E., Ghidoni, S. (eds) Intelligent Autonomous Systems 15. IAS 2018. Advances in Intelligent Systems and Computing, vol 867. Springer, Cham. https://doi.org/10.1007/978-3-030-01370-7_38
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DOI: https://doi.org/10.1007/978-3-030-01370-7_38
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