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.

Passive Dynamics in the Control of Gymnastic Maneuvers

The control of aerial gymnastic maneuvers is challenging because these maneuvers frequently involve complex rotational motion and because the performer has limited control of the maneuver during flight. A performer can influence a maneuver using a sequence of limb movements during flight. However, the same sequence may not produce reliable performances in the presence of off-nominal conditions. How do people compensate for variations in performance to reliably produce aerial maneuvers? In this report I explore the role that passive dynamic stability may play in making the performance of aerial maneuvers simple and reliable. I present a control strategy comprised of active and passive components for performing robot front somersaults in the laboratory. I show that passive dynamics can neutrally stabilize the layout somersault which involves an "inherently unstable" rotation about the intermediate principal axis. And I show that a strategy that uses open loop joint torques plus passive dynamics leads to more reliable 1 1/2 twisting front somersaults in simulation than a strategy that uses prescribed limb motion. Results are presented from laboratory experiments on gymnastic robots, from dynamic simulation of humans and robots, and from linear stability analyses of these systems.

A Note on the Generalization Performance of Kernel Classifiers with Margin

We present distribution independent bounds on the generalization misclassification performance of a family of kernel classifiers with margin. Support Vector Machine classifiers (SVM) stem out of this class of machines. The bounds are derived through computations of the $V_gamma$ dimension of a family of loss functions where the SVM one belongs to. Bounds that use functions of margin distributions (i.e. functions of the slack variables of SVM) are derived.