The Blog

π₀ is an innovative VLA model that combines a vision–language backbone with an action expert module and flow matching, producing continuous action sequences from natural language and images.

Diffusion-based models such as DDPM and their use in policy learning rely on denoising mechanisms, UNet architectures, and structured action representations to capture complex sequential behaviors.

OpenVLA is a 7B open-source VLA model built on Llama2 + DINOv2 + SigLIP, trained on 970k demos, achieving stronger generalization and robustness than closed RT-2-X (55B) and outperforming Diffusion Policy.

Action Chunking with Transformers (ACT) combines the representational strength of autoencoders with the contextual modeling of transformers, producing compact latent variables that generate coherent action sequences.

Automatic control adjusts a system’s input so that its output follows a desired reference, ensuring stability, precision, and robustness across engineering applications.

Compensation techniques such as PD, Lead, PI, and Lag are essential in control engineering. They shape transient response, steady-state error, and stability margins by modifying the frequency characteristics of systems.