Ant Robbyant Open-Sources LingBot-VLA 2.0 and LingBot-Vision: VLA Brain for 20 Robot Morphologies and Space-Native Vision Foundation Model

In June 2025, Ant Robbyant released and open-sourced the next-generation embodied foundation model LingBot-VLA 2.0 and the world's first 'space-native' vision foundation model for embodied AI, LingBot-Vision. LingBot-VLA 2.0 is pre-trained on 50,000 hours of robot trajectory data and 10,000 hours of first-person human manipulation videos (totaling 60,000 hours), supporting 20 robot configurations from 17 manufacturers. Its action space extends from dual arms to full-body degrees of freedom including head, waist, mobile base, and dexterous hands. The model introduces a MoE architecture to handle multi-morphology differences and enhances long-horizon task capabilities through future depth prediction and semantic feature prediction. On the GM-100 multi-task benchmark, LingBot-VLA 2.0 achieves an average progress score/success rate of 66.2/34.4 on the AgileX Cobot Magic platform, outperforming GR00T N1.7 (36.3/17.8) and π0.5 (59.1/32.2); on the Galaxea R1 Pro platform, it achieves 34.6/15.6, also leading. In long-horizon mobile manipulation tasks, under the fridge organization ID setting, LingBot-VLA 2.0 scores 77.1/60.0 vs π0.5's 65.3/46.7; under OOD setting, 37.0/13.3 vs 30.3/6.7. For stove cleaning ID setting, 84.3/66.7 vs 79.9/60.0; OOD setting, 67.5/40.0 vs 62.5/33.3.

LingBot-Vision is a ViT-g/16 model with approximately 1.1B parameters, employing 'Boundary-centric Masked Modeling' that forces the model to learn object boundaries and geometric structures during pre-training, rather than random masking. It uses only 161 million images for training (compared to DINOv3's 1.689 billion), with less than one-third of DINOv3's training iterations. On NYUv2 depth estimation, LingBot-Vision achieves an RMSE of 0.296, outperforming the 7B-parameter DINOv3's 0.309; on KITTI, it is the strongest among models under 2B parameters. The distilled 0.3B ViT-L model matches the 7B DINOv3 on NYUv2, with a parameter count difference of about 23x. LingBot-Depth 2.0, based on LingBot-Vision, leads on 12 depth completion benchmarks, especially excelling in transparent, reflective, small object, long-range, and complex indoor scenes. Ant Robbyant has partnered with Orbbec to launch EGO-RGBD data acquisition devices, SDK integration, and all-in-one cameras.

LingBot-VLA 2.0's model weights, training code, and technical report are open-sourced (Hugging Face, ModelScope, GitHub), and LingBot-Vision is also open-sourced simultaneously. LingBot-Depth 2.0 is not open-sourced for now, serving as a commercial capability for the industry.