Robotic object grasping in context of human grasping and manipulation

This paper presents experimental and deductive findings that shed new light on grasp force estimation, which improves robot’s chances to grasp and manipulate the object close to optimum conditions on the first attempt, which in turn improves robot’s object manipulation dexterity.
This paper proposes that object slippage detection in the human hand is not detected based purely on microvibrations sensed by the human skin during incipient slippage but also on load sensing at each finger and movement of fingers relative to each other while holding an object.

Robotic grasping and manipulation controller framework: architecture redevelopment

This paper details the further improvements obtained by redesigning a previously offered Manipulation Controller Framework to provide support to an innovative, friction-based object slippage detection strategy employed by the robotic object manipulator. This upgraded Manipulation Controller Framework includes improved slippage detection functionality and a streamlined architecture designed to improve controller robustness, reliability and speed. Improvements include enhancements to object slippage detection strategy, the removal of the decision making module and integration of its functionality into the Motion Planner, and the stream-lining of the Motion Planner to improve its effectiveness. It is anticipated that this work will be useful to researchers developing integrated robot controller architectures and slippage control.