Robbyant Releases LingBot-VLA 2.0: An Open-Source 6B Vision-Language-Action (VLA) Model for Cross-Embodiment Robot Manipulation

Ant Group’s Robbyant has released LingBot-VLA 2.0 , a Vision-Language-Action (VLA) foundation model for robots. The release includes a technical report, an Apache-2.0 codebase, and a 6B checkpoint. The research team targets a well-known gap: VLA models often work in labs but stumble in deployment. LingBot-VLA 2.0 advances the prior version along three practical axes. These are generalization, an expanded action space, and predictive dynamics modeling.

LingBot-VLA 2.0 is a generalist robot policy built on a vision-language backbone. It converts camera images and a language instruction into robot actions. The public model is lingbot-vla-v2-6b, a 6B ‘native depth’ checkpoint. It uses Qwen3-VL-4B-Instruct as the VLM backbone. Two teacher models, LingBot-Depth and DINO-Video , supervise training through distillation.

One inference call takes about 130 ms on an NVIDIA GeForce RTX 4090D. That measurement uses 10 denoising steps. The action expert uses a Mixture-of-Experts (MoE) design for scaling.

Generalization starts with data. The research team curates roughly 60,000 hours of pre-training data. This covers 50,000 hours of robot trajectories and 10,000 hours of egocentric human videos. The robot data spans 20 robot configurations , from single-arm rigs to full humanoids. The raw pool is larger: about 90,000 robot hours and 20,000 egocentric hours. A redesigned pipeline filters noisy samples down to the high-quality set.

Filtering is explicit and measurable. The research team computes third-order jerk along with velocity and acceleration Z-scores per embodiment. Episodes with abnormal smoothness or over 95% static signals are dropped. Videos are checked against replayed states using each robot’s URDF. Annotators remove blur, occlusion, dropped frames, and multi-view misalignment. Egocentric clips pass a VLM filter, then egocentric SLAM and MANO hand-pose reconstruction.

Annotation is automated with a vision-language model. Qwen3.6-27B segments each video into temporally contiguous subtasks. Each subtask gets an atomic action from a closed vocabulary of 18 categories. That vocabulary holds 15 primitive actions plus transit, idle, and other. Across the corpus, move and transit dominate by frequency.

Different robots expose different joints, so LingBot-VLA 2.0 unifies them. It uses a 55-dimensional canonical vector for both states and actions. The layout is fixed across every embodiment in the dataset.

Each arm end-effector pose uses XYZ coordinates plus a rotation quaternion, giving 7 dimensions per arm. Robots that lack a body part simply pad the corresponding dimensions. This lets one model control arms, hands, grippers, waists, heads, and mobile bases.