Year: 2025
Type: conference-paper
Abstract: The properties of human skin, such as softness, viscosity, and friction, enable precise handling and manipulation of tools for various tasks. One particularly challenging task in robotic manipulation is operating a knife, where most approaches simplify the problem by rigidly attaching the knife to the robot's end-effector. In this work, inspired by the adaptive nature of human skin, we designed a gripper equipped with four tactile sensors embedded in soft silicone pads to predict the torque applied at the knife. This information is used for closed-loop control during a chopping task. Furthermore, we developed a search algorithm to identify the contact point between the knife and the object, facilitating continuous force estimation throughout the cutting process with an LSTM-based model. Our approach introduces tactile sensing as a novel feedback mechanism for robotic chopping, setting it apart from existing methods that rely on adaptive controllers or vision-based object detection algorithms. The integration of force feed-back, informed by tactile sensors, allows for real-time trajectory and force adjustments, substantially improving the efficiency and precision of the chopping process. Experimental results show that the proposed LSTM-based model can effectively map non-linear and noisy tactile data to estimate contact normal forces, making it highly suitable for closed-loop control in dynamic tasks like cutting.
Authors: Ran Xin, Arsen Abdulali, Fumiya Iida, A. Abdulali
Institution: University of Cambridge
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Funder: European Commission
Awards: G434337789, Future Roads Fellowships
OpenAlex ID: W4411020065