Maximo A. Roa

Posture and balance control for biped robots based on contact force optimization

This paper presents a new balancing control approach for regulating the center of mass position and trunk orientation of a bipedal robot in a compliant way. The controller computes a desired wrench (force and torque) required to recover the posture when an unknown external perturbation has changed the posture of the robot. This wrench is later distributed as forces at predefined contact points via a constrained optimization, which aims at achieving the desired wrench while minimizing the Euclidean norm of the contact forces. The formulation of the force distribution as an optimization problem is adopted from the grasping literature and allows to consider restrictions coming from the friction between the contact points and the ground.

Bipedal walking control based on Capture Point dynamics

This paper builds up on the Capture Point concept and exploits the simple form of the dynamical equations of the Linear Inverted Pendulum model when formulated in terms of the center of mass and the Capture Point. The presented methods include (i) the derivation of a Capture Point (CP) control principle based on the natural dynamics of the linear inverted pendulum (LIP), which stabilizes the walking robot and motivates (ii) the design of a CP tracking and a CP end-of-step controller. The exponential stability of the CP control law is proven. Tilting is avoided by proper projection of the commanded zero moment point. The robustness of the derived control algorithms is analyzed analytically and verified in simulation and experiments.

Grasp quality measures: review and performance

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.

Computation of Independent Contact Regions for Grasping 3-D Objects

Precision grasp synthesis has received a lot of attention in past few last years. However, real mechanical hands can hardly assure that the fingers will precisely touch the object at the computed contact points. The concept of independent contact regions (ICRs) was introduced to provide robustness to finger positioning errors during an object grasping: A finger contact anywhere inside each of these regions assures a force-closure grasp, despite the exact contact position. This paper presents an efficient algorithm to compute ICRs with any number of frictionless or frictional contacts on the surface of any 3-D object. The proposed approach generates the independent regions by growing them around the contact points of a given starting grasp. A two-phase approach is provided to find a locally optimal force-closure grasp that serves as the starting grasp, considering as grasp quality measure the largest perturbation wrench that the grasp can resist, independently of the perturbation direction. The proposed method can also be applied to compute ICRs when several contacts are fixed beforehand. The approach has been implemented, and application examples are included to illustrate its performance.

Overview of the torque-controlled humanoid robot TORO

This paper gives an overview on the torque-controlled 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.

Stable myoelectric control of a hand prosthesis using non-linear incremental learning

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.

Geometrical approach for grasp synthesis on discretized 3d objects

Grasp synthesis on real 3D objects is a critical problem in grasp and manipulation planning. This paper presents a geometrical approach to compute force closure (FC) grasps, with or without friction and with any number of fingers. The objects surface is discretized in a cloud of points, so the algorithm is applicable to objects of any arbitrary shape. One or more FC grasps are obtained with a geometrical approach, which embeds the FC test in the algorithm to simplify achieving the force-closure property. This initial FC grasp may be improved with a complementary optimization algorithm. The grasp quality is measured considering the largest perturbation wrench that the grasp can resist with independence of the perturbation direction. The efficiency of both algorithms is illustrated through numerical examples.

Power grasp planning for anthropomorphic robot hands

This paper presents an approach for computing power grasps for hands with kinematic structure similar to the human hand, which allows the implementation of strategies inspired in human grasping actions. The proposed method first samples the object surface to look for the best spots for creating an opposing grasp with two or three fingers, and then aligns the other fingers to match the local curvature of the object surface. Different grasp strategies are considered, depending on the relative size of the object with respect to the hand, and on the location of potential obstacles in the environment. Several application examples are provided with two different hand models.

Transferring functional grasps through contact warping and local replanning

We present a method for transferring grasps between objects of the same functional category. This transfer is intended to preserve the functionality of a grasp constructed for one of the objects, thus enabling the analogous action to be performed on a novel object for which no grasp has been specified. Manipulation knowledge is hence generalized from a single example to a class of objects with a significant amount of shape variability. The transfer is achieved through warping the surface geometry of the source object onto the target object, and along with it the contact points of a grasp. The warped contacts are locally replanned, if necessary, to ensure grasp stability, and a suitable grasp pose is computed. We present extensive results of experiments with a database of four-finger grasps, designed to systematically cover variations on grasping the mugs of the Princeton Shape Benchmark.

Reachable Independent Contact Regions for precision grasps

Independent Contact Regions allow a robust finger placement on the object, despite of potential errors in finger position. They are computed without considering the kinematics of the end-effector, and are usually applied to off-line grasp planners. This paper presents an approach to obtain Reachable Independent Contact Regions by including the hand kinematics in the computational loop. The regions are computed in a short time, which allows real-time applications in virtual grasping. Potential applications of the proposed approach include regrasp planning, and dual-hand manipulation of objects.