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

Grasp analysis using deformable fingers

The human hand is unrivaled in its ability to grasp and manipulate objects, but we still do not understand all of its complexities. One benefit it has over traditional robot hands is the fact that our fingers conform to a grasped object's shape, giving rise to larger contact areas and the ability to apply larger frictional forces. In this paper, we demonstrate how we have extended our simulation and analysis system with finite element modeling to allow us to evaluate these complex contact types. We also propose a new contact model that better accounts for the deformations and show how grasp quality is affected. This work is part of a larger project to understand the benefits the human hand has in grasping

Innovative robot hand designs of reduced complexity for dexterous manipulation

This thesis investigates the mechanical design of robot hands to sensibly reduce the system complexity in terms of the number of actuators and sensors, and control needs for performing grasping and in-hand manipulations of unknown objects. Human hands are known to be the most complex, versatile, dexterous manipulators in nature, from being able to operate sophisticated surgery to carry out a wide variety of daily activity tasks (e.g. preparing food, changing cloths, playing instruments, to name some). However, the understanding of why human hands can perform such fascinating tasks still eludes complete comprehension. Since at least the end of the sixteenth century, scientists and engineers have tried to match the sensory and motor functions of the human hand. As a result, many contemporary humanoid and anthropomorphic robot hands have been developed to closely replicate the appearance and dexterity of human hands, in many cases using sophisticated designs that integrate multiple sensors and actuators---which make them prone to error and difficult to operate and control, particularly under uncertainty. In recent years, several simplification approaches and solutions have been proposed to develop more effective and reliable dexterous robot hands. These techniques, which have been based on using underactuated mechanical designs, kinematic synergies, or compliant materials, to name some, have opened up new ways to integrate hardware enhancements to facilitate grasping and dexterous manipulation control and improve reliability and robustness. Following this line of thought, this thesis studies four robot hand hardware aspects for enhancing grasping and manipulation, with a particular focus on dexterous in-hand manipulation. Namely: i) the use of passive soft fingertips; ii) the use of rigid and soft active surfaces in robot fingers; iii) the use of robot hand topologies to create particular in-hand manipulation trajectories; and iv) the decoupling of grasping and in-hand manipulation by introducing a reconfigurable palm. In summary, the findings from this thesis provide important notions for understanding the significance of mechanical and hardware elements in the performance and control of human manipulation. These findings show great potential in developing robust, easily programmable, and economically viable robot hands capable of performing dexterous manipulations under uncertainty, while exhibiting a valuable subset of functions of the human hand.Open Acces

Soft Finger Model with Adaptive Contact Geometry for Grasping and Manipulation Tasks

This paper presents a method for building analytical contact models for soft fingers. Friction constraints are derived based on general expressions for non-planar contacts of elastic bodies, taking into account the local geometry and structure of the objects in contact. These constraints are then formulated as a linear complementarity problem, the solution of which provides the normal and frictional forces applied at each contact, as well as the relative velocity of the bodies involved. This approach captures frictional effects such as coupling between tangential force and frictional torque. We illustrate this method by analyzing manipulation tasks performed by an anthropomorphic robotic hand equipped with soft fingerpads

Pseudo-Haptics for Rigid Tool/Soft Object Interaction Feedback in Virtual Environments

This paper proposes a novel pseudo-haptics soft object stiffness simulation technique which is a marked improvement to currently used simulation methods and an effective low-cost alternative to expensive 3-DOF haptic devices. Soft object stiffness simulation is achieved by maneuvering an indenter avatar over the surface of a virtual soft object by means of an input device, such as a mouse, a joystick, or a touch-sensitive tablet. The alterations to the indenter avatar behavior produced by the proposed technique create for the user the illusion of interaction with a hard inclusion embedded in the soft object. The proposed pseudo-haptics technique is validated with a series of experiments conducted by employing three types of 2-DOF force-sensitive haptic surfaces, including a touchpad, a tablet with an S-pen input, and a tablet with a bare finger input. It is found that both the sensitivity and the positive predictive value of hard inclusion detection can be significantly improved by 33.3% and 13.9% respectively by employing tablet computers. Using tablet computers could produce results comparable to direct hand touch in detecting hard inclusions in a soft object. The experimental results presented here confirm the potential of the proposed technique for conveying haptic information in rigid tool / soft object interaction in virtual environments