Object Shape Recognition Using Sparse Soft Capacitive Tactile Sensors for Robotic Hands

Abstract

Reliable tactile object‑shape recognition on robotic hands is often achieved using dense sensor arrays or vision‑based tactile skins, which increase fabrication complexity and computational cost. This work demonstrates that high recognition performance can instead be achieved through principled sparse sensing. A minimal multimodal tactile system is developed by fusing soft capacitive stretch sensors at finger PIP joints with a compact six‑element palmar pressure array, integrated on a human‑like hand mechanically constrained to emulate robotic grasping. Using an ANOVA‑based channel selection, low‑informative metacarpophalangeal signals are identified and removed, reducing the sensing configuration from 16 to 11 channels while improving classification accuracy. A lightweight multilayer perceptron operating on this low‑dimensional input achieves 95.4% size‑invariant recognition accuracy across 12 rigid objects representing four geometric primitives: cuboid, sphere, cylinder, and cone, outperforming the denser baseline. Ablation studies confirm the complementary roles of finger‑joint deformation, which encodes curvature cues, and palmar force distribution, which captures contact topology; neither modality alone achieves comparable performance. Beyond accuracy, the proposed design reduces sensor count, wiring, and computational requirements, enabling embedded‑ready deployment. The results show that data‑driven sensor placement, rather than sensor density, can yield robust grasp‑based shape recognition, offering practical guidance for tactile perception in resource‑constrained robotic hands.