π0-FAST正式集成到LeRobot中！pytorch版本来了

Core Viewpoint - The article discusses the introduction of π0-FAST, a new model by the pi team that integrates visual language model capabilities with FAST (Frequency Domain Action Sequence Tokenization) action encoding technology, significantly improving training speed and precision for complex robotic tasks [1][4]. Group 1 - π0-FAST enhances the training of high-precision operational tasks, achieving a training speed increase of up to 5 times compared to traditional diffusion model methods [1]. - The model addresses the limitations of traditional action encoding methods, which struggle with complex dexterous skill tasks requiring precise control and high-frequency response [3]. - The implementation of π0-FAST has been integrated into the LeRobot framework, which now supports multiple models including π0, π0.5, and π0-FAST, as well as the domestic model WALL-OSS [2][7]. Group 2 - The original π0-FAST implementation was based on the JAX framework, but it has been restructured using PyTorch, incorporating cross-entropy loss objectives, FAST tokenization schemes, and inference optimization techniques such as KV caching [6]. - π0-FAST generates dense action token sequences that can be predicted in a self-regressive manner, aligning its prediction method with that of language tokens, thus solving the challenges faced by traditional methods [4].