  Transformer has shown promise in reinforcement learning to model time-varying
features for obtaining generalized low-level robot policies on diverse robotics
datasets in embodied learning. However, it still suffers from the issues of low
data efficiency and high inference latency. In this paper, we propose to
investigate the task from a new perspective of the frequency domain. We first
observe that the energy density in the frequency domain of a robot's trajectory
is mainly concentrated in the low-frequency part. Then, we present the Fourier
Controller Network (FCNet), a new network that uses Short-Time Fourier
Transform (STFT) to extract and encode time-varying features through frequency
domain interpolation. In order to do real-time decision-making, we further
adopt FFT and Sliding DFT methods in the model architecture to achieve parallel
training and efficient recurrent inference. Extensive results in both simulated
(e.g., D4RL) and real-world environments (e.g., robot locomotion) demonstrate
FCNet's substantial efficiency and effectiveness over existing methods such as
Transformer, e.g., FCNet outperforms Transformer on multi-environmental
robotics datasets of all types of sizes (from 1.9M to 120M). The project page
and code can be found https://thkkk.github.io/fcnet.
