Gaze Allocation Analysis for a Visually Guided Manipulation Task

Nuñez-Varela, José I.; Ravindran, Balaraman; Wyatt, Jeremy L.

doi:10.1007/978-3-642-33093-3_5

Cited by 3 publications

(5 citation statements)

References 13 publications

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“…It is important to evaluate how much these differences affect task performance. We have already demonstrated that the RUG gaze strategy outperforms two common baselines: random and round robin schemes [Nunez-Varela et al 2012b]; we have also characterised this model's robustness in terms of variations of three environmental variables [Nunez-Varela et al 2012a]. In this article, our new results show how the RUG model is the most effective of the three strategies presented earlier, along with the baselines.…”

Section: Gaze Control Based On Rewards Uncertainty and Gain (Rug)mentioning

confidence: 67%

“…This analysis makes use of the pick & place task (Section 1.2), which consists of picking up objects from the tabletop and then placing them inside one of two containers. Even though the aim is to make comparisons between our three gaze control models, two more gaze schemes are presented that serve as a common baseline and provide a more general analysis [Nunez-Varela et al 2012a]:…”

Section: Experimental Analysis For the Pick And Place Taskmentioning

confidence: 99%

“…In order to distinguish these models, they are examined in two ways: relative performance in controlled conditions and goodness of fit to human data. Thus, our second contribution is a study of the performance of each model in a simulation of a humanoid robot performing a manipulation task previously explored in Nunez-Varela et al [2012a, 2012b. This article plots the degradation of each strategy's performance as (i) grasp sensitivity increases, (ii) observation reliability decreases, and (iii) the field of view (FoV) narrows.…”

Section: Introductionmentioning

confidence: 98%

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Models of gaze control for manipulation tasks

Nuñez-Varela

Wyatt

2013

ACM Trans. Appl. Percept.

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Human studies have shown that gaze shifts are mostly driven by the current task demands. In manipulation tasks, gaze leads action to the next manipulation target. One explanation is that fixations gather information about task relevant properties, where task relevance is signalled by reward. This work presents new computational models of gaze shifting, where the agent imagines ahead in time the informational effects of possible gaze fixations. Building on our previous work, the contributions of this article are: (i) the presentation of two new gaze control models, (ii) comparison of their performance to our previous model, (iii) results showing the fit of all these models to previously published human data, and (iv) integration of a visual search process. The first new model selects the gaze that most reduces positional uncertainty of landmarks (Unc), and the second maximises expected rewards by reducing positional uncertainty (RU). Our previous approach maximises the expected gain in cumulative reward by reducing positional uncertainty (RUG). In experiment ii the models are tested on a simulated humanoid robot performing a manipulation task, and each model's performance is characterised by varying three environmental variables. This experiment provides evidence that the RUG model has the best overall performance. In experiment iii, we compare the hand-eye coordination timings of the models in a robot simulation to those obtained from human data. This provides evidence that only the models that incorporate both uncertainty and reward (RU and RUG) match human data.

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Section: Gaze Control Based On Rewards Uncertainty and Gain (Rug)mentioning

confidence: 67%

Section: Experimental Analysis For the Pick And Place Taskmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

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Models of gaze control for manipulation tasks

Nuñez-Varela

Wyatt

2013

ACM Trans. Appl. Percept.

Self Cite

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“…Shahzad and Mehmood (2010) presented a master’s thesis on controlling articulated robot arms using eye tracking. ( Nunez-Varela, 2012 ), focusing on task-driven object manipulation by gaze locations on the object (intention read from gaze). The MyEccPupil (HomeBrace GmbH) is a commercially available eye tracking controller for a wheelchair-mounted robotic arm.…”

Section: Resultsmentioning

confidence: 99%

A scoping review of gaze and eye tracking-based control methods for assistive robotic arms

Fischer-Janzen,

Wendt,

Van Laerhoven

2024

Front. Robot. AI

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Background: Assistive Robotic Arms are designed to assist physically disabled people with daily activities. Existing joysticks and head controls are not applicable for severely disabled people such as people with Locked-in Syndrome. Therefore, eye tracking control is part of ongoing research. The related literature spans many disciplines, creating a heterogeneous field that makes it difficult to gain an overview.Objectives: This work focuses on ARAs that are controlled by gaze and eye movements. By answering the research questions, this paper provides details on the design of the systems, a comparison of input modalities, methods for measuring the performance of these controls, and an outlook on research areas that gained interest in recent years.Methods: This review was conducted as outlined in the PRISMA 2020 Statement. After identifying a wide range of approaches in use the authors decided to use the PRISMA-ScR extension for a scoping review to present the results. The identification process was carried out by screening three databases. After the screening process, a snowball search was conducted.Results: 39 articles and 6 reviews were included in this article. Characteristics related to the system and study design were extracted and presented divided into three groups based on the use of eye tracking.Conclusion: This paper aims to provide an overview for researchers new to the field by offering insight into eye tracking based robot controllers. We have identified open questions that need to be answered in order to provide people with severe motor function loss with systems that are highly useable and accessible.

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“…The approach we take here is to consider vision as part of a control process where a human, or agent, actively chooses taskrelevant information from the environment to guide actions and achieve goals [4][5][6][7][8]. Our visual task module approach is inspired by human visual behaviour, in particular a foveated visual system that can be highly specific in accessing particular pieces of information over time.…”

Section: Modelling Visual Attentionmentioning

confidence: 99%

Predicting human visuomotor behaviour in a driving task

Johnson

Sullivan

Hayhoe

et al. 2014

Phil. Trans. R. Soc. B

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The sequential deployment of gaze to regions of interest is an integral part of human visual function. Owing to its central importance, decades of research have focused on predicting gaze locations, but there has been relatively little formal attempt to predict the temporal aspects of gaze deployment in natural multi-tasking situations. We approach this problem by decomposing complex visual behaviour into individual task modules that require independent sources of visual information for control, in order to model human gaze deployment on different task-relevant objects. We introduce a softmax barrier model for gaze selection that uses two key elements: a priority parameter that represents task importance per module, and noise estimates that allow modules to represent uncertainty about the state of task-relevant visual information. Comparisons with human gaze data gathered in a virtual driving environment show that the model closely approximates human performance.

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Gaze Allocation Analysis for a Visually Guided Manipulation Task

Cited by 3 publications

References 13 publications

Models of gaze control for manipulation tasks

Models of gaze control for manipulation tasks

A scoping review of gaze and eye tracking-based control methods for assistive robotic arms

Predicting human visuomotor behaviour in a driving task

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