Robbyant, an embodied AI company under Ant Group, has open-sourced LingBot-Touch, a tactile foundation model designed to give robots advanced touch perception.

Pre-trained on nearly 5,000 hours of real-world tactile data, the model supports three sensing modalities: magnetic tactile, visual tactile, and piezoresistive sensing.

It converts signals from these different sensors into a unified tactile representation, allowing different robotic hands and action models to share and reuse touch-related information.

The company says this capability could improve fine manipulation tasks, including turning pages, unscrewing bottle caps, and identifying Mahjong tiles.

LingBot-Touch is an open-source tactile foundation model designed to help robots interpret physical contact across different types of sensors. The model addresses a major challenge in robotic manipulation: enabling robots to retain and reuse touch perception capabilities when switching between sensors, dexterous hands, and control models.

Commercial tactile sensors currently use different technologies, including magnetic tactile, visual tactile, and piezoresistive sensing. Each approach has distinct advantages, limitations, costs, and application scenarios, making it unlikely that a single sensing technology will dominate the market.

However, these different sensing principles generate incompatible raw signals, even when detecting the same physical contact. Models trained to interpret data from one sensor type may struggle to transfer that knowledge to another, requiring developers to collect new datasets, retrain models, and adapt their systems whenever the hardware changes.

LingBot-Touch attempts to overcome this limitation by translating heterogeneous tactile signals into a shared representation. Its architecture uses separate encoding pathways to process data from different sensing technologies before mapping contact points from different sensor layouts into a common fingertip coordinate space.

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