Human-inspired force compliant grasping primitives

Abstract

We address the problem of grasping everyday objects that are small relative to an anthropomorphic hand, such as pens, screwdrivers, cellphones, and hammers from their natural poses on a support surface, e.g., a table top. In such conditions, state of the art grasp generation techniques fail to provide robust, achievable solutions due to either ignoring or trying to avoid contact with the support surface. In contrast, when people grasp small objects, they often make use of substantial contact with the support surface. In this paper we give results of human subjects grasping studies which show the extent and characteristics of environment contact under different task conditions. We develop a simple closed-loop hybrid grasping controller that mimics this interactive, contact-rich strategy by a position-force, pre-grasp and landing strategy for finger placement. The approach uses a compliant control of the hand during the grasp and release of objects in order to preserve safety. We conducted extensive robotic grasping experiments on a variety of small objects with similar shape and size. The results demonstrate that our approach is robust to localization uncertainties and applies to many everyday objects.

Modeling compliant grasps exploiting environmental constraints

“…Each of the strategies was tailored to exploit constraints commonly present in real-world grasping problems. Similarly in [2], Kazemi et al reported the results of human subjects grasping studies which show the extent and characteristics of the contact with the environment under different task conditions. A closed-loop hybrid grasping controller that mimics this interactive, contact-rich strategy was also tested using a a compliant 7-DoFs Barrett Whole-Arm Manipulator (WAM).…”

A Computational Framework for Environment-Aware Robotic Manipulation Planning

“…Also recently, both grasp planning algorithms and grasping mechanisms have begun to take advantage of EC [13], albeit not systematically. While sequences of EC exploitation primitives have been shown to be robust and capable [9], [26], there has been no comprehensive research on how to enumerate and describe these primitives or how to sequence them into task-directed manipulation plans.…”

Reinforcement learning for improving imitated in-contact skills

“…Recently, researchers have shown interest in studying in-contact skills. In-contact tasks which have been learned from demonstration include cleaning a vertical surface [2], controlling stiffness [3], ball-in-box [4], pouring drink [4], box pulling [5], flipping task [5], stapling [6], and grasping small objects [7]. However, all these studies aim at learning a policy merely to reproduce the demonstrated force.…”