We develop a geometric framework for the stability analysis of multifingered grasps and propose a measure of grasp stability for arbitrary perturbations and loading conditions. The measure requires a choice of metric on the group of rigid body displacements. We show that although the stability of a grasp itself does not depend on the choice of metric, comparison of the stability of different grasps depends on the metric. Finally, we provide some insight into the choice of metrics for stability analysis.

This paper extends previous work on (suboptimal) Kalman filter based identification of geometric uncertainties that occur during the manipulation of an object in contact with its environment. The setup consists of a force-controlled robot which has grasped a cylindrical peg; the relative position and orientation of the peg with respect to the robot end effector are only approximately known. The peg is moved over a surface, whose position and orientation are also uncertain. This paper describes how to estimate the above-mentioned uncertainties, even if some of the geometric uncertainties are instantaneously indeterminate, i.e., their influences on the force and/or velocity errors are instantaneously indistinguishable. Experimental results illustrate the theoretical background of the estimation problem.

This paper shows how to identify the local second order shape of an unknown surface with a force controlled robot. Dedicated tools and paths are designed in order to make the identification easier. Two basic methods are presented, based on measurements of contact forces and of positions and velocities of the end effector. The first method fits a surface through a set of contact points while the second method uses the shape of surface curves. Experiments show that following two curves of limited length is sufficient to identify second order surface shape with an accuracy which is in general better than 10%. The most important result however is that this accuracy is sufficient to increase the performance of subsequent surface following tasks.

This paper presents a general approach for the identification of contact and grasping uncertainties, and the monitoring of contact situation changes in force controlled assembly operations. The identification problem is solved using virtual contact manipulators and Kalman filter techniques. Monitoring is solved by carrying out a statistical test on the sum of normalized and squared innovations of the Kalman filter, within a moving window, identification and monitoring are verified by experimental results. The paper explains how the error covariance matrix of the Kalman filter is interpreted to analyse the observability of the Kalman filter's states. Preliminary simulation results are presented for an ad hoc active sensing strategy to achieve complete state observability.

This paper shows how to improve the following of a planar contour with a force controlled robot using a model of the contour (i.e. how to make the task execution faster, of higher quality and more robust to large position uncertainties of the contour). The contour description is differential (it consists of curvature as a function of arc length) and Euclidean invariant as it does not change if the location of the contour is changed. A contour description is also identified from sensor measurements and matched online with the modelled contour description in order to identify the point in the model corresponding to the real contact point. The modelled curvature in this point is used for trajectory generation. Experiments have proved the usefulness of this approach.

This paper deals with the combination of model-based programming and sensor-based execution of surface following tasks. It shows how differential workpiece models, in combinations with proper matching strategies, allow a robust and high quality (i.e. fast and accurate) task execution in the presence of positioning and modelling inaccuracies. A geometric description of the local shape of curves and surfaces in the neighbourhood of the current "contact-point" is given in terms of local curvature and torsion, as used in differential geometry. Matching strategies try to find a good correspondence between the real contact point on the physical workpiece and its counterpart in the model. For this purpose, local characteristics of the surface are derived from sensor and control signals. Experiments show how the introduction of heuristic rules can make the application of model information less sensitive to matching errors.>