Ant Group's Robbyant Open-Sources LingBot-Vision, a 1B Vision Foundation Model

Robbyant, the embodied-AI company within Ant Group, has open-sourced LingBot-Vision, a family of self-supervised Vision Transformers built for dense spatial perception. The weights ship under Apache-2.0 on Hugging Face in four sizes — ViT-giant, ViT-large, ViT-base, and ViT-small — together with a technical report and inference code. Most vision foundation models are trained for semantic invariance: they learn to answer what is in an image while discarding exactly the fine-grained spatial structure — object boundaries, contours, depth discontinuities — that robots and other physically embodied systems depend on.

LingBot-Vision inverts that priority. It treats boundaries as a native pretraining signal rather than a downstream output, and the payoff is a 1B-parameter backbone that matches or surpasses models up to 7× larger on dense spatial tasks, including the 7B DINOv3. LingBot-Vision is a self-supervised pretrained encoder for spatially structured downstream tasks.

The flagship ViT-g/16 has roughly 1.1B parameters and is trained with a new objective called masked boundary modeling on a curated corpus of about 161M images — selected from a 2B web pool — with no human labels, no external edge detectors, and no pretrained backbone to bootstrap from. The training is economical: the corpus is an order of magnitude smaller than DINOv3’s LVD-1689M, and the model consumes less than a third of DINOv3’s training samples. The encoder outputs dense patch-token features intended for frozen readouts.

For deployment at smaller budgets, the flagship is distilled into ViT-L (300M), ViT-B (86M), and ViT-S students that lead dense prediction within their size classes. The method builds on the DINO/iBOT self-distillation paradigm: a teacher — an EMA copy of the student — generates online targets, and the student recovers them from masked views. Standard masked image modeling hides patches at random, ignoring what each patch depicts.

A flat interior patch is cheap to recover from its neighbors; a patch straddling an object boundary carries structure that context alone cannot supply. Boundaries are the least redundant, most informative regions of an image — and random masking treats them like everything else. LingBot-Vision closes that gap with two ideas.

Boundary-forcing. The teacher predicts a dense boundary field online and identifies the boundary-bearing tokens B. These are forced into the student’s masked set on top of the random mask M, giving the combined mask M⁺ = M ∪ B.

Masked tokens are then routed by geometry: boundary tokens receive an explicit geometric target in addition to the semantic self-distillation target, while interior masked tokens keep the standard semantic objective alone. Categorical boundary field. Boundaries are modeled as line segments lifted into a dense field: every nearby pixel stores an attribute vector a(p) = (d, θ, φ¹, φ²) recording its distance to the nearest segment and three angles that locate it.