prosthetic device manufacturers

Abstract: This paper proposes a refinement to existing vision-based portable pipelines for the recognition of graspable object parts. Such pipelines can effectively support automatic grasp selection and preshaping in upper-limb prosthetic devices. However, realistic scenarios in which multiple objects are present still represent a challenge. The proposed method exploits a minor modification to the conventional hardware setup and a custom training approach to extend the applicability of vision-based pipelines. A laser pointer is introduced in the setup as an effective mechanism for object disambiguation. Accordingly, a suitable retraining of the deep convolutional neural networks supporting recognition of graspable parts is implemented. A mixed reality strategy allows the reuse of available high-quality segmentation masks to generate realistic images procedurally, extending the range of scenarios supported by the final device. Experimental results confirm that the proposed pipeline improves target disambiguation accuracy by up to 9% compared with existing solutions, while preserving the computational cost.

Abstract: Vision-based tactile sensors (VBTS) provide rich contact information for robotic manipulation, but existing designs can be hard to simplify and adapt to the size and constraints of humanoid fingertips. We introduce \textbf{GlowTact}, a pressure-responsive vision-based tactile sensing mechanism that directly visualizes contact pressure. \textbf{GlowTact} requires only single-color, non-directional illumination, and the raw tactile image directly represents the pressure distribution without explicit geometry reconstruction. This simple sensing principle enables compact, customizable tactile sensors while preserving high sensitivity and rich spatial detail. We demonstrate gram-scale contact detection, accurate normal-force estimation, and reconstruction of fine contact geometry, including M1 screw threads. These results establish \textbf{GlowTact} as a practical new sensing technology for compact humanoid fingertips, combining a durable nitrile membrane and simple optical design with sensitive and information-rich tactile perception.