Robbyant launches LingBot-Depth 2.0 to halve depth sensing errors for robots

Robbyant's release of LingBot-Depth 2.0 and LingBot-Vision addresses a long-standing challenge in robotics: enabling machines to perceive and navigate complex, real-world environments with the same robustness humans take for granted. The predecessor LingBot-Depth pioneered the Masked Depth Modeling technique to handle transparent and reflective surfaces, but the new version represents a substantial scale-up—trained on 150 million samples rather than its predecessor's lesser volume—allowing it to cut error rates in half for demanding indoor scenarios where depth loss is severe.

The pairing of LingBot-Depth 2.0 with LingBot-Vision reflects a complementary approach: depth sensing needs precise visual boundary information to work reliably, and LingBot-Vision's sub-pixel-level localization and structural understanding provide that foundation. Remarkably, LingBot-Vision achieves this with a training corpus that is an order of magnitude smaller than comparable models, suggesting a more efficient use of data.

The commercial partnership with Orbbec signals that Robbyant views this as infrastructure for the broader robotics and embodied AI ecosystem rather than a proprietary advantage. By open-sourcing LingBot-Vision weights and collaborating on SDK and camera products, the company is positioning itself as a foundational layer for robotics companies, similar to how computer vision libraries became critical utilities. The certification by Orbbec's Depth Vision Laboratory and planned integration into real products by year-end suggest this is moving beyond research into deployable commercial systems.