Extrinsic Dexterity: In-Hand Manipulation with External Forces

Abstract — “In-hand manipulation ” is the ability to reposition an object in the hand, for example when adjusting the grasp of a hammer before hammering a nail. The common approach to in-hand manipulation with robotic hands, known as dexterous manipulation [1], is to hold an object within the fingertips of the hand and wiggle the fingers, or walk them along the object’s surface. Dexterous manipulation, however, is just one of the many techniques available to the robot. The robot can also roll the object in the hand by using gravity, or adjust the object’s pose by pressing it against a surface, or if fast enough, it can even toss the object in the air and catch it in a different pose. All these techniques have one thing in common: they rely on resources extrinsic to the hand, either gravity, external contacts or dynamic arm motions. We refer to them as “extrinsic dexterity”. In this paper we study extrinsic dexterity in the context of regrasp operations, for example when switching from a power to a precision grasp, and we demonstrate that even simple grippers are capable of ample in-hand manipulation. We develop twelve regrasp actions, all open-loop and handscripted, and evaluate their effectiveness with over 1200 trials of regrasps and sequences of regrasps, for three different objects (see video [2]). The long-term goal of this work is to develop a general repertoire of these behaviors, and to understand how such a repertoire might eventually constitute a general-purpose in-hand manipulation capability. I

A qualitative test for N-finger force-closure grasps on planar objects with applications to manipulation and finger gaits

This paper presents a force-closure test function for an n-finger grasp on a planar object with friction. All n-finger grasps can be represented by an n-dimensional contact space. The critical conditions of the test function are used to define force-closure curves which are the boundaries of force-closure sets in the contact configuration space. We show that the force closure sets can be decomposed into subsets in which m (m < n) fingers satisfy force closure. We also prove that m = 6 is an upper bound on the order of the force closure subsets. These subsets are required for planning finger gait maneuvers which are force-closure in all phases of the gait. The characteristics of these subsets are discussed, and an algorithm to enumerate them is given. The application of the test function and the contact configuration space formulation to multifinger object manipulation and finger gait planning is demonstrated by an example

Experimental Validation of Contact Dynamics for In-Hand Manipulation

This paper evaluates state-of-the-art contact models at predicting the motions and forces involved in simple in-hand robotic manipulations. In particular it focuses on three primitive actions --linear sliding, pivoting, and rolling-- that involve contacts between a gripper, a rigid object, and their environment. The evaluation is done through thousands of controlled experiments designed to capture the motion of object and gripper, and all contact forces and torques at 250Hz. We demonstrate that a contact modeling approach based on Coulomb's friction law and maximum energy principle is effective at reasoning about interaction to first order, but limited for making accurate predictions. We attribute the major limitations to 1) the non-uniqueness of force resolution inherent to grasps with multiple hard contacts of complex geometries, 2) unmodeled dynamics due to contact compliance, and 3) unmodeled geometries dueto manufacturing defects.Comment: International Symposium on Experimental Robotics, ISER 2016, Tokyo, Japa

Segmented capacitance tomography electrodes: a design and experimental verifications

A segmented capacitance tomography system for real-time imaging of multiphase flows is developed and pre-sented in this work. The earlier research shows that the electrical tomography (ECT) system is applicable in flow visualization (image reconstruction). The acquired concentration profile ob-tained from capacitance measurements able to imaged liquid and gas mixture in pipelines meanwhile the system development is designed to attach on a vessel. The electrode plates which act as the sensor previously has been assembled and fixed on the pipeline, thus it causes obscurity for the production to have any new process installation in the future. Therefore, a segmented electrode sensor offers a new design and idea on ECT system which is portable to be assembled in different diameter sizes of pipeline, and it is flexible to apply in any number due to different size of pipeline without the need of redesigning the sensing module. The new ap-proach of this sensing module contains the integration intelligent electrode sensing circuit on every each of electrode sensors. A microcontroller unit and data acquisition (DAQ) system has been integrated on the electrode sensing circuit and USB technology was applied into the data acquisition system making the sensor able to work independently. Other than that the driven guard that usually placed between adjacent measuring electrodes and earth screen has been embedded on the segmented electrode sensor plates. This eliminates the cable noise and the electrode, so the signal conditioning board can be expanded according to pipe diameter

Dexterous manipulation of unknown objects using virtual contact points

The manipulation of unknown objects is a problem of special interest in robotics since it is not always possible to have exact models of the objects with which the robot interacts. This paper presents a simple strategy to manipulate unknown objects using a robotic hand equipped with tactile sensors. The hand configurations that allow the rotation of an unknown object are computed using only tactile and kinematic information, obtained during the manipulation process and reasoning about the desired and real positions of the fingertips during the manipulation. This is done taking into account that the desired positions of the fingertips are not physically reachable since they are located in the interior of the manipulated object and therefore they are virtual positions with associated virtual contact points. The proposed approach was satisfactorily validated using three fingers of an anthropomorphic robotic hand (Allegro Hand), with the original fingertips replaced by tactile sensors (WTS-FT). In the experimental validation, several everyday objects with different shapes were successfully manipulated, rotating them without the need of knowing their shape or any other physical property.Peer ReviewedPostprint (author's final draft

Prehensile Pushing: In-hand Manipulation with Push-Primitives

This paper explores the manipulation of a grasped object by pushing it against its environment. Relying on precise arm motions and detailed models of frictional contact, prehensile pushing enables dexterous manipulation with simple manipulators, such as those currently available in industrial settings, and those likely affordable by service and field robots. This paper is concerned with the mechanics of the forceful interaction between a gripper, a grasped object, and its environment. In particular, we describe the quasi-dynamic motion of an object held by a set of point, line, or planar rigid frictional contacts and forced by an external pusher (the environment). Our model predicts the force required by the external pusher to “break” the equilibrium of the grasp and estimates the instantaneous motion of the object in the grasp. It also captures interesting behaviors such as the constraining effect of line or planar contacts and the guiding effect of the pusher’s motion on the objects’s motion. We evaluate the algorithm with three primitive prehensile pushing actions—straight sliding, pivoting, and rolling—with the potential to combine into a broader in-hand manipulation capability.National Science Foundation (U.S.). National Robotics Initiative (Award NSF-IIS-1427050)Karl Chang Innovation Fund Awar

Planning dextrous robot hand grasps from range data, using preshapes and digit trajectories

Dextrous robot hands have many degrees of freedom. This enables the manipulation of objects between the digits of the dextrous hand but makes grasp planning substantially more complex than for parallel jaw grippers. Much of the work that addresses grasp planning for dextrous hands concentrates on the selection of contact sites to optimise stability criteria and ignores the kinematics of the hand. In more complete systems, the paradigm of preshaping has emerged as dominant. However, the criteria for the formation and placement of the preshapes have not been adequately examined, and the usefulness of the systems is therefore limited to grasping simple objects for which preshapes can be formed using coarse heuristics.In this thesis a grasp metric based on stability and kinematic feasibility is introduced. The preshaping paradigm is extended to include consideration of the trajectories that the digits take during closure from preshape to final grasp. The resulting grasp family is dependent upon task requirements and is designed for a set of "ideal" object-hand configurations. The grasp family couples the degrees of freedom of the dextrous hand in an anthropomorphic manner; the resulting reduction in freedom makes the grasp planning less complex. Grasp families are fitted to real objects by optimisation of the grasp metric; this corresponds to fitting the real object-hand configuration as close to the ideal as possible. First, the preshape aperture, which defines the positions of the fingertips in the preshape, is found by optimisation of an approximation to the grasp metric (which makes simplifying assumptions about the digit trajectories and hand kinematics). Second, the full preshape kinematics and digit closure trajectories are calculated to optimise the full grasp metric.Grasps are planned on object models built from laser striper range data from two viewpoints. A surface description of the object is used to prune the space of possible contact sites and to allow the accurate estimation of normals, which is required by the grasp metric to estimate the amount of friction required. A voxel description, built by ray-casting, is used to check for collisions between the object and the robot hand using an approximation to the Euclidean distance transform.Results are shown in simulation for a 3-digit hand model, designed to be like a simplified human hand in terms of its size and functionality. There are clear extensions of the method to any dextrous hand with a single thumb opposing multiple fingers and several different hand models that could be used are described. Grasps are planned on a wide variety of curved and polyhedral object

Dynamic modeling and simulation of a multi-fingered robot hand.

by Joseph Chun-kong Chan.Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.Includes bibliographical references (leaves 117-124).Abstract also in Chinese.Abstract --- p.iAcknowledgments --- p.ivList of Figures --- p.xiList of Tables --- p.xiiList of Algorithms --- p.xiiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.1Chapter 1.2 --- Related Work --- p.5Chapter 1.3 --- Contributions --- p.7Chapter 1.4 --- Organization of the Thesis --- p.9Chapter 2 --- Contact Modeling: Kinematics --- p.11Chapter 2.1 --- Introduction --- p.11Chapter 2.2 --- Contact Kinematics between Two Rigid Bodies --- p.14Chapter 2.2.1 --- Contact Modes --- p.14Chapter 2.2.2 --- Montana's Contact Equations --- p.15Chapter 2.3 --- Finger Kinematics --- p.18Chapter 2.3.1 --- Finger Forward Kinematics --- p.19Chapter 2.3.2 --- Finger Jacobian --- p.21Chapter 2.4 --- Grasp Kinematics between a Finger and an Object --- p.21Chapter 2.4.1 --- Velocity Transformation between Different Coordinate Frames --- p.22Chapter 2.4.2 --- Grasp Kinematics for the zth Contact --- p.23Chapter 2.4.3 --- Different Fingertip Models and Different Contact Modes --- p.25Chapter 2.5 --- Velocity Constraints of the Entire System --- p.28Chapter 2.6 --- Summary --- p.29Chapter 3 --- Contact Modeling: Dynamics --- p.31Chapter 3.1 --- Introduction --- p.31Chapter 3.2 --- Multi-fingered Robot Hand Dynamics --- p.33Chapter 3.3 --- Object Dynamics --- p.35Chapter 3.4 --- Constrained System Dynamics --- p.37Chapter 3.5 --- Summary --- p.39Chapter 4 --- Collision Modeling --- p.40Chapter 4.1 --- Introduction --- p.40Chapter 4.2 --- Assumptions of Collision --- p.42Chapter 4.3 --- Collision Point Velocities --- p.43Chapter 4.3.1 --- Collision Point Velocity of the ith. Finger --- p.43Chapter 4.3.2 --- Collision Point Velocity of the Object --- p.46Chapter 4.3.3 --- Relative Collision Point Velocity --- p.47Chapter 4.4 --- Equations of Collision --- p.47Chapter 4.4.1 --- Sliding Mode Collision --- p.48Chapter 4.4.2 --- Sticking Mode Collision --- p.49Chapter 4.5 --- Summary --- p.51Chapter 5 --- Dynamic Simulation --- p.53Chapter 5.1 --- Introduction --- p.53Chapter 5.2 --- Architecture of the Dynamic Simulation System --- p.54Chapter 5.2.1 --- Input Devices --- p.54Chapter 5.2.2 --- Dynamic Simulator --- p.58Chapter 5.2.3 --- Virtual Environment --- p.60Chapter 5.3 --- Methodologies and Program Flow of the Dynamic Simulator --- p.60Chapter 5.3.1 --- Interference Detection --- p.61Chapter 5.3.2 --- Constraint-based Simulation --- p.63Chapter 5.3.3 --- Impulse-based Simulation --- p.66Chapter 5.4 --- Summary --- p.69Chapter 6 --- Simulation Results --- p.71Chapter 6.1 --- Introduction --- p.71Chapter 6.2 --- Change of Grasping Configurations --- p.71Chapter 6.3 --- Rolling Contact --- p.76Chapter 6.4 --- Sliding Contact --- p.76Chapter 6.5 --- Collisions --- p.85Chapter 6.6 --- Dextrous Manipulation Motions --- p.93Chapter 6.7 --- Summary --- p.94Chapter 7 --- Conclusions --- p.99Chapter 7.1 --- Summary of Contributions --- p.99Chapter 7.2 --- Future Work --- p.100Chapter 7.2.1 --- Improvement of Current System --- p.100Chapter 7.2.2 --- Applications --- p.101Chapter A --- Montana's Contact Equations for Finger-object Contact --- p.103Chapter A.1 --- Local Coordinates Charts --- p.103Chapter A.2 --- "Curvature, Torsion and Metric Tensors" --- p.104Chapter A.3 --- Montana's Contact Equations --- p.106Chapter B --- Finger Dynamics --- p.108Chapter B.1 --- Forward Kinematics of a Robot Finger --- p.108Chapter B.1.1 --- Link-coordinate Transformation --- p.109Chapter B.1.2 --- Forward Kinematics --- p.109Chapter B.2 --- Dynamic Equation of a Robot Finger --- p.110Chapter B.2.1 --- Kinetic and Potential Energy --- p.110Chapter B.2.2 --- Lagrange's Equation --- p.111Chapter C --- Simulation Configurations --- p.113Chapter C.1 --- Geometric models --- p.113Chapter C.2 --- Physical Parameters --- p.113Chapter C.3 --- Simulation Parameters --- p.116Bibliography --- p.12