LingBot-VA 2.0: 原生控制视频动作基础模型

Most video-action robot models are a content-creation video generator with an action module attached. LingBot-VA 2.0 from @robbyant_brain, a video-action foundation model, throws that starting point out and trains the whole stack natively for control. And it runs closed-loop at a peak 225 Hz. It's so important because A robot cannot move responsively when its controller pauses to imagine the next few frames. LingBot-VA 2.0 predicts during execution, then corrects using each real observation. And it carries only about 13B video parameters while activating roughly 1.9B per token. Bigger robot models usually mean slower reactions, creating a direct conflict between intelligence and control. LingBot-VA 2.0 is trained from scratch for robot control rather than adapted from a video generator built for content creation. Robbyant, an embodied AI company under Ant Group, built it to learn how scenes change under actions, predict what should happen next, and turn those predictions into real-time robot movements. Most video-action systems inherit a tokenizer and video backbone trained mainly to reproduce visual appearance. LingBot-VA 2.0 rebuilds both parts around physical control. Its semantic visual-action tokenizer maps observations toward features from a frozen vision foundation model and learns compact latent actions from frame-to-frame changes using self-supervised inverse and forward dynamics. Unlabeled web video can therefore carry action-relevant training signals without robot action labels. The policy is causal from the start, so every prediction can use only past observations. Its sparse Mixture-of-Experts video backbone has about 13B total parameters, while about 1.9B are active per token, keeping the compute lower during each step. A high-level vision-language planner breaks long tasks into smaller instructions, while the low-level video-action policy handles continuous movement. Foresight Reasoning predicts future visual states while the robot is already acting, then replaces imagined states with every new real observation. Combined with few-step distillation and systems acceleration, the paper reports a peak asynchronous execution frequency of 225 Hz. The model adapts from 10–15 demonstrations, transfers across robot embodiments, and handles some new tasks zero-shot. In the paper’s own evaluations, it reaches 93.6 average on RoboTwin 2.0 and reports stronger real-world results than LingBot-VA and π0.5 across the tested tasks. 🧵 1. https://video.twimg.com/amplify_video/2076806534903042048/vid/avc1/1920x1080/2NQQEo3tPamYqXah.mp4?tag=28 https://video.twimg.com/amplify_video/2076806534890467328/vid/avc1/1920x822/hm7fNZNa_1KRTIoz.mp4?tag=28 https://video.twimg.com/amplify_video/2076806534919860224/vid/avc1/1920x1080/IrSGlYqBxDcJV3e5.mp4?tag=28 Rohan Paul (@rohanpaul_ai): 📄 Paper: https://t.co/fDpfoU3vqZ The big deal is that the model learns control-friendly world knowledge before task training, rather than forcing a generic video generator to become a robot controller later. Overall, LingBot-VA 2.0 links video, actions, causality, planning, and execution within one control-focused pretraining design.