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Learning control of robot manipulators in task space

Learning control of robot manipulators in task space

Several problems were addressed via the design of learning controllers and their extensions. However, almost all of the learning controllers designed for robot manipulators were joint space controllers. That is; main control objectives were to track periodic joint positions as opposed to tracking a periodic end-effector posi- tion. Only a few past works addressed designing iterative learning controllers where the desired trajectory is spec- ified in task space [24,25]. For both designs, the conver- gence of the task space tracking errors to the origin were guaranteed. However, requiring the calculation of inverse kinematics on position level was the main shortcoming of the proposed controllers. Specifically, solving for the inverse kinematics problem typically involves solving a nonlinear system of equations with trigonometric func- tions. Issues such as singularity, multiple (non-unique) solutions (as in the case of ‘elbow up’ and ‘elbow down’ configurations for a robot arm), and no solution (as in the case in which the specified trajectory goes beyond the workspace of the mechanism) can often come up, further complicating the solution process. The complex- ity in the inverse kinematics problem is compounded even more for parallel link manipulators [26]. In [27], the problem of operational/task space tracking control of a robot manipulator is considered where simulation results implemented on a two link planar robot are pre- sented to illustrate the viability of the proposed learning control scheme.
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Learning Task-Space Synergies using Riemannian Geometry

Learning Task-Space Synergies using Riemannian Geometry

Task-space synergies require a suitable parameterization of robot pose, which involves both position and orientation. Since no parameterization of orientation exists that is global singularity free and Euclidean, the typical methods available for imitation learning and LQR cannot directly be applied. We build upon the probabilistic framework for imitation learning on Riemannian manifolds introduced in our previous work [5]. This framework allows us to learn distributions over robot poses whose support is contained in a regular geodesic ball [6]. In practice, this restricts the orientation data to lie within a ±π radius of the empirical mean (the Riemannian center of mass). This is achieved by encoding robot poses as elements on the manifold R 3 × S 3 —The
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Adaptive Fuzzy Control of Puma Robot Manipulator in Task Space with Unknown Dynamic and Uncertain Kinematic

Adaptive Fuzzy Control of Puma Robot Manipulator in Task Space with Unknown Dynamic and Uncertain Kinematic

To investigate the control performance using uncertain Jacobian matrix, 50 percent uncertainty is considered for kinematic parameters (Denavit Hartenberg parameters). The Jacobian matrix is hence inexact and imperfect error is transmitted to joint space. The control system is applied to PUMA robot to track a circle the same as the last subsection. Tracking error in Cartesian space is displayed in Figure 13, and the motor voltages are shown in Figure 14. Tracking error increases in comparison with simulation B. The motor voltages are increased in unstable manner to compensate the error. Therefore, the system is out of control. It can be found that the proposed control does not have acceptable performance if there exists uncertainty in Jacobian matrix. To overcome this problem, an improved adaptive fuzzy task space control is presented in the next section.
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Task-Space and Null-Space Control Design for Robotic-Assisted Minimally Invasive Surgery

Task-Space and Null-Space Control Design for Robotic-Assisted Minimally Invasive Surgery

which minimizes the robot kinetic energy [2], [8]. One advantage of truncated Jacobian solutions for robots that are not intrinsically redundant is the possibility to utilize the null space to optimize another objective function, very useful in robotic-assisted MIS. If external manipulation of the orientation is desired, a full Jacobian can be used, with zero values for the orientation vector (keeping the same task space controller) 3 .

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From Taxonomy to Requirements: A Task Space Partitioning Approach

From Taxonomy to Requirements: A Task Space Partitioning Approach

We present a taxonomy-driven approach to requirements specifica- tion in a large-scale project setting, drawing on our work to develop visualization dashboards for improving the quality of healthcare. Our aim is to overcome some of the limitations of the qualitative methods that are typically used for requirements analysis. When applied alone, methods like interviews fall short in identifying the full set of functionalities that a visualization system should support. We present a five-stage pipeline to structure user task elicitation and analysis around well-established taxonomic dimensions, and make the following contributions: (i) criteria for selecting dimensions from the large body of task taxonomies in the literature,, (ii) use of three particular dimensions (granularity, type cardinality and target) to create materials for a requirements analysis workshop with domain experts, (iii) a method for characterizing the task space that was produced by the experts in the workshop, (iv) a decision tree that partitions that space and maps it to visualization design alternatives, and (v) validating our approach by testing the decision tree against new tasks that collected through interviews with further domain experts.
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A Data Model and Task Space for Data of Interest (DOI) Eye-Tracking Analyses

A Data Model and Task Space for Data of Interest (DOI) Eye-Tracking Analyses

We pursued the contributions listed in Section 1 using a parallel two-folded approach. On the one hand, we consid- ered DOI data that can be collected in eye-tracking exper- iments, and questions it can answer, in a purely concep- tual way. We focused our efforts in imagining exhaustive data models that can describe and support a full range of possible eye-tracking experiments, including many users and diverse visual stimuli (e.g., interactive), data (e.g., time varying data), and research goals (e.g., understanding visual perception, understanding higher-level cognitive and data foraging behavior). We then followed guidelines from state of the art generative task-frameworks, in particular those of Andrienko et al. [6] and Roth [5], to consider questions this data can answer. This approach aligns with conceptual efforts at structuring tasks and analytic requirements in various domains [6], [37], [40], [46].
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Task-specific effects of reward on task switching

Task-specific effects of reward on task switching

two possibilities make opposing predictions related to the SC (cf. Gilbert & Shallice, 2002 ). By definition, increasing the strength of neural pathways that map task-related inputs to outputs causes the task to become more automatic or easier to execute (Cohen et al., 1990 ). As discussed above, these stronger connection strengths should result in larger SCs when switching to easy tasks (i.e., to the fast and accurate, reinforced task) compared to switching to hard tasks (i.e., to the slow and inaccurate, non-reinforced task) (Monsell, 2003 ) (Fig. 1 , left). For example, Yeung and Monsell ( 2003b ) observed that simply practicing a task induces a PASC. By contrast, the HRL account predicts that the reinforcement will increase top-down control over the reinforced task, causing performance of that task to be fast and accurate. In this case, switching to the slow and inaccurate, non-reinforced task will yield a relatively larger SC—that is, in a non-paradoxical asymmetric SC (NASC)—due to the disengagement of control from the faster and more accurate task (Fig. 1 , right). Therefore, the pattern of SCs should distinguish these two accounts: whereas reinforcement of stimulus–response pathways according to principles of simple RL should lead to PASCs, we predicted that reinforcement of task representations that apply top-down control according to principles of HRL would yield NASCs.
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All Space Will be Public Space

All Space Will be Public Space

[43  CLICK  TO  BLANK  SPACE]  Of  course,  the  implications  of  living  in  public   are  not  all  scary.    Some  are  quite  exciting.    For  example,  we  are  moving   from  a  society  with  (near)  universal  literacy  to  one  with  (near)  universal   authorship. 14    Everyone  in  public  is  performing,  everyone  creates…and  the   nature  of  networked  publics  is  that  our  inscriptions  persist.      

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The suprachoroidal space: from potential space to a space with potential

The suprachoroidal space: from potential space to a space with potential

Abstract: Recent advances have made it possible to image the suprachoroidal space, and the understanding of its clinical applications is currently being greatly expanded. This opinion piece covers the advances in imaging techniques that enable the demonstration of the suprachoroidal space, and its implication in various retinal pathologies. It also reviews its potential uses as a route for drug delivery for the treatment of retinal diseases, and its use in innovative surgical techniques. Current research is leading the way for the suprachoroidal space to be an aspect of retinal disease diagnosis, monitoring, medical treatment, and surgical manipulation.
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Shapes Forms Forms Form Space Space Space

Shapes Forms Forms Form Space Space Space

Let's take a look at how Space effects a work of art and just how artists use Space to create interest in their works.... Value refers to the lightness or darkness of a color2[r]

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Task integration in the Discrete Sequence Production Task

Task integration in the Discrete Sequence Production Task

In the proposed model, the cognitive processor has a cognitive representation of the motor sequence (Verwey, Shea & Wright, 2015). Until now, there is just a notion that there is a cognitive representation about sequences. The cognitive processor is still committed to the task after uploading the information about the motor sequence, while no indication has been found that it is responsible for concatenating motor chunks (Verwey, 2010). We aim to discover more precisely, which role the cognitive processor plays in discrete sequence production.

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Appraisal Task Force Report Task Force Members:

Appraisal Task Force Report Task Force Members:

Something along similar lines can be said about the need to appraise records early in their life, when the appraiser can see a fully operational live system. In fact, modern records schedules, which in effect constitute a series of disposition decisions class by class, are often created before records are created. The difficulty in the digital environment, one discussed widely in the literature reviewed by the task force (see Appendix 3), is that designers of digital systems, particularly in the early years of office automation, paid little or no attention to questions of the disposition of records. It was this fact, rather than any inherent characteristic of the digital environment, that pushed archivists to suppose that appraisal capability had to be built into the design of systems. The need to appraise early in the digital environment is, by contrast, vital for quite another reason. Information about the technological context, much of it now contained in the systems themselves, cannot be found or reconstructed, we know from sad experience, even a short time after systems have reached the end of their life. It is exceedingly difficult to assess the authenticity of such records, determine the feasibility of preserving them, and understand them in the future, without this information about the technological context. Once again, archivists are familiar with the difficulties of having to construe the context of the records with little else to speak of it but the records themselves. This is hardly an argument for expecting the acuity of Jean Mabillon (the Benedictine Monk who laid out the concepts and tenets of diplomatics in the seventeenth century) in all future users of electronic records where information about their technological context is concerned. Rather, considerable information about the technological context of the records needs to accompany them through time in order for the records to be intelligible in anything like an acceptable fashion in years and centuries to come. It is a principal task of appraisal to gather this information so that it can be associated with the records.
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Target Space ≠ Space

Target Space ≠ Space

Matsubara (2013, §6) argues along similar lines (as we did in Huggett and Wuthrich 2013), though he takes (with misgivings) the shared commitments of the duals to be ‘structure’, which I’m not convinced illuminates these matters without further elaboration. However, his account does not recognize the role of phenomenal space in the logic of the situation. For instance, at one point (485-6) he correctly says that ‘space in the mathematical formalism’ (i.e., according to T or T 0 read literally) has unphysical properties, especially determinate radius. And he goes on to infer that ‘physical space’ is indeterminate with respect to such properties. Here he is referring to target space as I have understood it, and we are in agreement. But phenomenal space is also physical, and has determinate radius, R in our example. Of course, phenomenal space is derived in some sense, from such processes as those analysed by Brandenberger and Vafa, but that does not mean it is not physical: things reduced to physical things are also physical. Only by ignoring phenomenal space, or incorrectly asserting that only target space is physical, can one reach Matsubara’s stated conclusion.
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Determinants of task order in dual-task situations

Determinants of task order in dual-task situations

There are few studies which have compared TOJ with dual tasks. One of those few is De Jong (1995), who used the TOJ task as a control condition for a dual-task paradigm with random task order. With this control condition, he wanted to ensure that the participants were able to correctly identify the order of stimulus presentation. His results showed, that the number of response reversals (i.e., trials in which the participants did not respond in the order of task presentation) was higher in the dual-task conditions than in the TOJ condition. Despite this result, he thought it unlikely that this difference in the number of response reversals was caused by the additional requirement to carry out a choice RT task in the dual-task condition. His assumption was based on findings by Sternberg et al. (1971). However, Sternberg et al. (1971), who presented an auditory and a cutaneous stimulus, asked their participants to pull a lever in response to a cue before one of the two stimuli and then, in a second step, judge the
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Task-switching costs without task-switching

Task-switching costs without task-switching

There is a small problem in the present calculations. In order to make calculations of interference simpler, I assume that each feature incompatibility (i.e., the cue, the shape, and the colour) creates the same amount of interference (1 unit per feature). However, it seems very likely that the colour-response, shape-response, and cue-response mappings produce different amounts of interference. This is not a major problem, because if we only look at the difference between the switch and repeat trials, differences between the three types of feature-response mappings may balance each other out so that the difference between the two conditions remains the same. However, if researchers want to predict interference in single trials, then this would become a problem. For example, if trial n and trial m both had one unit of interference due to trial n - 1 and trial m - 1, respectively then the current method would assume that trial n and m receive the same amount of interference. However, if trial n received interference from a colour-response mapping, and trial m received interference from a shape-response mapping,the amounts of interference in trial n and trial m might be different. The calculation method is not specific enough yet to reflect this difference. Therefore, I describe an interference task-switching paradigm that should create equal amounts of interference, as a potential future study (see Figure 5.10). The results of this paradigm may further confirm or refute the modified proactive interference account.
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Face-to-face and online interactions - is a task a task?

Face-to-face and online interactions - is a task a task?

also has to be ‘interaction as a social activity’ which Wang (2004, p.91) defines as: “a socially reciprocal action involving two or more people”. Hampel and Hauck (2004, p. 67) summarise the necessity for social interaction in communicative language teaching as satisfying the demands of both second language acquisition theories and socio-cultural learning. This suggests that learners need not only to be exposed to comprehensible input and to produce comprehensible output, but also that they need to also interact to negotiate meaning as implied by Wang’s definition above. For Warschauer (1997, p. 487) a further qualification is that students must be encouraged to “conduct actively meaningful tasks and solve meaningful problems in an environment that reflects their own personal interest as well as the multiple purpose to which their knowledge will be put in the future”, and thus be prepared for their future independent use of the target language in the real world. This presupposes learner-oriented task set-up which according to Rüschoff (1999) must also be collaborative in nature in order to allow for knowledge construction.
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A model of learning task-specific knowledge for a new task

A model of learning task-specific knowledge for a new task

People may not know anything about schedules, but they do know something about lists, and how to construct them. Sup- pose we need to make a schedule. We may use knowledge about lists to start with. How do we make a list? First we have to find a first item for the list, a begin. Once we have a begin, we find a next task until we are done. But how do we find something to begin with, and how do find a next task? We may choose to handle these problems making them sub- goals, or we may try to find mappings between ‘begin’ and ‘next’ and terms in the scheduling problem. For example, a mapping can be made between ‘next’ and an order-constraint in the scheduling problem. The result is a modified version of the list-building declarative rules, with ‘list’ substituted by ‘schedule’ and ‘next’ substituted by ‘order’. Note that for sake of the explanation, the terms ‘list’, ‘begin’, ‘before’ and ‘next’ will be used to refer to general terms, and ‘schedule’ and ‘order’ to refer to task-specific terms. Except for knowl- edge on how to build a list, the analogy between a schedule and a list may also offer knowledge on how to retain a list in memory by rehearsal.
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Understanding the task

Understanding the task

Walk-Through/Talk-Through The walk-through/talk-through is a simple process which consists of an experienced person demonstrating how the task is carried out. Each step, no matter how minor (pressing a switch) or effortful (walking to the other end of the premises to collect a tool), is demonstrated. This includes communicating with other people, retrieving information from computers or display systems and making decisions on information retrieved. In addition to the demonstrator, it may also be helpful to have an engineer and/or health and safety professional in the team. As the procedure is demonstrated, the team should identify what might go wrong if a particular step is not carried out or incorrectly carried out.
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Task outline:

Task outline:

This task requires pupils to write a letter to a magazine arguing against the suggestion made in an earlier article that teenagers should always listen to adult advice. As preparation, pupils explore a number of arguments and counter arguments, paying par- ticular attention to the language features involved in the statements. They read the original magazine article and then write a let- ter arguing against the views expressed in it and making some additional points of their own.

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Task and Dataflow

Task and Dataflow

DataflowTaskGraph Main class which extends the BaseDataflowTaskGraph, first it validate the task graph then store the directed edges into the task map which consists of source task vertex a[r]

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