Fig. 4: Comprehensive tactile sensing capabilities of F-TAC Hand.
From: Embedding high-resolution touch across robotic hands enables adaptive human-like grasping
a–d, Raw tactile sensor readings from the configuration shown in Fig. 1a (a) are processed by neural networks (b) to reconstruct contact site geometries (c), visualized as normal maps. The neural networks, trained on simulated data (d) generated by a physics-based image formation model, enable efficient and precise mapping of extensive raw data to geometric information at the contact interface. e,f, When grasping an object (e), F-TAC Hand captures detailed contact information through its advanced tactileer sensing capabilities (f). g, This rich tactile feedback enables F-TAC Hand to accurately perceive and interpret object characteristics, as demonstrated by its precise estimation of in-hand object poses.
