Ant Group's Robbyant Releases LingBot-VA 2.0, a Causal Video-Action Model for Physical AI

Ant Group's robotics division Robbyant has unveiled LingBot-VA 2.0, a model architecture described as a causal video-action model built natively for physical AI applications. Unlike transformer models adapted from language or vision tasks, the causal video-action framing implies the model reasons over temporal sequences of visual observations to produce grounded physical actions, a design choice intended to improve generalization in real-world robotic settings. This is part of a broader industry push — alongside efforts from Physical Intelligence, Google DeepMind, and others — to develop foundation models that can be deployed across diverse robot embodiments. For developers and researchers working on robotics, autonomous systems, or embodied AI, LingBot-VA 2.0 represents a concrete new architecture to evaluate against existing video-based policy models. The release from Ant Group also signals that Chinese tech conglomerates are investing seriously in physical AI as a competitive frontier.