Sigma: The Key for Vision-Language-Action Models toward Telepathic Alignment
This provides reproducible experience for semantic alignment and intention-driven behavior in humanoid robots, though it appears incremental as it builds on an existing base model.
This study developed Sigma, a vision-language-action model for humanoid robots that addresses the lack of a time-updatable mediating thought space between semantics and continuous control, achieving stable decreases in control MSE across vector, fragment, and entire trajectory timescales while maintaining telepathy norm and semantic-text alignment quality.
To address the gap in humanoid robot cognitive systems regarding the lack of a time-updable mediating thought space between semantics and continuous control, this study constructs and trains a VLA model named "Sigma" that runs on a single RTX 4090. It uses the open-source pi05_base model as a foundation and preprocesses svla_so101_pickplace into a training dataset. The researcher independently designed an architecture for a vision-language-action model that combines deep semantic understanding and association to achieve telepathic communication. The training process involved repeated optimizations of data preprocessing, LoRA fine-tuning, and the inference-stage adapter. The experiment employed offline closed-loop replay, comparing Sigma with the untuned pure pi05_base_base model under data conditions. Results showed that Sigma exhibited a stable decrease in control MSE across vector, fragment, and entire trajectory timescales, while maintaining the telepathy norm and semantic-text alignment quality unchanged. It demonstrates that mind-responsive alignment control is quantified through an architecture that combines deep understanding of semantics and association without retraining the base model, which provides reproducible experience for semantic alignment and intention-driven behavior in humanoid robots.