Lingbot VLAs

From Foundation to Deployment: LingBot-VLA v1 and v2 Explained How RobbyAnt's VLA series evolved from a pragmatic dual-arm foundation model to a whole-body, multi-embodiment system built for the real world. --- Vision-Language-Action (VLA) models are rapidly becoming the backbone of generalist robot policies. They combine the semantic richness of large vision-language models (VLMs) with action generation modules, enabling robots to interpret natural language instructions and translate them into physical manipulation. But turning laboratory-grade VLA research into something that works reliably across diverse robots, in diverse environments, is a much harder problem. LingBot-VLA is RobbyAnt's answer to that challenge. Released in two iterations, the model series traces a clear arc: from a pragmatically scaled dual-arm foundation model (v1) to a whole-body, cross-embodiment system with richer action coverage and predictive scene understanding (v2). This post walks through both versions — what they do, how they're built, and what changed. --- LingBot-VLA v1: Building the Foundation The Core Idea The central question driving LingBot-VLA v1 was straightforward: how do VLA models truly scale with massive real-world robot data? Rather than focusing on simulation or synthetic data, the team committed to large-scale real-world teleoperation data across multiple robotic platforms and studied whether success rates kept improving as data volume grew. The answer was a clear yes — and,…