Environment-Aware Grasp Strategy Planning in Clutter for a Variable Stiffness Hand

This paper deals with the problem of planning grasp strategies on constrained and cluttered scenarios. The planner sequences the objects for grasping by considering multiple factors: (i) possible environmental constraints that can be exploited to grasp an object, (ii) object neighborhood, (iii) capability of the arm, and (iv) confidence score of the vision algorithm. To successfully exploit the environmental constraints, this work uses the CLASH hand, a compliant hand that can vary its passive stiffness. The hand can be softened such that it can comply with the object shape, or it can be stiffened to pierce between the objects in clutter. A stiffness decision tree is introduced to choose the best stiffness setting for each particular scenario. In highly cluttered scenarios, a finger position planner is used to find a suitable orientation for the hand such that the fingers can slide in the free regions around the object. Thus, the grasp strategy planner predicts not only the sequence in which the objects can be grasped, but also the required stiffness of the end effector, and the appropriate positions for the fingers around the object. Different experiments are carried out in the context of grocery handling to test the performance of the planner in scenarios that require different grasping strategies.