External Sensor-Less in-Hand Object Position Manipulation for an Under-Actuated Hand Using Data-Driven-Based Methods to Compensate for the Nonlinearity of Self-Locking Mechanism

Abstract

Dexterous manipulation using an under-actuated hand has been a challenging task due to its non-linear dynamical characteristics. For a linkage-based under-actuated hand designed to be used to grasp and manipulate large, heavy, and rigid objects stably, precision grasping is necessary, which makes the task even more difficult to deal with. While approaches based on external sensors have been introduced throughout the years, to create a robotic hand that can be used for various tasks in unstructured environments, this paper takes the standpoint that control techniques that do not fully depend on utilizing additional sensing elements need to be further developed. This paper applies the hybrid method using analytics models and data-driven-based approaches to analyze internal sensors' data during the operation of the robot and introduces novel data-driven-based techniques to compensate for the limitations of controlling a linkage-based under-actuated hand with a self-locking mechanism. Then, a within-hand object position manipulation framework with proposed methodologies is presented and experimented with to show its effectiveness.