Physical Intelligence Feature Trails

The release of the openpi repository provides code and model weights for the π0 general-purpose robotic foundation model, enabling fine-tuning for various robots and tasks. It includes checkpoints fine-tuned for specific platforms like ALOHA and DROID, example code for inference and fine-tuning, and a PyTorch port. This release aims to foster community experimentation and development in embodied AI.

The FAST tokenizer, inspired by continuous compression methods like JPEG, compresses raw robot action chunks using Discrete Cosine Transform (DCT) and Byte Pair Encoding (BPE). This allows for a 10x compression of action sequences into dense tokens, enabling faster training (5x) of autoregressive transformer policies for dexterous robotic tasks, achieving similar dexterity to diffusion models. FAST enables generalist policies to zero-shot generalize to new environments and instructions, as demonstrated with the DROID dataset.