FEWT: Improving Humanoid Robot Perception with Frequency-Enhanced Wavelet-based Transformers

--The embodied intelligence bridges the physical world and information space. As its typical physical embodiment, humanoid robots have shown great promise through robot learning algorithms in recent years. In this study, a hardware platform, including humanoid robot and exoskeleton -style teleoperation cabin, was developed to realize intuitive remote manipulation and efficient collection of anthropomorphic action data. To improve the perception representation of humanoid robot, an imitation l earning framework, termed Frequency-Enhanced Wavelet -based Transformer (FEWT), was proposed, which consists of two primary modules: Frequency-Enhanced Efficient Multi -Scale Attention (FE-EMA) and Time -Series Discrete Wavelet Transform (TS-DWT). This fusion is able to capture feature information across various scales effectively, thereby enhancing model robustness. Experimental performance demonstrates that FEWT improves the success rate of the state -of-the -art algorithm (Action Chunking with Transformers, ACT baseline) by up to 30% in simulation and by 6-12% in real -world . In the field of imitation learning [1], humanoid robot learning effectiveness is usually determined by the quality and diversity of expert demonstrations and the perception representations.