Detecting Grasp Phases and Adaption of Object-Hand Interaction Forces of a Soft Humanoid Hand Based on Tactile Feedback

Abstract

Engineering humanoid robot hands with the ability to dexterously grasp objects of different sizes, shapes, mate-rial properties and weights requires sophisticated tactile sensing and intelligent controllers able to interpret sensory information and adapt contact forces with the object to achieve a stable and safe grasp. In this paper, we present a new soft humanoid hand equipped with a multimodal sensor system in each finger and a human-inspired grasp-phases controller that is able to detect the different phases of a grasping and manipulation task, adapt interaction forces with the manipulated object and balance the force distribution in both precision and power grasps based on tactile feedback. To evaluate the controller, we conducted experiments with the hand on the humanoid robot ARMAR-6 and 31 different soft and rigid everyday objects and food items with weights ranging from 4.8 g of a paper cup to 1133.8 g of a bottle, different shapes and material properties. The results show that grasping force can be reduced by 65% compared to a naive grasping approach using maximum force for grasping and manipulating both fragile objects without destruction as well as heavy objects.