The correct grasp of objects is a key aspect for the right fulfillment of a given task. Obtaining a good grasp requires algorithms to automatically determine proper contact points on the object as well as proper hand configurations, especially when dexterous manipulation is desired, and the quantification of a good grasp requires the definition of suitable grasp quality measures. This article reviews the quality measures proposed in the literature to evaluate grasp quality. The quality measures are classified into two groups according to the main aspect they evaluate: location of contact points on the object and hand configuration. The approaches that combine different measures from the two previous groups to obtain a global quality measure are also reviewed, as well as some measures related to human hand studies and grasp performance. Several examples are presented to illustrate and compare the performance of the reviewed measures.

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This work presents a new control approach to multi-contact balancing for torque-controlled humanoid robots. The controller includes a non-strict task hierarchy, which allows the robot to use a subset of its end effectors for balancing while the remaining ones can be used for interacting with the environment. The controller creates a passive and compliant behavior for regulating the center of mass (CoM) location, hip orientation and the poses of each end effector assigned to the interaction task. This is achieved by applying a suitable wrench (force and torque) at each one of the end effectors used for interaction. The contact wrenches at the balancing end effectors are chosen such that the sum of the balancing and interaction wrenches produce the desired wrench at the CoM. The algorithm requires the solution of an optimization problem, which distributes the CoM wrench to the end effectors taking into account constraints for unilaterality, friction and position of the center of pressure. Furthermore, the feedback controller is combined with a feedforward control in order to improve performance while tracking a predefined trajectory, leading to a control structure similar to a PD+ control. The controller is evaluated in several experiments with the humanoid robot TORO.

Stable myoelectric control of hand prostheses remains an open problem. The only successful human–machine interface is surface electromyography, typically allowing control of a few degrees of freedom. Machine learning techniques may have the potential to remove these limitations, but their performance is thus far inadequate: myoelectric signals change over time under the influence of various factors, deteriorating control performance. It is therefore necessary, in the standard approach, to regularly retrain a new model from scratch. We hereby propose a non-linear incremental learning method in which occasional updates with a modest amount of novel training data allow continual adaptation to the changes in the signals. In particular, Incremental Ridge Regression and an approximation of the Gaussian Kernel known as Random Fourier Features are combined to predict finger forces from myoelectric signals, both finger-by-finger and grouped in grasping patterns. We show that the approach is effective and practically applicable to this problem by first analyzing its performance while predicting single-finger forces. Surface electromyography and finger forces were collected from 10 intact subjects during four sessions spread over two different days; the results of the analysis show that small incremental updates are indeed effective to maintain a stable level of performance. Subsequently, we employed the same method on-line to teleoperate a humanoid robotic arm equipped with a state-of-the-art commercial prosthetic hand. The subject could reliably grasp, carry and release everyday-life objects, enforcing stable grasping irrespective of the signal changes, hand/arm movements and wrist pronation and supination.

Fig. 1: The torque-controlled humanoid robot TORO and its development stages from 2010 (DLR Biped [1]) to 2014.Abstract-This paper gives an overview on the torquecontrolled humanoid robot TORO, which has evolved from the former DLR Biped. In particular, we describe its mechanical design and dimensioning, its sensors, electronics and computer hardware. Additionally, we give a short introduction to the walking and multi-contact balancing strategies used for TORO.