Cognitive Grasping and Manipulation of Unknown Object with Control Grip Force using Cyber Physical System Approach

Abstract

Service robots working in human environments must have the ability to grasp a wide variety of unseen objects with ability to grasp a wide variety of unseen objects with appropriate grip force without knowing their properties and its response to action in a human environment. This research uses a novel Cyber Physical System framework to estimate and apply minimum grasping force for unknown objects under a task motion using only tactile motion data captured from sensor-less acceleration-based controlled common gripper. The goal is to use this novel framework for developing functions for position-controlled robots to perform real-time cognitive grasping and enable quick implementation across multiple robots. This paper uses soft sensing inputs from the earlier research to characterize unknown object properties like stiffness, mass and surface interaction and show how a data-based analytics algorithm learns a wide range of object properties and slip status along with task action dynamics features, to propose the grasping action with minimum applied force. We validated that the minimum grasping force is able to hold the object firmly during any task motion. In order to improve the learning, the paper proposes a part of the framework to use virtual simulation to gather more learning data and show results that compare well between simulated results with experimental data. We use the simulated data to train Reinforcement Learning approach and show that small variation in object width can be learnt to identify optimum gripping position.