Human-Robotic Variable-Stiffness Grasps of Small-Fruit Containers Are Successful Even Under Severely Impaired Sensory Feedback

Application areas of robotic grasping extend to delicate objects like groceries. The intrinsic elasticity offered by variable-stiffness actuators (VSA) appears to be promising in terms of being able to adapt to the object shape, to withstand collisions with the environment during the grasp acquisition, and to resist the weight applied to the fingers by a lifted object during the actual grasp. It is hypothesized that these properties are particularly useful in the absence of high-quality sensory feedback, which would otherwise be able to guide the shape adaptation and collision avoidance, and that in this case, VSA hands perform better than hands with fixed stiffness. This hypothesis is tested in an experiment where small-fruit containers are picked and placed using a newly developed variable-stiffness robotic hand. The grasp performance is measured under different sensory feedback conditions: full or impaired visual feedback, full or impaired force feedback. The hand is switched between a variable-stiffness mode and two fixed-stiffness modes. Strategies for modulating the stiffness and exploiting environmental constraints are observed from human operators that control the robotic hand. The results show consistently successful grasps under all stiffness and feedback conditions. However, the performance is affected by the amount of available visual feedback. Different stiffness modes turn out to be beneficial in different feedback conditions and with respect to different performance criteria, but a general advantage of VSA over fixed stiffness cannot be shown for the present task. Guidance of the fingers along cracks and gaps is observed, which may inspire the programming of autonomously grasping robots

Robotics of human movements

The construction of robotic systems that can move the way humans do, with respect to agility, stability and precision, is a necessary prerequisite for the successful integration of robotic systems in human environments. We explain human-centered views on robotics, based on the three basic ingredients (1) actuation; (2) sensing; and (3) control, and formulate detailed examples thereof

Elastic Elements in a Wrist Prosthesis for Drumming Reduce Muscular Effort, but Increase Imprecision and Perceived Stress

Recently, progress has been made in the development of mechanical joints with variable intrinsic stiffness, opening up the search for application areas of such variable-stiffness joints. By varying the stiffness of its joints, the resonant frequency of a system can be tuned to perform cyclical tasks most energy-efficiently, making the variable-stiffness joint a candidate element for an advanced prosthetic device specifically designed for the cyclical task of drumming. A prerequisite for a successful variable-stiffness drumming prosthesis is the ability of human drummers to profitably employ different stiffness levels for playing different beats. In this pilot study, 29 able-bodied subjects (20 drumming novices and 9 experts) wear a cuff on the forearm, to which a drumstick is connected using changeable adapters, consisting of several leaf springs with different stiffness and one maximally stiff connection element. The subjects are asked to play simple regular drum beats at different frequencies, one of which is the resonant frequency of the adapter-drumstick system. The subject's performance of each drumming task is rated in terms of accuracy and precision, and the effort is measured using questionnaires for the perceived stress as well as electromyography (EMG) for the muscular activity. The experiments show that using springs instead of the stiff connection leads to lower muscular activity, indicating that humans are able to use the energy-storing capabilities of the springs, or that muscular activity is reduced due to the lower mass of the springs. However, the perceived stress is increased and the novices' performance lowered, possibly due to a higher cerebral load for controlling the elastic system. The hypothesis that "matching the resonant frequency of the spring-drumstick system to the desired frequency leads to better performance and lower effort" is not confirmed. Possible explanations are discussed. In conclusion, a series-elastic element appears to lower the muscular effort of drumming, while a stiff connection appears to minimize the mental load and has a positive effect on the performance of drumming novices

Exodex Adam—A Reconfigurable Dexterous Haptic User Interface for the Whole Hand

Applications for dexterous robot teleoperation and immersive virtual reality are growing. Haptic user input devices need to allow the user to intuitively command and seamlessly “feel” the environment they work in, whether virtual or a remote site through an avatar. We introduce the DLR Exodex Adam, a reconfigurable, dexterous, whole-hand haptic input device. The device comprises multiple modular, three degrees of freedom (3-DOF) robotic fingers, whose placement on the device can be adjusted to optimize manipulability for different user hand sizes. Additionally, the device is mounted on a 7-DOF robot arm to increase the user’s workspace. Exodex Adam uses a front-facing interface, with robotic fingers coupled to two of the user’s fingertips, the thumb, and two points on the palm. Including the palm, as opposed to only the fingertips as is common in existing devices, enables accurate tracking of the whole hand without additional sensors such as a data glove or motion capture. By providing “whole-hand” interaction with omnidirectional force-feedback at the attachment points, we enable the user to experience the environment with the complete hand instead of only the fingertips, thus realizing deeper immersion. Interaction using Exodex Adam can range from palpation of objects and surfaces to manipulation using both power and precision grasps, all while receiving haptic feedback. This article details the concept and design of the Exodex Adam, as well as use cases where it is deployed with different command modalities. These include mixed-media interaction in a virtual environment, gesture-based telemanipulation, and robotic hand–arm teleoperation using adaptive model-mediated teleoperation. Finally, we share the insights gained during our development process and use case deployments

Human hand kinematics based on MRI imaging

Anthropomorphic robot hands have come to a technical level where understanding exact human hand kinematics becomes relevant, e.g. the hand/arm system that is presently being developed at DLR. Human hand kinematics have been investigated through cadaver hands and optical motion tracking of surface markers. A problem with the former is that tissue properties might be altered due to tissue necrosis. With the latter, the motion of the skin relative to the bones leads to so called soft tissue artifacts (STA) that negatively influence the quality of the results. To allow in vivo measurements and to avoid STA, we recorded finger postures by magnetic resonance imaging (MRI). We used a method similar to the one described by Miyata et al. in 2005, but with a much larger number of hand postures, resulting in a model with multi-degree-of-freedom (multi-DoF) joints

Movement model of a human hand based on magnetic resonance imaging (MRI)

Building on a long tradition of developing robotic hands, we are developing robotic systems closely copying human hands in its kinematic and dynamic properties. To this end, we require an exact computational model of human hand kinematics in order to obtain optimal grasping properties. From a large number of MRI recordings of hand bones in various grasps, we construct a parametrisable kinematic model, of which optimal versions can be determined. In this paper we present the required image processing and modelling methods as well as a resulting model

A data-driven kinematic model of the human hand with soft-tissue artifact compensation mechanism for grasp synergy analysis

This paper presents a methodology to accurately record human finger postures during grasping. The main contribution consists of a kinematic model of the human hand reconstructed via magnetic resonance imaging of one subject that (i) is fully parameterized and can be adapted to different subjects, and (ii) is amenable to in-vivo joint angle recordings via optical tracking of markers attached to the skin. The principal novelty here is the introduction of a soft-tissue artifact compensation mechanism that can be optimally calibrated in a systematic way. The high-quality data gathered are employed to study the properties of hand postural synergies in humans, for the sake of ongoing neuro-science investigations. These data are analyzed and some comparisons with similar studies are reported. After a meaningful mapping strategy has been devised, these data could be employed to define robotic hand postures suitable to attain effective grasps, or could be used as prior knowledge in lower-dimensional, real-time avatar hand animation

Modellierung menschlicher Fingergelenke als Schaniergelenke mit geneigter Drehachse

Die Beweglichkeit (Kinematik) des neuen integrierten Hand-Arm-Systems soll sehr nah an der Kinematik der menschlichen Hand sein. In der Literatur wurde kein geeignetes kinematisches Modell einer menschlichen Hand gefunden. Daher wurden Magnetresonanztomographie-Aufnahmen (MRT-Aufnahmen) einer Hand in verschiedenen Stellungen angefertigt, um die Kinematik selbst zu untersuchen. Diese Semesterarbeit ist ein erster Schritt in diese Richtung. Aus den MRT-Aufnahmen wurde für jedes Fingergelenk die Position des Drehpunktes ermittelt. Zusätzlich wurden für einige Gelenke die Orientierungen der Drehachsen ermittelt. Dafür wurde die Lage jedes Knochens in jeder Aufnahme geschätzt, mit einen automatischen Verfahren von U. Hillenbrand. Aus den Lagen wurden die Bewegungen des Gelenks berechnet. Mit numerischen Optimierungsvefahren wurden Drehpunkte und Drehachsen ermittelt, welche die Bewegungen möglichst gut abbilden. Die Genauigkeit des Ergebnisses wurde mit geeigneten Fehlermaßen bestimmt. Sie ist zufriedenstellend bei den Drehpunkten, während das Verfahren für die Drehachse noch verbessert werden muss

Multi-body simulation of a human thumb joint by sliding surfaces

The development of anthropomorphic robotic grippers requires profound knowledge about the functionality of the human hand. For investigating its kinematic and dynamic properties, numerous biomechanical models have been established based on the assumption of fixed rotational axes. Even though this approach has proven to be accurate for most joints of the hand, difficulties have been reported for modelling the movement of the thumb. In order to investigate errors resulting from the thumb carpo-metacarpal joint, a new modelling approach is pursued that is based on contacting surfaces and stabilizing tissues. The joint is modelled as a multi-body system and driven by forces exerted by the cartilage contact, ligaments and muscles. Comparing the simulation results to anatomical literature reveals the capabilities of the proposed approach, but also the necessity of further improvements for its applicability in biomechanical investigations