Action-Conditional Recurrent Kalman Networks For Forward and Inverse
Dynamics Learning
- URL: http://arxiv.org/abs/2010.10201v2
- Date: Thu, 5 Nov 2020 20:58:23 GMT
- Title: Action-Conditional Recurrent Kalman Networks For Forward and Inverse
Dynamics Learning
- Authors: Vaisakh Shaj, Philipp Becker, Dieter Buchler, Harit Pandya, Niels van
Duijkeren, C. James Taylor, Marc Hanheide, Gerhard Neumann
- Abstract summary: Estimating accurate forward and inverse dynamics models is a crucial component of model-based control for robots.
We present two architectures for forward model learning and one for inverse model learning.
Both architectures significantly outperform exist-ing model learning frameworks as well as analytical models in terms of prediction performance.
- Score: 17.80270555749689
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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 Re-current
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 exist-ing model learning frameworks as well as analytical models in
terms of prediction performance on a variety of real robot dynamics models.
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