Sigma: The Key for Vision-Language-Action Models toward Telepathic Alignment

Understand

To address a fundamental limitation in cognitive systems, namely the absence of a time-updatable mediating thought space between semantics and continuous control, this work constructs and trains a vision-language-action model termed Sigma, deployed on a single RTX 4090.

• The model is built upon the open-source pi0.5_base backbone, with the svla_so101_pickplace dataset preprocessed into a structured training corpus.
• An independently designed VLA architecture is introduced to integrate deep semantic understanding with associative reasoning, enabling telepathic-style alignment between perception and action.
• Training proceeds through iterative optimization of data preprocessing, LoRA-based fine-tuning, and inference-stage adapter design.