Quantization-Free Autoregressive Action Transformer

Understand

Current transformer-based imitation learning approaches introduce discrete action representations and train an autoregressive transformer decoder on the resulting latent code.

• However, the initial quantization breaks the continuous structure of the action space thereby limiting the capabilities of the generative model.
• We propose a quantization-free method instead that leverages Generative Infinite-Vocabulary Transformers (GIVT) as a direct, continuous policy parametrization for autoregressive transformers.
• This simplifies the imitation learning pipeline while achieving state-of-the-art performance on a variety of popular simulated robotics tasks.