Principal components analysis based control of a multi-dof underactuated prosthetic hand

<p>Abstract</p> <p>Background</p> <p>Functionality, controllability and cosmetics are the key issues to be addressed in order to accomplish a successful functional substitution of the human hand by means of a prosthesis. Not only the prosthesis should duplicate the human hand in shape, functionality, sensorization, perception and sense of body-belonging, but it should also be controlled as the natural one, in the most intuitive and undemanding way. At present, prosthetic hands are controlled by means of non-invasive interfaces based on electromyography (EMG). Driving a multi degrees of freedom (DoF) hand for achieving hand dexterity implies to selectively modulate many different EMG signals in order to make each joint move independently, and this could require significant cognitive effort to the user.</p> <p>Methods</p> <p>A Principal Components Analysis (PCA) based algorithm is used to drive a 16 DoFs underactuated prosthetic hand prototype (called CyberHand) with a two dimensional control input, in order to perform the three prehensile forms mostly used in Activities of Daily Living (ADLs). Such Principal Components set has been derived directly from the artificial hand by collecting its sensory data while performing 50 different grasps, and subsequently used for control.</p> <p>Results</p> <p>Trials have shown that two independent input signals can be successfully used to control the posture of a real robotic hand and that correct grasps (in terms of involved fingers, stability and posture) may be achieved.</p> <p>Conclusions</p> <p>This work demonstrates the effectiveness of a bio-inspired system successfully conjugating the advantages of an underactuated, anthropomorphic hand with a PCA-based control strategy, and opens up promising possibilities for the development of an intuitively controllable hand prosthesis.</p

In-Hand Object Stabilization by Independent Finger Control

Grip control during robotic in-hand manipulation is usually modeled as part of a monolithic task, relying on complex controllers specialized for specific situations. Such approaches do not generalize well and are difficult to apply to novel manipulation tasks. Here, we propose a modular object stabilization method based on a proposition that explains how humans achieve grasp stability. In this bio-mimetic approach, independent tactile grip stabilization controllers ensure that slip does not occur locally at the engaged robot fingers. Such local slip is predicted from the tactile signals of each fingertip sensor i.e., BioTac and BioTac SP by Syntouch. We show that stable grasps emerge without any form of central communication when such independent controllers are engaged in the control of multi-digit robotic hands. These grasps are resistant to external perturbations while being capable of stabilizing a large variety of objects.Comment: Submitted to IEEE Transactions on Robotics Journal. arXiv admin note: text overlap with arXiv:1612.0820

Extrinsic Dexterity: In-Hand Manipulation with External Forces

Abstract — “In-hand manipulation ” is the ability to reposition an object in the hand, for example when adjusting the grasp of a hammer before hammering a nail. The common approach to in-hand manipulation with robotic hands, known as dexterous manipulation [1], is to hold an object within the fingertips of the hand and wiggle the fingers, or walk them along the object’s surface. Dexterous manipulation, however, is just one of the many techniques available to the robot. The robot can also roll the object in the hand by using gravity, or adjust the object’s pose by pressing it against a surface, or if fast enough, it can even toss the object in the air and catch it in a different pose. All these techniques have one thing in common: they rely on resources extrinsic to the hand, either gravity, external contacts or dynamic arm motions. We refer to them as “extrinsic dexterity”. In this paper we study extrinsic dexterity in the context of regrasp operations, for example when switching from a power to a precision grasp, and we demonstrate that even simple grippers are capable of ample in-hand manipulation. We develop twelve regrasp actions, all open-loop and handscripted, and evaluate their effectiveness with over 1200 trials of regrasps and sequences of regrasps, for three different objects (see video [2]). The long-term goal of this work is to develop a general repertoire of these behaviors, and to understand how such a repertoire might eventually constitute a general-purpose in-hand manipulation capability. I

User needs, benefits and integration of robotic systems in a space station laboratory

The methodology, results and conclusions of the User Needs, Benefits, and Integration Study (UNBIS) of Robotic Systems in the Space Station Microgravity and Materials Processing Facility are summarized. Study goals include the determination of user requirements for robotics within the Space Station, United States Laboratory. Three experiments were selected to determine user needs and to allow detailed investigation of microgravity requirements. A NASTRAN analysis of Space Station response to robotic disturbances, and acceleration measurement of a standard industrial robot (Intelledex Model 660) resulted in selection of two ranges of low gravity manipulation: Level 1 (10-3 to 10-5 G at greater than 1 Hz.) and Level 2 (less than = 10-6 G at 0.1 Hz). This included an evaluation of microstepping methods for controlling stepper motors and concluded that an industrial robot actuator can perform milli-G motion without modification. Relative merits of end-effectors and manipulators were studied in order to determine their ability to perform a range of tasks related to the three low gravity experiments. An Effectivity Rating was established for evaluating these robotic system capabilities. Preliminary interface requirements were determined such that definition of requirements for an orbital flight demonstration experiment may be established

Toward Fine Contact Interactions: Learning to Control Normal Contact Force with Limited Information

Dexterous manipulation of objects through fine control of physical contacts is essential for many important tasks of daily living. A fundamental ability underlying fine contact control is compliant control, \textit{i.e.}, controlling the contact forces while moving. For robots, the most widely explored approaches heavily depend on models of manipulated objects and expensive sensors to gather contact location and force information needed for real-time control. The models are difficult to obtain, and the sensors are costly, hindering personal robots' adoption in our homes and businesses. This study performs model-free reinforcement learning of a normal contact force controller on a robotic manipulation system built with a low-cost, information-poor tactile sensor. Despite the limited sensing capability, our force controller can be combined with a motion controller to enable fine contact interactions during object manipulation. Promising results are demonstrated in non-prehensile, dexterous manipulation experiments

A Convex Polynomial Force-Motion Model for Planar Sliding: Identification and Application

We propose a polynomial force-motion model for planar sliding. The set of generalized friction loads is the 1-sublevel set of a polynomial whose gradient directions correspond to generalized velocities. Additionally, the polynomial is confined to be convex even-degree homogeneous in order to obey the maximum work inequality, symmetry, shape invariance in scale, and fast invertibility. We present a simple and statistically-efficient model identification procedure using a sum-of-squares convex relaxation. Simulation and robotic experiments validate the accuracy and efficiency of our approach. We also show practical applications of our model including stable pushing of objects and free sliding dynamic simulations.Comment: 2016 IEEE International Conference on Robotics and Automation (ICRA

Innovative evaluation of dexterity in pediatrics.

STUDY DESIGN: Review paper. INTRODUCTION: Hand dexterity is multifaceted and essential to the performance of daily tasks. Timed performance and precision demands are the most common features of quantitative dexterity testing. Measurement concepts such as rate of completion, in-hand manipulation and dynamic force control of instabilities are being integrated into assessment tools for the pediatric population. PURPOSE: To review measurement concepts inherent in pediatric dexterity testing and introduce concepts that are infrequently measured or novel as exemplified with two assessment tools. METHODS: Measurement concepts included in common assessment tools are introduced first. We then describe seldom measured and novel concepts embedded in two instruments; the Functional Dexterity Test (FDT) and the Strength-Dexterity (SD) Test. DISCUSSION: The inclusion of novel yet informative tools and measurement concepts in our assessments could aid our understanding of atypical dexterity, and potentially contribute to the design of targeted therapy programs

Sensory mechanisms involved in obtaining frictional information for perception and grip force adjustment during object manipulation

Sensory signals informing about frictional properties of a surface are used both for perception to experience material properties and for motor control to be able to handle objects using adequate manipulative forces. There are fundamental differences between these two purposes and scenarios, how sensory information typically is obtained. This thesis aims to explore the mechanisms involved in the perception of frictional properties of the touched surfaces under conditions relevant for object manipulation. Firstly, I show that in the passive touch condition, when the surface is brought in contact with immobilised finger, humans are unable to use existing friction-related mechanical cues and perceptually associate them with frictional properties. However, a submillimeter range lateral movement significantly improved the subject's ability to evaluate the frictional properties of two otherwise identical surfaces. It is demonstrated that partial slips within the contact area and fingertip tissue deformation create very potent sensory stimuli, enabling tactile afferents to signal friction-dependent mechanical effects translating into slipperiness (friction) perception. Further, I demonstrate that natural movement kinematics facilitate the development of such small skin displacements within the contact area and may play a central role in enabling the perception of surface slipperiness and adjusting grip force to friction when manipulating objects. This demonstrates intimate interdependence between the motor and sensory systems. This work significantly extends our understanding of fundamental tactile sensory processes involved in friction signaling in the context of motor control and dexterous object manipulation tasks. This knowledge and discovered friction sensing principles may assist in designing haptic rendering devices and artificial tactile sensors as well as associated control algorithms to be used in robotic grippers and hand prostheses