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

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

Compliant manipulation with a dextrous robot hand

The control of precise, compliant manipulation tasks with multifingered robots is discussed. Emphasis is placed on performing manipulations of grasped objects that are themselves undergoing compliant motion. This class of manipulations include common tasks such as using tools, writing, and sliding an object on a surface. A task-level formulation is presented and illustrated. Results of experiments are presented to demonstrate the feasibility of performing precision manipulations with a dextrous hand

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

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

A friendly teaching system for dexterous manipulation tasks of multi-fingered hands.

by Lam Pak Chio.Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.Includes bibliographical references (leaves 101-105).Abstract also in Chinese.Abstract --- p.iiAcknowledgements --- p.vContentsChapter 1 --- Introduction --- p.1Chapter 1.1 --- Background --- p.1Chapter 1.2 --- Problem Definition and Approach --- p.3Chapter 1.3 --- Outline --- p.5Chapter 2 --- Algorithm Outline --- p.7Chapter 2.1 --- Introduction --- p.7Chapter 2.2 --- Assumptions --- p.7Chapter 2.3 --- Object Model --- p.8Chapter 2.4 --- Hand Model --- p.9Chapter 2.5 --- Measurement Data --- p.11Chapter 2.6 --- Algorithm Outline --- p.12Chapter 3 --- Calculation of Contact States --- p.14Chapter 3.1 --- Introduction --- p.14Chapter 3.2 --- Problem Analysis --- p.15Chapter 3.3 --- Details of Algorithm --- p.17Chapter 3.3.1 --- Calculation of Contact Points --- p.18Chapter 3.3.2 --- Calculation of Object Position and Orientation --- p.26Chapter 3.3.2.1 --- The Object Orientation --- p.26Chapter 3.3.2.2 --- The Object Position --- p.28Chapter 3.3.3 --- Contact Points on Other Fingers --- p.32Chapter 4 --- Calculation of Contact Motion --- p.34Chapter 4.1 --- Introduction --- p.34Chapter 4.2 --- Search-tree --- p.34Chapter 4.3 --- Cost Function --- p.36Chapter 4.4 --- Details of Algorithm --- p.37Chapter 4.4.1 --- Calculation of the Next Instant Contact States --- p.39Chapter 4.4.1.1 --- Contact Region Estimation --- p.41Chapter 4.4.1.2 --- Contact Point Calculation --- p.45Chapter 4.4.1.3 --- Object Position and Orientation Calculation --- p.48Chapter 4.4.1.4 --- Contact Motion Calculation --- p.50Chapter 5 --- Implementation --- p.56Chapter 5.1 --- Introduction --- p.56Chapter 5.2 --- Architecture of Friendly Teaching System --- p.56Chapter 5.2.1 --- CyberGlove --- p.57Chapter 5.2.2 --- CyberGlove Interface Unit --- p.57Chapter 5.2.3 --- Host Computer --- p.58Chapter 5.2.4 --- Software --- p.58Chapter 5.3 --- Algorithm Implementation --- p.59Chapter 5.4 --- Examples for Calculation of Contact Configuration --- p.59Chapter 5.5 --- Simulation --- p.68Chapter 5.6 --- Experiments --- p.82Chapter 5.6.1 --- Translation of an Object --- p.82Chapter 5.6.2 --- Rotation of an Object --- p.90Chapter 6 --- Conclusions --- p.98References --- p.101Appendix --- p.10

Task-Oriented Contact Optimization for Pushing Manipulation with Mobile Robots

This work addresses the problem of transporting an object along a desired planar trajectory by pushing with mobile robots. More specifically, we concentrate on establishing optimal contacts between the object and the robots to execute the given task with minimum effort. We present a task-oriented contact placement optimization strategy for object pushing that allows calculating optimal contact points minimizing the amplitude of forces required to execute the task. Exploiting the optimized contact configuration, a motion controller uses the computed contact forces in feed-forward and position error feedback terms to realize the desired trajectory tracking task. Simulations and real experiments results confirm the validity of our approach