Summary of Fourier Controller Networks For Real-time Decision-making in Embodied Learning, by Hengkai Tan et al.
Fourier Controller Networks for Real-Time Decision-Making in Embodied Learning
by Hengkai Tan, Songming Liu, Kai Ma, Chengyang Ying, Xingxing Zhang, Hang Su, Jun Zhu
First submitted to arxiv on: 30 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Fourier Controller Network (FCNet) tackles the limitations of Transformer-based reinforcement learning models in robotics by exploiting the frequency domain. By observing that robot trajectories have low-frequency energy density, FCNet uses Short-Time Fourier Transform (STFT) and frequency domain interpolation to encode time-varying features. This approach enables parallel training and efficient recurrent inference through FFT and Sliding DFT methods. Experimental results on simulated (D4RL) and real-world environments demonstrate FCNet’s superiority over Transformer in terms of efficiency and effectiveness, outperforming it on various multi-environmental robotics datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers develop a new model called Fourier Controller Network (FCNet) to improve reinforcement learning for robots. They found that the frequency domain can be useful in understanding robot movements and developed a way to extract important features from there. This helps with training the model faster and making better decisions in real-time. The results show that FCNet is more efficient and effective than another popular model, Transformer. |
Keywords
» Artificial intelligence » Inference » Reinforcement learning » Transformer
