Task-level Control Research Articles

Associations between job demand-control-support and high burnout risk among physicians in Sweden: a cross-sectional study

BackgroundThe knowledge about job demands, control, and support, and their potential associations with burnout risk among physicians in Sweden, is limited. This study aimed to explore (i) factors of the JobDemand-Control-Support (J-DCS) model across different groups of physicians in Sweden, (ii) their association with high burnout risk, and (iii) the potential buffering impact of job control and support.MethodsCross-sectional data from the Swedish Longitudinal Occupational Health in Healthcare Survey (LOHHCS) study cohort was used. In 2021, a total of 2032 respondents submitted questionnaire data comprising J-DCS measures (i.e., job demands, workplace control and task-level control, and social support from peers and managers). Burnout risk was measured using the Burnout Assessment Tool. Binary logistic regression models were used to investigate the associations between the J-DCS variables and high burnout risk. Interaction analysis was performed to explore any moderation of the associations.ResultsJob demands were significantly associated with increased odds of high burnout risk (odds ratio (OR) 2.71, 95% confidence interval (CI) 1.91–3.84. Workplace control (OR 0.50, 95% CI 0.35–0.71) and peer support (OR 0.61, 95% CI 0.48–0.77) were significantly associated with reduced odds of high burnout risk. The interaction analysis showed no significant moderation of the association between job demands and high burnout risk by either peer support or workplace control, and no buffering impact was found.ConclusionJob demands were associated with high burnout risk among physicians in Sweden. Although workplace control and peer support had inverse associations with high burnout risk, no moderation or buffering impact on the association between job demands and high burnout risk was found. Longitudinal studies are needed to confirm these associations.

Mechanisms of sensorimotor adaptation in a hierarchical state feedback control model of speech.

Upon perceiving sensory errors during movements, the human sensorimotor system updates future movements to compensate for the errors, a phenomenon called sensorimotor adaptation. One component of this adaptation is thought to be driven by sensory prediction errors-discrepancies between predicted and actual sensory feedback. However, the mechanisms by which prediction errors drive adaptation remain unclear. Here, auditory prediction error-based mechanisms involved in speech auditory-motor adaptation were examined via the feedback aware control of tasks in speech (FACTS) model. Consistent with theoretical perspectives in both non-speech and speech motor control, the hierarchical architecture of FACTS relies on both the higher-level task (vocal tract constrictions) as well as lower-level articulatory state representations. Importantly, FACTS also computes sensory prediction errors as a part of its state feedback control mechanism, a well-established framework in the field of motor control. We explored potential adaptation mechanisms and found that adaptive behavior was present only when prediction errors updated the articulatory-to-task state transformation. In contrast, designs in which prediction errors updated forward sensory prediction models alone did not generate adaptation. Thus, FACTS demonstrated that 1) prediction errors can drive adaptation through task-level updates, and 2) adaptation is likely driven by updates to task-level control rather than (only) to forward predictive models. Additionally, simulating adaptation with FACTS generated a number of important hypotheses regarding previously reported phenomena such as identifying the source(s) of incomplete adaptation and driving factor(s) for changes in the second formant frequency during adaptation to the first formant perturbation. The proposed model design paves the way for a hierarchical state feedback control framework to be examined in the context of sensorimotor adaptation in both speech and non-speech effector systems.

Tracing mathematics engagement in the first year of high school: relationships between prior experience, observed support, and task-level emotion and motivation

We examined the relationships between different aspects of mathematics engagement for 285 students in their first year of high school in the United States. Path Analyses were used to trace the relationships between students’ self-reported prior motivation and appraisals of control and value of mathematics, perceptions of teacher support and peer support. These variables and observed teacher and peer support as coded from video by researchers, were examined as potentially impacting students’ self-reported in-the moment affect and task-level control and value appraisals Our results showed three key contributions. First, significant paths corresponded to relationships predicted by Control Value Theory (CVT) across a particularly robust set of variables and over the course of their first semester in high school. Second, results added further nuance by considering the objects that students’ in-the-moment emotions were directed toward, showing distinctions between positive and negative emotions directed at the mathematics task, students’ teachers and peers, and selves. Third, results more closely considered the impact of both observed and perceived aspect of support from peers and teachers in the classroom, in both its academic and social forms. Implications are discussed for theory and practice.

Kinematic trajectories in response to speed perturbations in walking suggest modular task-level control of leg angle and length.

Navigating complex terrains requires dynamic interactions between the substrate, musculoskeletal and sensorimotor systems. Current perturbation studies have mostly used visible terrain height perturbations, which do not allow us to distinguish among the neuromechanical contributions of feedforward control, feedback-mediated and mechanical perturbation responses. Here, we use treadmill belt speed perturbations to induce a targeted perturbation to foot speed only, and without terrain-induced changes in joint posture and leg loading at stance onset. Based on previous studies suggesting a proximo-distal gradient in neuromechanical control, we hypothesized that distal joints would exhibit larger changes in joint kinematics, compared to proximal joints. Additionally, we expected birds to use feedforward strategies to increase the intrinsic stability of gait. To test these hypotheses, seven adult guinea fowl were video recorded while walking on a motorized treadmill, during both steady and perturbed trials. Perturbations consisted of repeated exposures to a deceleration and acceleration of the treadmill belt speed. Surprisingly, we found that joint angular trajectories and center of mass fluctuations remain very similar, despite substantial perturbation of foot velocity by the treadmill belt. Hip joint angular trajectories exhibit the largest changes, with the birds adopting a slightly more flexed position across all perturbed strides. Additionally, we observed increased stride duration across all strides, consistent with feedforward changes in the control strategy. The speed perturbations mainly influenced the timing of stance and swing, with the largest kinematic changes in the strides directly following a deceleration. Our findings do not support the general hypothesis of a proximo-distal gradient in joint control, as distal joint kinematics remain largely unchanged. Instead, we find that leg angular trajectory and the timing of stance and swing are most sensitive to this specific perturbation, and leg length actuation remains largely unchanged. Our results are consistent with modular task-level control of leg length and leg angle actuation, with different neuromechanical control and perturbation sensitivity in each actuation mode. Distal joints appear to be sensitive to changes in vertical loading but not foot fore-aft velocity. Future directions should include in vivo studies of muscle activation and force-length dynamics to provide more direct evidence of the sensorimotor control strategies for stability in response to belt speed perturbations.

Optimization of Fuel Economy Using Optimal Controls on Regulatory and Real-World Driving Cycles

<div class="section abstract"><div class="htmlview paragraph">In recent years, electrification of vehicle powertrains has become more mainstream to meet regulatory fuel economy and emissions requirements. Amongst the many challenges involved with powertrain electrification, developing supervisory controls and energy management of hybrid electric vehicle powertrains involves significant challenges due to multiple power sources involved. Optimizing energy management for a hybrid electric vehicle largely involves two sets of tasks: component level or low-level control task and supervisory level or high-level control task. In addition to complexity within powertrain controls, advanced driver assistance systems and the associated chassis controls are also continuing to become more complex. However, opportunities exist to optimize energy management when a cohesive interaction between chassis and powertrain controls can be realized. To optimize energy management along a given route, certain information such as the projected vehicle route, driver behavior, and battery charge level should be considered. In this paper, simulation models of a parallel P0/P4 hybrid electric vehicle are presented, which optimize powertrain controls using the Dynamic Programming approach. This virtual vehicle model is exercised through the HWFET and FTP-75 regulatory driving cycles to establish a performance baseline in a controlled driving environment. For comparison to off-cycle driving, the virtual vehicle is then also exercised through a real-world driving scenario over real-world roads, with similar trip characteristics to the regulatory tests, but with real traffic conditions during the day. This comparison provides insights into how optimized real-world fuel economy results can differ compared to the controlled testing environment, and how predictive powertrain controls can offer “in-situ” optimization of energy management.</div></div>

Formal Verification for Task Description Languages. A Petri Net Approach.

One of the main challenges in verifying robotic systems is its asynchronous interaction with an unstructured environment, observed by imperfect sensors. Autonomous robot systems usually require some language to support task-level control. This paper presents an effective approach to apply formal verification methods for that kind of language. A main contribution of this method is to avoid modeling the robotic system with a specific formalism. The approach translates the task-level control models into a Petri net (PN) based representation. This is used to define new methods to analyze some task properties such as liveness, deadlock-freeness and terminability. The approach has been applied to the Task Description Language (TDL) and it is illustrated by experiments. The final goal is to create new tools within the application development environment to include formal verification as part of the normal software development cycle. The TDL to PN translator uses the Petri Net Markup Language (PNML) as its file format. This format permits interoperability with other Petri net tools that can also be used to analyze the PNs.

On-line collision avoidance for collaborative robot manipulators by adjusting off-line generated paths: An industrial use case

Human–robot collision avoidance is a key in collaborative robotics and in the framework of Industry 4.0. It plays an important role for achieving safety criteria while having humans and machines working side-by-side in unstructured and time-varying environment. This study introduces the subject of manipulator’s on-line collision avoidance into a real industrial application implementing typical sensors and a commonly used collaborative industrial manipulator, KUKA iiwa. In the proposed methodology, the human co-worker and the robot are represented by geometric primitives (capsules). The minimum distance and relative velocity between them is calculated, when human/obstacles are nearby the concept of hypothetical repulsion and attraction vectors is used. By coupling this concept with a mathematical representation of robot’s kinematics, a task level control with collision avoidance capability is achieved. Consequently, the off-line generated nominal path of the industrial task is modified on-the-fly so the robot is able to avoid collision with the co-worker safely while being able to fulfill the industrial operation. To guarantee motion continuity when switching between different tasks, the notion of repulsion-vector-reshaping is introduced. Tests on an assembly robotic cell in automotive industry show that the robot moves smoothly and avoids collisions successfully by adjusting the off-line generated nominal paths.

Control of Optical Clearing of Human Skin by Ellipsoidal Reflector Method

Current research deals with the problem of optical clearing during photometry by ellipsoidal reflectors. The specific problem is optical clearing of human skin, specifically the level control tasks during non-invasive conditions of experiment. In silico experiment involve Monte Carlo simulation of light propagation in multi-layered biological tissue and ellipsoidal reflector. The research was made for the set of human skin layers optical properties and their anatomic thicknesses. There were received photometric images in back scattered light for human skin from following parts of body: palm (hand), chest, abdomen, shoulder, back and thigh. Based on the zone analysis there were received illuminance dependencies of middle and external rings of photometric images from the optical clearing time. In addition, there was investigated the influence of optical clearing time on the character of light scattering on human skin from different parts of human body. Ref. 45, fig. 5, tabl. 2.

Stability of hand force production. II. Ascending and descending synergies

We combined the theory of neural control of movement with referent coordinates and the uncontrolled manifold hypothesis to investigate multifinger coordination. We tested hypotheses related to stabilization of performance by covarying control variables, translated into apparent stiffness and referent coordinate, at different levels of an assumed hierarchy of control. Subjects produced an accurate combination of total force and total moment of force with the four fingers under visual feedback on both variables and after feedback was partly or completely removed. The "inverse piano" device was used to estimate control variables. We observed strong synergies in the space of hypothetical control variables that stabilized total force and moment of force, as well as weaker synergies stabilizing individual finger forces; whereas the former were attenuated by alteration of visual feedback, the latter were much less affected. In addition, we investigated the organization of "ascending synergies" stabilizing task-level control variables by covaried adjustments of finger-level control variables. We observed intertrial covariation of individual fingers' referent coordinates that stabilized hand-level referent coordinate, but we observed no such covariation for apparent stiffness. The observations suggest the existence of both descending and ascending synergies in a hierarchical control system. They confirm a trade-off between synergies at different levels of control and corroborate the hypothesis on specialization of different fingers for the control of force and moment. The results provide strong evidence for the importance of central back-coupling loops in ensuring stability of action. NEW & NOTEWORTHY We expand analysis of action in the space of hypothetical control variables to hierarchically organized multieffector systems. We also introduce the novel concept of ascending synergies, which reflect covariation of control variables to individual effectors (fingers) that stabilize task-specific control variables at a hierarchically higher, task-specific level (hand).

Task-dependent modulation of spinal and transcortical stretch reflexes linked to motor learning rate.

It is generally believed that task-dependent control of body configuration ("posture") is achieved by adjusting voluntary motor activity and transcortical "long-latency" reflexes. Spinal monosynaptic circuits are thought not to be engaged in such task-level control. Similarly, being in a state of motor learning has been strongly associated only with an upregulation of feedback responses at transcortical latencies and beyond. In two separate experiments, the current study examined the task-dependent modulation of stretch reflexes by perturbing the hand of human subjects while they were waiting for a "Go" signal to move at the different stages of a classic kinematic learning task (visuomotor rotation). Although the subjects had to resist all haptic perturbations equally across task stages, the study leveraged that task-dependent feedback controllers may already be "loaded" at the movement anticipation stage. In addition to an upregulation of reflex gains during early exposure to the visual distortion, I found a relative inhibition of reflex responses in the "washout" stage (sensory realignment state). For more distal muscles (brachioradialis) this inhibition also extended to the monosynaptic reflex response ("R1"). Moreover, these R1 gains reflected individual motor learning performance in the visuomotor task. The results demonstrate that the system's "control policy" in visuomotor adaptation can also include inhibition of proprioceptive reflexes, and that aspects of this policy can affect monosynaptic spinal circuits. The latter finding suggests a novel form of state-related control, probably realized by independent control of fusimotor neurons, through which segmental circuits can tune to higher-level features of a sensorimotor task. (PsycINFO Database Record

Dealing with Complexity through Advanced Control Techniques

The control of complex dynamic systems, both in their behavior and in their mission, goes through the implementation of multi-loop control architectures based on information about the system internal state and from the environment, as well as on the mission plan state. This results in systems that are becoming increasingly autonomous, for which requirements in terms of safety and reliability, as well as expected performance, are increasingly high. Research works developed at ONERA in the field of control for autonomous systems cover all levels of the control architectures, which are basically structured with respect to temporal aspects, as well as the level of abstraction that they entail for the system dynamic. We will consider them in this paper by increasing level. We will discuss the advances achieved recently in the robust control techniques of uncertain dynamic systems generally implemented at the lower control level and we will discuss their extensions to consider input and output constraints, as well as the hybrid nature of most of the systems considered. To design task level control primitives that take place just above the previous control loops, we will introduce sensor-based robust and non-linear control techniques. These are based on information on the environment extracted from exteroceptive sensors, to adapt system behavior to uncertainties and perturbations. Multi-sensor and/or multi-objective controls will be discussed in this particular context. We will also present several recent results in the field of trajectory tracking based on visual navigation techniques in complex environments, which combine objectives and constraints within the same control architecture. We will discuss how model predictive control (MPC) techniques and advanced optimization techniques can be used for solving the resulting control problems. In addition, we will discuss several ongoing developments of these methods by exploiting distributed model predictive control techniques (DMPC) and predictive control of hybrid systems. Finally, integration with the control architectures at the upper level of reactive, predictive and distributed planning capabilities will be proposed to accommodate time constraints and uncertainties in decision.

Hybrid-state driven autonomous control for planar bipedal locomotion

The focus of this paper is on the development of a human inspired autonomous control scheme for a planar bipedal robot in a hybrid dynamical framework to realize human-like walking projected onto sagittal plane. In addition, a unified modelling scheme is presented for the biped dynamics incorporating the effects of various locomotion constraints due to varying feet-ground contact states, unilateral ground contact force, contact friction cone, passive dynamics associated with floating base etc. along with a practical impact velocity map on heel strike event. The autonomous control synthesis is formulated as a two-level hierarchical control algorithm with a hybrid-state based supervisory control in outer level and an integrated set of constrained motion control primitives, called task level control, in inner level. The supervisory level control is designed based on a human inspired heuristic approach whereas the task level control is formulated as a quadratic optimization problem with linear constraints. The explicit analytic solution obtained in terms of joint acceleration and ground contact force is used in turn to generate the joint torque command based on inverse dynamics model of the biped. The proposed controller framework is named as Hybrid-state Driven Autonomous Control (HyDAC). Unlike many other bipedal control schemes, HyDAC does not require a preplanned trajectory or orbit in terms of joint variables for locomotion control. Moreover, it is built upon a set of basic motion control primitives similar to those in human walk which provides a transparent and easily adaptable structure for the controller. These features make HyDAC framework suitable for bipedal walk on terrain with step and slope discontinuities without a priori gait optimization. The stability and agility of the proposed control scheme are demonstrated through dynamic model simulation of a 12-link planar biped having similar size and mass properties of an adult sized human being restricted to sagittal plane. Simulation results show that the planar biped is able to walk for a speed range of 0.1–2 m/s on level terrain and for a ground slope range of +/−20 deg for 1 m/s speed.

Optimal control of 2-wheeled mobile robot at energy performance index

The paper presents the application of the optimal control method at the energy performance index towards motion control of the 2-wheeled mobile robot. With the use of the proposed method of control the 2-wheeled mobile robot can realise effectively the desired trajectory. The problem of motion control of mobile robots is usually neglected and thus performance of the realisation of the high level control tasks is limited.

Swing-leg trajectory of running guinea fowl suggests task-level priority of force regulation rather than disturbance rejection.

To achieve robust and stable legged locomotion in uneven terrain, animals must effectively coordinate limb swing and stance phases, which involve distinct yet coupled dynamics. Recent theoretical studies have highlighted the critical influence of swing-leg trajectory on stability, disturbance rejection, leg loading and economy of walking and running. Yet, simulations suggest that not all these factors can be simultaneously optimized. A potential trade-off arises between the optimal swing-leg trajectory for disturbance rejection (to maintain steady gait) versus regulation of leg loading (for injury avoidance and economy). Here we investigate how running guinea fowl manage this potential trade-off by comparing experimental data to predictions of hypothesis-based simulations of running over a terrain drop perturbation. We use a simple model to predict swing-leg trajectory and running dynamics. In simulations, we generate optimized swing-leg trajectories based upon specific hypotheses for task-level control priorities. We optimized swing trajectories to achieve i) constant peak force, ii) constant axial impulse, or iii) perfect disturbance rejection (steady gait) in the stance following a terrain drop. We compare simulation predictions to experimental data on guinea fowl running over a visible step down. Swing and stance dynamics of running guinea fowl closely match simulations optimized to regulate leg loading (priorities i and ii), and do not match the simulations optimized for disturbance rejection (priority iii). The simulations reinforce previous findings that swing-leg trajectory targeting disturbance rejection demands large increases in stance leg force following a terrain drop. Guinea fowl negotiate a downward step using unsteady dynamics with forward acceleration, and recover to steady gait in subsequent steps. Our results suggest that guinea fowl use swing-leg trajectory consistent with priority for load regulation, and not for steadiness of gait. Swing-leg trajectory optimized for load regulation may facilitate economy and injury avoidance in uneven terrain.

FunctionalMRImapping of visual function and selective attention for performance assessment and presurgical planning using conjunctive visual search

BackgroundAccurate mapping of visual function and selective attention using fMRI is important in the study of human performance as well as in presurgical treatment planning of lesions in or near visual centers of the brain. Conjunctive visual search (CVS) is a useful tool for mapping visual function during fMRI because of its greater activation extent compared with high-capacity parallel search processes.AimsThe purpose of this work was to develop and evaluate a CVS that was capable of generating consistent activation in the basic and higher level visual areas of the brain by using a high number of distractors as well as an optimized contrast condition.Materials and methodsImages from 10 healthy volunteers were analyzed and brain regions of greatest activation and deactivation were determined using a nonbiased decomposition of the results at the hemisphere, lobe, and gyrus levels. The results were quantified in terms of activation and deactivation extent and mean z-statistic.ResultsThe proposed CVS was found to generate robust activation of the occipital lobe, as well as regions in the middle frontal gyrus associated with coordinating eye movements and in regions of the insula associated with task-level control and focal attention. As expected, the task demonstrated deactivation patterns commonly implicated in the default-mode network. Further deactivation was noted in the posterior region of the cerebellum, most likely associated with the formation of optimal search strategy.ConclusionWe believe the task will be useful in studies of visual and selective attention in the neuroscience community as well as in mapping visual function in clinical fMRI.

Social Relations at the Collective Level: The Meaning and Measurement of Collective Control in Research on the Psychosocial Work Environment

In this article, we suggest that organizational-level social relations should be defined and measured as workplace norms. We base this argument on new research on the components of the psychosocial work environment and on the availability of new techniques for measuring and analyzing workplace norms as organizational properties. Workplace norms emerge from interactions and negotiations among organizational actors, through which patterns of behavior, attitudes, and perspectives become defined as legitimate. This is an underestimated dimension of the psychosocial work environment that should be assessed with two types of data: self-reports by employees of their experiences in the workplace (task-level control) and self-reports by employees and employers of collective or group-level norms. Hierarchical linear modeling is an especially useful tool for analyzing the relationships between workplace norms and different organizational outcomes because it allows researchers to separate the effects of individual-level variables from group or organizational-level factors. Our approach is anchored in the Nordic perspective of the work environment developed over the past 50 years.

Optimization of muscle activity for task-level goals predicts complex changes in limb forces across biomechanical contexts.

Optimality principles have been proposed as a general framework for understanding motor control in animals and humans largely based on their ability to predict general features movement in idealized motor tasks. However, generalizing these concepts past proof-of-principle to understand the neuromechanical transformation from task-level control to detailed execution-level muscle activity and forces during behaviorally-relevant motor tasks has proved difficult. In an unrestrained balance task in cats, we demonstrate that achieving task-level constraints center of mass forces and moments while minimizing control effort predicts detailed patterns of muscle activity and ground reaction forces in an anatomically-realistic musculoskeletal model. Whereas optimization is typically used to resolve redundancy at a single level of the motor hierarchy, we simultaneously resolved redundancy across both muscles and limbs and directly compared predictions to experimental measures across multiple perturbation directions that elicit different intra- and interlimb coordination patterns. Further, although some candidate task-level variables and cost functions generated indistinguishable predictions in a single biomechanical context, we identified a common optimization framework that could predict up to 48 experimental conditions per animal (n = 3) across both perturbation directions and different biomechanical contexts created by altering animals' postural configuration. Predictions were further improved by imposing experimentally-derived muscle synergy constraints, suggesting additional task variables or costs that may be relevant to the neural control of balance. These results suggested that reduced-dimension neural control mechanisms such as muscle synergies can achieve similar kinetics to the optimal solution, but with increased control effort (≈2×) compared to individual muscle control. Our results are consistent with the idea that hierarchical, task-level neural control mechanisms previously associated with voluntary tasks may also be used in automatic brainstem-mediated pathways for balance.

Task-Level Control of Motion and Constraint Forces in Holonomically Constrained Robotic Systems

AbstractHolonomically constrained multibody systems constitute an important class of robotic systems. Under holonomic constraints motion is restricted to a constrained motion manifold within configuration space. The task-level control scheme presented here provides an effective approach to executing motion control in the presence of constraints. This scheme also allows for the simultaneous specification of desired constraint forces, given sufficient actuation, by exposing both motion coordinates and constraint forces within the control formalism. This allows for substantial flexibility in control synthesis and, thus, this methodology can be extensively applied to a wide range of holonomically constrained systems. An example is presented that demonstrates the efficacy of the analytical framework and its ease of implementation in practical robotic control problems involving constraints.

Role of the anterior insula in task-level control and focal attention

In humans, the anterior insula (aI) has been the topic of considerable research and ascribed a vast number of functional properties by way of neuroimaging and lesion studies. Here, we argue that the aI, at least in part, plays a role in domain-general attentional control and highlight studies (Dosenbach et al. 2006; Dosenbach et al. 2007) supporting this view. Additionally, we discuss a study (Ploran et al. 2007) that implicates aI in processes related to the capture of focal attention. Task-level control and focal attention may or may not reflect information processing supported by a single functional area (within the aI). Therefore, we apply a novel technique (Cohen et al. 2008) that utilizes resting state functional connectivity MRI (rs-fcMRI) to determine whether separable regions exist within the aI. rs-fcMRI mapping suggests that the ventral portion of the aI is distinguishable from more dorsal/anterior regions, which are themselves distinct from more posterior parts of the aI. When these regions are applied to functional MRI (fMRI) data, the ventral and dorsal/anterior regions support processes potentially related to both task-level control and focal attention, whereas the more posterior aI regions did not. These findings suggest that there exists some functional heterogeneity within aI that may subserve related but distinct types of higher-order cognitive processing.Electronic supplementary materialThe online version of this article (doi:10.1007/s00429-010-0260-2) contains supplementary material, which is available to authorized users.

Vision-Based Task-Level Control of a Flexible-Link Manipulator

This paper discusses a vision-based approach to implement task-level control in flexible-link manipulators. The proposed approach emphasizes the advantage of using vision in the control of flexible manipulators. It is pointed out that taking advantage of the inherent robustness, implementation of an image-based visual servo can be regarded as a synthetic solution to precise task-level control of flexible manipulators. This approach is implemented in a three-dimensional flexible-link manipulator. The implementation makes good use of filters in decoupling task-level control and vibration suppression control. Moreover, we point out that although the robustness of the approach can help to overcome the difficulty in control resulting from the complex measurement of the link's elastic deformation, it lacks in capability of tip trajectory specification. This problem is analyzed in this research and it leads to the proposal for the integration of the image interpolation technique. This technique makes the proposed approach adequate for tasks involved with complex tip trajectories. For flexible-link manipulators, the proposed approach with the remedy is the first vision-based synthetic solution that attempts to make a flexible manipulator usable for a practical task.