**Meet LingBot-VLA: Revolutionizing Real-World Robotics**
Hey fellow tech enthusiasts! Today I’m super excited to share with you a major breakthrough in robotics – meet LingBot-VLA, a cutting-edge Vision Language Motion (VLA) foundation model developed by Ant Group’s Robbyant. This tech is going to change the game for robots performing complex tasks in real-life scenarios!
**Data-Packed and Diverse**
So what makes LingBot-VLA so special? It’s all about the data. The team has collected an astonishing 20,000 hours of teleoperated bimanual data from 9 different robotic embodiments. Each robot is equipped with dual 6 or 7-degree-of-freedom arms and multiple RGB-D cameras, giving the model unparalleled views of the environment. This diversity of data lets LingBot-VLA learn a wide range of scenarios and adapt to new situations.
**The Transformer Powerhouse**
At the heart of LingBot-VLA is a powerful Combination of Transformers architecture. It combines a vision language spine with an action expert to encode visual inputs, natural language instructions, and robotic proprioceptive state. The model even shares a self-attention module for joint sequence modeling – talk about depth!
**Adding Depth with LingBot-Depth**
But here’s the thing: LingBot-VLA doesn’t just stop at vision. It’s got a special trick up its sleeve – it integrates LingBot-Depth, a spatial perception model that uses Masked Depth Modeling to align visual queries with depth tokens. This means it can better understand 3D spaces and perform tasks like insertion, stacking, and folding with ease.
**Real-World Results: GM-100 Benchmark**
The researchers tested LingBot-VLA on the GM-100 real-world benchmark, a comprehensive test bed with 100 manipulation tasks on three hardware platforms. And the results? Impressive! LingBot-VLA outperforms π0.5, GR00T N1.6, and WALL-OSS with a mean Success Rate of 17.30% and Progress Rating of 35.41%.
**Scalability and Efficiency**
But that’s not all – the team also explored how LingBot-VLA scales with more data. It turns out that both Success Rate and Progress Rate improve as the dataset size increases, and it doesn’t even plateau at the largest scale studied. Plus, LingBot-VLA demonstrates high data efficiency, outperforming π0.5 with just 80 demonstrations per task.
**Open-Source and Efficient**
To top it all off, the LingBot-VLA codebase is open-source and super efficient, achieving a throughput of 261 samples per second per GPU on an 8-GPU setup. This uses techniques like FSDP fashion, hybrid sharding, blended precision, and operator-level acceleration – essentially, it’s a masterclass in optimization!
**The Bottom Line**
LingBot-VLA is a Qwen2.5-VL-based VLA foundation model with 20,000 hours of real-world data and LingBot-Depth integration. It outperforms other models on the GM-100 benchmark and shows impressive scalability and data efficiency. Stay tuned for more robotics and AI updates by following us on Twitter and joining our 100k+ ML SubReddit – and don’t forget to subscribe to our Newsletter!
