Manually controlled instrumented spasticity assessments: a systematic review of psychometric properties

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Towards Dynamic Visual Servoing for Interaction Control and Moving Targets

International audienceIn this work we present our results on dynamic visual servoing for the case of moving targets while also exploring the possibility of using such a controller for interaction with the environment. We illustrate the derivation of a feature space impedance controller for tracking a moving object as well as an Extended Kalman Filter based on the visual servoing kinematics for increasing the rate of the visual information and estimating the target velocity for both the cases of PBVS and IBVS with image point features. Simulations are carried out to validate the estimator performance during a Peg-in-Hole insertion task with a moving part. Experiments are also conducted on a real redundant manipulator with a low-cost wrist-mounted camera. Details on several implementation issues encountered during implementation are also discussed

Improving productivity and worker conditions in assembly : part 2 : rapid deployment of learnable robot skills

Collaborative robots (cobots) have a strong potential to improve both productivity as well as the working conditions of assembly operators by assisting in their tasks and by decreasing their physical and cognitive stress. The use of cobots in factories however introduces multiple challenges: how should the overall assembly architecture look like? How to allocate specific (sub)tasks to the operator or the cobot? How to program and deploy the cobot? How to make changes to the robot program? In this paper dilogy, we briefly highlight our recent contributions to this field. In part I we presented our collaborative architecture for human-robot assembly tasks and discussed the working principles of our task allocation framework, based upon agent capabilities and ergonomic measurements. In this second part we focus on our programming by demonstration approach targeted at expediting the deployment of learnable robot skills

Development and Acquisition of Skills for Deburring with Kinematically Redundant Robots (Ontwikkelen en aanleren van vaardigheden voor het ontbramen met kinematisch redundante robots)

Het doel van dit werk is het vereenvoudigen van het programmeren van rob ots in industriële toepassingen. Bij geavanceerde toepassingen van robot s is sensorinteractie nodig voor het opvangen van stoorinvloeden van de omgeving, het werkstuk, en het werktuig. Om de grootte van de robotopste lling te beperken, om het aantal inklemposities van het werkstuk te verk leinen, of om grotere werkstukken te kunnen behandelen, worden redundant e vrijheidsgraden toegevoegd aan een robotopstelling. Hoewel deze beide aspecten de inzetbaarheid van de opstelling verhogen, wordt hun flexibil iteit bij kleine seriegroottes verminderd door een moeizame en lange pro grammeercyclus. Deze cyclus moet doorlopen worden telkens een nieuw type werkstuk verwerkt wordt. Door het invoeren van vaardigheden wordt de pr ogrammeercyclus van deze applicatie vereenvoudigd en verkort. Dit werk e xploreert twee types vaardigheden, enerzijds een modelgebaseerde vaardig heid en anderzijds een aangeleerde vaardigheid. Deze aspecten zijn uitge werkt voor het gerobotiseerd ontbramen van gietstukken door middel van e en komschijf. Deze taak is gekozen wegens haar industriële relevantie en de problemen die huidige technieken met deze taak hebben. Het eerste type vaardigheid, een modelgebaseerde vaardigheid, zorgt dat de robot zijn kinematisch redundante vrijheidsgraden optimaal gebruikt v oor het uitvoeren van een ontbraamtaak. Hiertoe is een methodiek uitgewe rkt voor de specificatie van willekeurige kinematische kettingen. De ben odigde mathematische en geometrische entiteiten worden berekend door mid del van automatische differentiatie. Een locale optimalisatietechniek ge baseerd op snelheden of acceleraties laat de robot toe gewrichtslimieten , snelheidslimieten, en singulariteiten te vermijden. De ontwikkelde vaa rdigheid kan zonder enige moeite aangepast worden aan nieuwe applicaties . Het gebruik van zuiver positiegestuurde robots is beperkt door variaties in de positie van het werkstuk, in de plaats en het type van de bramen, in de materiaaleigenschappen en de geometrie van het werkstuk. Het gebr uik van sensorterugkoppeling is een antwoord op dergelijke problemen. Ma ar dit maakt het tijdrovend opstellen noodzakelijk van controlestrategie ën die afhangen van moeilijk te modelleren en vaak variërende parameters . In dit onderzoek worden deze controlestrategieën aangeleerd uit voorbe elden van een menselijke operator. Het leersysteem observeert een demons tratie van een ontbraamtaak en extraheert daaruit een beschrijving van d e krachtinteractie in deze taak. Tijdens de uitvoering van de taak gebruikt de robot dan deze beschrijving. Het leersysteem gebruikt een indirecte benadering. Na segmentatie worden de parameters van een o bservatiemodel en van een actiemodel geïdentificeerd. De relatie tussen deze twee groepen parameters worden dan door middel van een neuraal netw erk aangeleerd.nrpages: 253status: publishe

Etasl/eTC: A Constraint-Based Task Specification Language and Robot Controller Using Expression Graphs

This paper presents a new framework for constraint-based task specification of robot controllers. A task specification language (eTaSL) is defined as well as a corresponding implementation of a controller (eTC). This new framework is based on feature variables and a new concept referred to as expression graphs. It avoids some of the common pitfalls in previous frameworks, and provides a flexible and composable way to define robot control tasks. An architecture for a robot controller is proposed, as well as an implementation that can execute tasks described in the new specification language. Typical usage patterns for the new framework are explained on an example consisting of a kinematically redundant, bi-manual task on a PR2 robot. A comparison with existing frameworks shows the advantages of the new approach.status: publishe

Learning a Predictive Model of Human Gait for the Control of a Lower-limb Exoskeleton

Abstract— For an intelligent dynamic motion interaction between a human and a lower-limb exoskeleton, it is necessary to predict the future evolution of the joint gait trajectories and to detect which phase of the gait pattern is currently active. A model of the gait trajectories and of the variations on these trajectories is learned from an example data set. A gait prediction module, based on a statistical latent variable model, is able to predict, in real-time, the future evolution of a joint trajectory, an estimate of the uncertainty on this prediction, the timing along the trajectory and the consistency of the measurements with the learned model. The proposed method is validated using a data set of 54 trials of children walking at three different velocitiesstatus: publishe

Active Force Feedback in Real-life Industrial Robots

Active force feedback in robots has been developed in the 1970s as an answer to the limitations of the passive solutions, like the RCC (Remote Center Compliance), offered as an add-on for peg-into-hole assembly operations. Generic solutions of force feedback and impedance control have been developed in laboratory environments, but their industrial use has been very limited ever since. This situation has recently changed and it becomes feasible now to provide off-the-shelf industrial robots with real-time force feedback capabilities. In the paper, two convincing case-studies of tasks with force control will be presented. One is the synchronisation of a brake press with a plate-handling robot for sheet metal bending applications. Synchronisation is assured in two ways: (i) through a geometric bending model, and (ii) through active force feedback. The second case study deals with forcecontrolled assembly of rotary compressors. Here passive RCC-like aids are compared with force-feedback solutions. In some critical cases, active force feedback is the only solution, while in other cases passive aids suffice.status: publishe

An observation model and segmentation algorithm for skill acquisition of a deburring task

In robotic deburring applications it is desirable to have sensor feedback. The control strategies for this sensor feedback have to be adapted frequently to work piece and work tool parameters. This paper discusses a method for transferring the skill of a human operator to a control strategy that can cope with these changes in parameters. The human skill is transferred by an indirect learning method. The human actions are modeled as an impedance controller whose parameters are adapted by observations of the deburring process state. The non-linear relation between the process state and the controller parameters is learnt by a neural network. To apply this method, it is necessary that the observations are independent of the controller actions. This is shown for an observation model that is derived from a process model for deburring and experimentally verified. Segmentation of the training data is done by analyzing the summed normalized innovation squared value (SNIS) of a static Kalman filter.status: publishe

Learning a Predictive Model of Human Gait for the Control of a Lower-limb Exoskeleton

Abstract— For an intelligent dynamic motion interaction between a human and a lower-limb exoskeleton, it is necessary to predict the future evolution of the joint gait trajectories and to detect which phase of the gait pattern is currently active. A model of the gait trajectories and of the variations on these trajectories is learned from an example data set. A gait prediction module, based on a statistical latent variable model, is able to predict, in real-time, the future evolution of a joint trajectory, an estimate of the uncertainty on this prediction, the timing along the trajectory and the consistency of the measurements with the learned model. The proposed method is validated using a data set of 54 trials of children walking at three different velocitiesstatus: publishe

Constraint-Based Robot Control for BMI Applications

This presentation discusses robotic assistive devices using invasive brain- machine interfaces (BMI) and discusses a shared control methodology such that the autonomy of the system can be gradually shifted from the robot controller to the subject. We expect that this will significantly improve the learning curve of the subjectstatus: Published onlin