From democracy in America to a la francaise accountability. Grasping the sociopolitical origins of accountability [De la démocratie en Amérique du Nord à l’accountability à la française]

Whilst native French speakers oftentimes collapse accountability to account giving, this paper outlines the shape of an accountability ala française. Reading Tocqueville’s (1835) work highlights that accountability as practiced in Anglo-Saxonc countries has been an offspring of American democracy. An accountability a la française would be characterised by conformance to a set or universal values, the submission of minorities to choices made by the majority, a means obligation as well as the rejection of transparency. [Alors que le francophone réduit généralement l’accountability à la reddition de comptes, cet article esquisse les contours d’une véritable accountability à la française. La lecture de Tocqueville (1835) révèle que l’accountability pratiquée dans les pays anglo-saxons trouve ses origines dans les fondements de la démocratie américaine. En France, l’accountability serait caractérisée par le respect d’un ensemble de valeurs universelles, l’adhésion des minorités aux choix majoritaires, l’absence de discriminations, une obligation de moyens et un rejet de la transparence.]

Reactive reaching and grasping on a humanoid: Towards closing the action-perception loop on the iCub

We propose a system incorporating a tight integration between computer vision and robot control modules on a complex, high-DOF humanoid robot. Its functionality is showcased by having our iCub humanoid robot pick-up objects from a table in front of it. An important feature is that the system can avoid obstacles - other objects detected in the visual stream - while reaching for the intended target object. Our integration also allows for non-static environments, i.e. the reaching is adapted on-the-fly from the visual feedback received, e.g. when an obstacle is moved into the trajectory. Furthermore we show that this system can be used both in autonomous and tele-operation scenarios.

Simulating grasping behavior of people using the relational description scheme

This paper presents an enhanced relational description for the prescription of the grasp requirement and evolution of the posture of a digital human hand towards satisfaction of this requirement. Precise relational description needs anatomical segmentation of the hand geometry into palmar, dorsal and lateral patches using the palm-plane and joint locations information, and operational segmentation of the object geometry into pull,push and lateral patches with due consideration to the effect of friction. Relational description identifies appropriate patches for a desired grasp condition. Satisfaction of this requirement occurs in two discrete stages,namely,contact establishment and post-contact force exertion for object capturing. Contact establishment occurs in four potentially overlapping phases,namely,re-orientation,transfer,pre- shaping,and closing-in. The novel h and re-orientation phase,enables the palm to face the object in a task sequence scenario, transfer takes the wrist to the ball park ; pre-shaping and close-in finally achieves the contact. In this paper, an anatomically pertinent closed-form formulation is presented for the closing-in phase for identification of the point of contact on the patches ,prescribed by the relational description. Since mere contact does not ensure grasp and slip phenomenon at the point of contact on application of force is a common occurrence, the effect of slip in presence of friction has been studied for 2D and 3D object grasping endeavours and a computational generation of the slip locus is presented.A general slip locus is found to be a non-linear curve even on planar faces.Two varieties of slip phenomena,namely,stabilizing and non-stabilizing slips, and their local characteristics have been identified.Study of the evolution of this slip characteristic over the slip locus exhibited diverse grasping behaviour possibilities. Thus, the relational description paradigm not only makes the requirement specification easy and meaningful but also enables high fidelity hand object interaction studies possible.

Estimation and inference for grasping and manipulation tasks using vision and kinesthetic sensors

This thesis presents a novel framework for state estimation in the context of robotic grasping and manipulation. The overall estimation approach is based on fusing various visual cues for manipulator tracking, namely appearance and feature-based, shape-based, and silhouette-based visual cues. Similarly, a framework is developed to fuse the above visual cues, but also kinesthetic cues such as force-torque and tactile measurements, for in-hand object pose estimation. The cues are extracted from multiple sensor modalities and are fused in a variety of Kalman filters.

A hybrid estimator is developed to estimate both a continuous state (robot and object states) and discrete states, called contact modes, which specify how each finger contacts a particular object surface. A static multiple model estimator is used to compute and maintain this mode probability. The thesis also develops an estimation framework for estimating model parameters associated with object grasping. Dual and joint state-parameter estimation is explored for parameter estimation of a grasped object's mass and center of mass. Experimental results demonstrate simultaneous object localization and center of mass estimation.

Dual-arm estimation is developed for two arm robotic manipulation tasks. Two types of filters are explored; the first is an augmented filter that contains both arms in the state vector while the second runs two filters in parallel, one for each arm. These two frameworks and their performance is compared in a dual-arm task of removing a wheel from a hub.

This thesis also presents a new method for action selection involving touch. This next best touch method selects an available action for interacting with an object that will gain the most information. The algorithm employs information theory to compute an information gain metric that is based on a probabilistic belief suitable for the task. An estimation framework is used to maintain this belief over time. Kinesthetic measurements such as contact and tactile measurements are used to update the state belief after every interactive action. Simulation and experimental results are demonstrated using next best touch for object localization, specifically a door handle on a door. The next best touch theory is extended for model parameter determination. Since many objects within a particular object category share the same rough shape, principle component analysis may be used to parametrize the object mesh models. These parameters can be estimated using the action selection technique that selects the touching action which best both localizes and estimates these parameters. Simulation results are then presented involving localizing and determining a parameter of a screwdriver.

Lastly, the next best touch theory is further extended to model classes. Instead of estimating parameters, object class determination is incorporated into the information gain metric calculation. The best touching action is selected in order to best discern between the possible model classes. Simulation results are presented to validate the theory.

Motor systems: reaching out and grasping the molecular tools.

Two recent studies provide important insights into the organization of premotor circuitries, showing that control of highly-specific skilled forelimb movements, such as reaching and grasping, requires activation of specific subpopulations of neurons in the brainstem and spinal cord.

The Grasping Problem: Toward Task-Level Programming for an Articulated Hand

This report presents a system for generating a stable, feasible, and reachable grasp of a polyhedral object. A set of contact points on the object is found that can result in a stable grasp; a feasible grasp is found in which the robot contacts the object at those contact points; and a path is constructed from the initial configuration of the robot to the stable, feasible final grasp configuration. The algorithm described in the report is designed for the Salisbury hand mounted on a Puma 560 arm, but a similar approach could be used to develop grasping systems for other robots.

Manipulator Grasping and Pushing Operations

The primary goal of this research is to develop theoretical tools for analysis, synthesis, application of primitive manipulator operations. The primary method is to extend and apply traditional tools of classical mechanics. The results are of such a general nature that they address many different aspects of industrial robotics, including effector and sensor design, planning and programming tools and design of auxiliary equipment. Some of the manipulator operations studied are: (1) Grasping an object. The object will usually slide and rotate during the period between first contact and prehension. (2) Placing an object. The object may slip slightly in the fingers upon contact with the table as the base aligns with the table. (3) Pushing. Often the final stage of mating two parts involves pushing one object into the other.

Robotic grasping by showing: an experimental comparison of two novel algorithms.

Neal M J, Boyce D, Rowland J J, Lee M H, and Olivier P L. Robotic grasping by showing: an experimental comparison of two novel algorithms. In Proceedings of IFAC - SICICA'97, pages 345-350, Annecy, France, 1997.

Components of reaching and grasping an object may emerge following a single minimization process

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal.

Passive and Active Grasping with a Prehensile Robot End-Effector

This report presents a design of a new type of robot end-effector with inherent mechanical grasping capabilities. Concentrating on designing an end-effector to grasp a simple class of objects, cylindrical, allowed a design with only one degree of actuation. The key features of this design are high bandwidth response to forces, passive grasping capabilities, ease of control, and ability to wrap around objects with simple geometries providing form closure. A prototype of this mechanism was built to evaluate these features.

Analysis and Implementation of Robust Grasping Behaviors

This thesis addresses the problem of developing automatic grasping capabilities for robotic hands. Using a 2-jointed and a 4-jointed nmodel of the hand, we establish the geometric conditions necessary for achieving form closure grasps of cylindrical objects. We then define and show how to construct the grasping pre-image for quasi-static (friction dominated) and zero-G (inertia dominated) motions for sensorless and sensor-driven grasps with and without arm motions. While the approach does not rely on detailed modeling, it is computationally inexpensive, reliable, and easy to implement. Example behaviors were successfully implemented on the Salisbury hand and on a planar 2-fingered, 4 degree-of-freedom hand.