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Action conditional recurrent Kalman networks for forward and inverse dynamics learning

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Action conditional recurrent Kalman networks for forward and inverse dynamics learning. / Shaj, V.; Becker, P.; Buchler, D.; Pandya, H.; van Duijkeren, N.; Taylor, C. James; Hanheide, M.; Neumann, G.

Proceedings of Machine Learning Research. 2020.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Shaj, V, Becker, P, Buchler, D, Pandya, H, van Duijkeren, N, Taylor, CJ, Hanheide, M & Neumann, G 2020, Action conditional recurrent Kalman networks for forward and inverse dynamics learning. in Proceedings of Machine Learning Research. 4th Conference on Robot Learning , Boston, United States, 16/11/20. <https://arxiv.org/abs/2010.10201>

APA

Shaj, V., Becker, P., Buchler, D., Pandya, H., van Duijkeren, N., Taylor, C. J., Hanheide, M., & Neumann, G. (2020). Action conditional recurrent Kalman networks for forward and inverse dynamics learning. In Proceedings of Machine Learning Research https://arxiv.org/abs/2010.10201

Vancouver

Shaj V, Becker P, Buchler D, Pandya H, van Duijkeren N, Taylor CJ et al. Action conditional recurrent Kalman networks for forward and inverse dynamics learning. In Proceedings of Machine Learning Research. 2020

Author

Shaj, V. ; Becker, P. ; Buchler, D. ; Pandya, H. ; van Duijkeren, N. ; Taylor, C. James ; Hanheide, M. ; Neumann, G. / Action conditional recurrent Kalman networks for forward and inverse dynamics learning. Proceedings of Machine Learning Research. 2020.

Bibtex

@inproceedings{d3a0e1aaa643498b83eff49f77abb8db,
title = "Action conditional recurrent Kalman networks for forward and inverse dynamics learning",
abstract = "Estimating accurate forward and inverse dynamics models is a crucial component of model-based control for sophisticated robots such as robots driven by hydraulics, artificial muscles, or robots dealing with different contact situations. Analytic models to such processes are often unavailable or inaccurate due to complex hysteresis effects, unmodelled friction and stiction phenomena, and unknown effects during contact situations. A promising approach is to obtain spatio-temporal models in a data-driven way using recurrent neural networks, as they can overcome those issues. However, such models often do not meet accuracy demands sufficiently, degenerate in performance for the required high sampling frequencies and cannot provide uncertainty estimates.We adopt a recent probabilistic recurrent neural network architecture, called Recurrent Kalman Networks (RKNs), to model learning by conditioning its transition dynamics on the control actions. RKNs outperform standard recurrent networks such as LSTMs on many state estimation tasks. Inspired by Kalman filters, the RKN provides an elegant way to achieve action conditioning within its recurrent cell by leveraging additive interactions between the current latent state and the action variables.We present two architectures, one for forward model learning and one for inverse model learning. Both architectures significantly outperform existing model learning frameworks as well as analytical models in terms of prediction performance on a variety of real robot dynamics models.",
keywords = "Recurrent Networks, Forward Dynamics Learning, Inverse Dynamics Learning, Action-Conditioning, Soft Robots",
author = "V. Shaj and P. Becker and D. Buchler and H. Pandya and {van Duijkeren}, N. and Taylor, {C. James} and M. Hanheide and G. Neumann",
year = "2020",
month = nov,
day = "18",
language = "English",
booktitle = "Proceedings of Machine Learning Research",
note = "4th Conference on Robot Learning , CoRL 2020 ; Conference date: 16-11-2020 Through 18-11-2020",

}

RIS

TY - GEN

T1 - Action conditional recurrent Kalman networks for forward and inverse dynamics learning

AU - Shaj, V.

AU - Becker, P.

AU - Buchler, D.

AU - Pandya, H.

AU - van Duijkeren, N.

AU - Taylor, C. James

AU - Hanheide, M.

AU - Neumann, G.

PY - 2020/11/18

Y1 - 2020/11/18

N2 - Estimating accurate forward and inverse dynamics models is a crucial component of model-based control for sophisticated robots such as robots driven by hydraulics, artificial muscles, or robots dealing with different contact situations. Analytic models to such processes are often unavailable or inaccurate due to complex hysteresis effects, unmodelled friction and stiction phenomena, and unknown effects during contact situations. A promising approach is to obtain spatio-temporal models in a data-driven way using recurrent neural networks, as they can overcome those issues. However, such models often do not meet accuracy demands sufficiently, degenerate in performance for the required high sampling frequencies and cannot provide uncertainty estimates.We adopt a recent probabilistic recurrent neural network architecture, called Recurrent Kalman Networks (RKNs), to model learning by conditioning its transition dynamics on the control actions. RKNs outperform standard recurrent networks such as LSTMs on many state estimation tasks. Inspired by Kalman filters, the RKN provides an elegant way to achieve action conditioning within its recurrent cell by leveraging additive interactions between the current latent state and the action variables.We present two architectures, one for forward model learning and one for inverse model learning. Both architectures significantly outperform existing model learning frameworks as well as analytical models in terms of prediction performance on a variety of real robot dynamics models.

AB - Estimating accurate forward and inverse dynamics models is a crucial component of model-based control for sophisticated robots such as robots driven by hydraulics, artificial muscles, or robots dealing with different contact situations. Analytic models to such processes are often unavailable or inaccurate due to complex hysteresis effects, unmodelled friction and stiction phenomena, and unknown effects during contact situations. A promising approach is to obtain spatio-temporal models in a data-driven way using recurrent neural networks, as they can overcome those issues. However, such models often do not meet accuracy demands sufficiently, degenerate in performance for the required high sampling frequencies and cannot provide uncertainty estimates.We adopt a recent probabilistic recurrent neural network architecture, called Recurrent Kalman Networks (RKNs), to model learning by conditioning its transition dynamics on the control actions. RKNs outperform standard recurrent networks such as LSTMs on many state estimation tasks. Inspired by Kalman filters, the RKN provides an elegant way to achieve action conditioning within its recurrent cell by leveraging additive interactions between the current latent state and the action variables.We present two architectures, one for forward model learning and one for inverse model learning. Both architectures significantly outperform existing model learning frameworks as well as analytical models in terms of prediction performance on a variety of real robot dynamics models.

KW - Recurrent Networks

KW - Forward Dynamics Learning

KW - Inverse Dynamics Learning

KW - Action-Conditioning

KW - Soft Robots

M3 - Conference contribution/Paper

BT - Proceedings of Machine Learning Research

T2 - 4th Conference on Robot Learning

Y2 - 16 November 2020 through 18 November 2020

ER -