Tactile Object Recognition with Recurrent Neural Networks through a Perceptive Soft Gripper
Enrico Donato, David Pelliccia, Matin Hosseinzadeh, Mahmood Amiri, Egidio Falotico
Abstract
Soft robot perception integrates information from distributed, multi-modal sensors, broadening their application to active interaction. Our work introduces recurrent learning models for tactile-based object recognition, demonstrating comparable performance in virtual and real-world scenarios. The work focuses on soft grippers, which facilitate adaptation to objects of varying shapes and sizes thanks to passive finger compliance. Our model successfully identifies over sixteen heterogeneous objects. Findings underscore the significance of sensory multi-modality over single. We highlight how spatial distribution and sensory signal dynamics influence overall estimation accuracy, and which is the minimal grasp set to achieve certain recognition.