Dexterous Manipulation Graphs

We propose the Dexterous Manipulation Graph as a tool to address in-hand manipulation and reposition an object inside a robot's end-effector. This graph is used to plan a sequence of manipulation primitives so to bring the object to the desired end pose. This sequence of primitives is translated into motions of the robot to move the object held by the end-effector. We use a dual arm robot with parallel grippers to test our method on a real system and show successful planning and execution of in-hand manipulation

A finger mechanism for adaptive end effectors

This paper presents design and analysis of a rigid link finger, which may be suitable for a number of adaptive end effectors. The design has evolved from an industrial need for a tele-operated system to be used in nuclear environments. The end effector is designed to assist repair work in nuclear reactors during retrieval operation, particularly for the purpose of grasping objects of various shape, size and mass. The work is based on the University of Southampton's Whole Arm Manipulator, which has a special design consideration for safety and flexibility. The paper discusses kinematic issues associated with the finger design, and to the end of the paper specifies the limits of finger operating parameters for implementing control law

Learning to Grasp the Ungraspable with Emergent Extrinsic Dexterity

A simple gripper can solve more complex manipulation tasks if it can utilize the external environment such as pushing the object against the table or a vertical wall, known as "Extrinsic Dexterity." Previous work in extrinsic dexterity usually has careful assumptions about contacts which impose restrictions on robot design, robot motions, and the variations of the physical parameters. In this work, we develop a system based on reinforcement learning (RL) to address these limitations. We study the task of "Occluded Grasping" which aims to grasp the object in configurations that are initially occluded; the robot needs to move the object into a configuration from which these grasps can be achieved. We present a system with model-free RL that successfully achieves this task using a simple gripper with extrinsic dexterity. The policy learns emergent behaviors of pushing the object against the wall to rotate and then grasp it without additional reward terms on extrinsic dexterity. We discuss important components of the system including the design of the RL problem, multi-grasp training and selection, and policy generalization with automatic curriculum. Most importantly, the policy trained in simulation is zero-shot transferred to a physical robot. It demonstrates dynamic and contact-rich motions with a simple gripper that generalizes across objects with various size, density, surface friction, and shape with a 78% success rate. Videos can be found at https://sites.google.com/view/grasp-ungraspable/

Stable Prehensile Pushing: In-Hand Manipulation with Alternating Sticking Contacts

This paper presents an approach to in-hand manipulation planning that exploits the mechanics of alternating sticking contact. Particularly, we consider the problem of manipulating a grasped object using external pushes for which the pusher sticks to the object. Given the physical properties of the object, frictional coefficients at contacts and a desired regrasp on the object, we propose a sampling-based planning framework that builds a pushing strategy concatenating different feasible stable pushes to achieve the desired regrasp. An efficient dynamics formulation allows us to plan in-hand manipulations 100-1000 times faster than our previous work which builds upon a complementarity formulation. Experimental observations for the generated plans show that the object precisely moves in the grasp as expected by the planner. Video Summary -- youtu.be/qOTKRJMx6HoComment: IEEE International Conference on Robotics and Automation 201

On Grasp Quality Measures: Grasp Robustness and Contact Force Distribution in Underactuated and Compliant Robotic Hands

The availability of grasp quality measures is fundamental for grasp planning and control, and also to drive designers in the definition and optimization of robotic hands. This work investigates on grasp robustness and quality indexes that can be applied to power grasps with underactuated and compliant hands. When dealing with such types of hands, there is the need of an evaluation method that takes into account the forces that can be actually controlled by the hand, depending on its actuation system. In this paper, we study the potential contact robustness and the potential grasp robustness (PCR, PGR) indexes. They both consider main grasp properties: contact points, friction coefficient, etc., but also hand degrees of freedom and consequently, the directions of controllable contact forces. The PCR comes directly from the classical grasp theory and can be easily evaluated, but often leads to too conservative solutions, particularly when the grasp has many contacts. The PGR is more complex and computationally heavier, but gives a more realistic, even if still conservative, estimation of the overall grasp robustness, also in power grasps. We evaluated the indexes for various simulated grasps, performed with underactuated and compliant hands, and we analyzed their variations with respect to the main grasp parameters