Action-Conditional Recurrent Kalman Networks For Forward and Inverse Dynamics Learning

TL;DR

This paper introduces action-conditional recurrent Kalman networks that improve forward and inverse dynamics modeling for complex robots, outperforming existing neural and analytical models in accuracy and real-world robot applications.

Contribution

The paper presents two novel RKN-based architectures for forward and inverse dynamics learning, enhancing prediction accuracy and robustness over prior models.

Findings

RKN architectures outperform standard recurrent networks like LSTMs.

Both models significantly outperform existing frameworks and analytical models.

Models achieve high accuracy on various real robot dynamics.

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.