scholarly journals Leveraging Human Perception in Robot Grasping and Manipulation Through Crowdsourcing and Gamification

2021 ◽  
Vol 8 ◽  
Author(s):  
Gal Gorjup ◽  
Lucas Gerez ◽  
Minas Liarokapis
Keyword(s):  
Visual Information ◽  
Collective Intelligence ◽  
Human Perception ◽  
Object Identification ◽  
Learning Approaches ◽  
Novel Object ◽  
Robot Perception ◽  
Grasping And Manipulation ◽  
Object Classes ◽  
Robot Grasping

Robot grasping in unstructured and dynamic environments is heavily dependent on the object attributes. Although Deep Learning approaches have delivered exceptional performance in robot perception, human perception and reasoning are still superior in processing novel object classes. Furthermore, training such models requires large, difficult to obtain datasets. This work combines crowdsourcing and gamification to leverage human intelligence, enhancing the object recognition and attribute estimation processes of robot grasping. The framework employs an attribute matching system that encodes visual information into an online puzzle game, utilizing the collective intelligence of players to expand the attribute database and react to real-time perception conflicts. The framework is deployed and evaluated in two proof-of-concept applications: enhancing the control of a robotic exoskeleton glove and improving object identification for autonomous robot grasping. In addition, a model for estimating the framework response time is proposed. The obtained results demonstrate that the framework is capable of rapid adaptation to novel object classes, based purely on visual information and human experience.

Exploring The Effect of Visual Information Degradation on Human Perception and Performance In A Human-Telerobot System

2020 ◽  
Vol 64 (1) ◽  
pp. 1302-1307
Author(s):  
Richard Stone ◽  
Minglu Wang ◽  
Thomas Schnieders ◽  
Esraa Abdelall
Keyword(s):  
Visual Information ◽  
Human Perception ◽  
Low Level ◽  
Hit Rate ◽  
Word Level ◽  
Level Of Automation ◽  
Significant Difference ◽  
Human Operators ◽  
And Performance ◽  
High Level

Human-robotic interaction system are increasingly becoming integrated into industrial, commercial and emergency service agencies. It is critical that human operators understand and trust automation when these systems support and even make important decisions. The following study focused on human-in-loop telerobotic system performing a reconnaissance operation. Twenty-four subjects were divided into groups based on level of automation (Low-Level Automation (LLA), and High-Level Automation (HLA)). Results indicated a significant difference between low and high word level of control in hit rate when permanent error occurred. In the LLA group, the type of error had a significant effect on the hit rate. In general, the high level of automation was better than the low level of automation, especially if it was more reliable, suggesting that subjects in the HLA group could rely on the automatic implementation to perform the task more effectively and more accurately.

Connecting the dots without top-down knowledge: Evidence for rapidly-learned low-level associations that are independent of object identity

2018 ◽  
Author(s):  
Patrick Sadil ◽  
Kevin Potter ◽  
David E. Huber ◽  
Rosemary Cowell
Keyword(s):  
Visual Information ◽  
Naming Task ◽  
Object Identification ◽  
Matching Task ◽  
Lateral Part ◽  
Object Identity ◽  
Top Down ◽  
Object Processing ◽  
Episodic Memories ◽  
High Level

Knowing the identity of an object can powerfully alter perception. Visual demonstrations of this – such as Gregory’s (1980) hidden Dalmatian – affirm the existence of both top-down and bottom-up processing. We consider a third processing pathway: lateral connections between the parts of an object. Lateral associations are assumed by theories of object processing and hierarchical theories of memory, but little evidence attests to them. If they exist, their effects should be observable even in the absence of object identity knowledge. We employed Continuous Flash Suppression (CFS) while participants studied object images, such that visual details were learned without explicit object identification. At test, lateral associations were probed using a part-to-part matching task. We also tested whether part-whole links were facilitated by prior study using a part-naming task, and included another study condition (“Word”), in which participants saw only an object’s written name. The key question was whether CFS study (which provided visual information without identity) would better support part-to-part matching (via lateral associations) whereas Word study (which provided identity without the correct visual form) would better support part-naming (via top-down processing). The predicted dissociation was found, and confirmed by state-trace analyses. Thus, lateral part-to-part associations were learned and retrieved independently of object identity representations. This establishes novel links between perception and memory, demonstrating that (1) lateral associations at lower levels of the object identification hierarchy exist and contribute to object processing, and (2) these associations are learned via rapid, episodic-like mechanisms previously observed for the high-level, arbitrary relations comprising episodic memories.

Model of indicator of linearization of user interfaces of automated workplaces of operational units of emergency services

2021 ◽  
Vol 93 ◽  
pp. 29-41
Author(s):  
S. V. Sokolov ◽  
◽  
A. N. Morozov ◽  
Keyword(s):  
User Interface ◽  
User Interfaces ◽  
Visual Information ◽  
Emergency Services ◽  
Human Perception ◽  
Relational Algebra ◽  
Interface Elements ◽  
Emergency Rescue ◽  
Pin Configuration

Introduction. The article presents a model of one of the possible indicators of the quality of user interfaces (PIN) of automated workplaces of operational units of emergency and emergency rescue services (EiASS), namely, the linearization indicator that takes into account the psychophysiological features of human perception of visual information. Goals and objectives. Reducing the response time of the operational units of the EiASS by reducing the time of their dispatching. Development of a mathematical model and an algorithm for calculating the linearization indicator of PIN elements (EPIN), which allows estimating the dispatch time depending on their relative location on the monitor. Methods. Methods of set theory and relational algebra were used to construct a PIN model and an algorithm for calculating the linearization index. To describe the PIN configuration, the concepts of an archipelago and a frame of interface elements are introduced. Results and discussion. The success of the EiASS actions largely depends on the dispatch time, during which the required number of units is determined and sent. Therefore, the time spent on solving dispatching tasks is one of the most common and objective indicators of the quality of the PIN. The best of the investigated automated workplaces will be the one with the specified time less. However, the time indicator gives an idea only about the relative time - the time of operation of one automated workplace relative to another. And it does not give any idea of the absolute time that would be spent on solving the problem by some abstract automated workplace with an optimal EPIN configuration. For this reason, an indicator that is sensitive to the PIN configuration has been developed. The indicator gives an answer to the question of why one automated workplace is better than another, and can be used to optimize the layout of the PIN. Conclusions. Based on the proposed model, an algorithm for calculating the numerical values of the linearization indicator of user interface elements sensitive to their size and relative position on the monitor of the EiASS operator is developed. This allows you to optimize the user interface according to the criterion of time for solving tasks and, accordingly, reduce the dispatching time of the operational units of the EiASS. Keywords: operational units, user interface, quality, linearization indicator, navigation, sets, archipelago, frame

Dynamic Norm-based Encoding for Unfamiliar Shapes in Human Visual Cortex

2011 ◽  
Vol 23 (7) ◽  
pp. 1829-1843 ◽  
Author(s):  
Sven Panis ◽  
Johan Wagemans ◽  
Hans P. Op de Beeck
Keyword(s):  
Visual Experience ◽  
Anterior Part ◽  
Neural Population ◽  
Visual Object ◽  
Comparison Task ◽  
Novel Object ◽  
Fmri Study ◽  
Object Classes ◽  
High Level ◽  
First Time

Previous studies have argued that faces and other objects are encoded in terms of their deviation from a class prototype or norm. This prototype is associated with a smaller neural population response compared with nonprototype objects. However, it is still unclear (1) whether a norm-based representation can emerge for unfamiliar or novel object classes through visual experience at the time scale of an experiment and (2) whether the results from previous studies are caused by the prototypicality of a stimulus, by the physical properties of individual stimuli independent from the stimulus distribution, and/or by the trial-to-trial adaptation. Here we show with a combined behavioral and event-related fMRI study in humans that a short amount of visual experience with exemplars from novel object classes determines which stimulus is represented as the norm. Prototypicality effects were observed at the behavioral level by behavioral asymmetries during a stimulus comparison task. The fMRI data revealed that class exemplars closest to the prototypes—the perceived average of each class—were associated with a smaller response in the anterior part of the visual object-selective cortex compared with other class exemplars. By dissociating between the physical characteristics and the prototypicality status of the stimuli and by controlling for trial-to-trial adaptation, we can firmly conclude for the first time that high-level visual areas represent the identity of exemplars using a dynamic, norm-based encoding principle.

scholarly journals Saturation-based quality assessment for colorful multi-exposure image fusion

2017 ◽  
Vol 14 (2) ◽  
pp. 172988141769462 ◽  
Author(s):  
Chenwei Deng ◽  
Zhen Li ◽  
Shuigen Wang ◽  
Xun Liu ◽  
Jiahui Dai
Keyword(s):  
Image Quality ◽  
Image Fusion ◽  
Quality Assessment ◽  
Visual Information ◽  
Image Quality Assessment ◽  
Human Perception ◽  
Image Features ◽  
Consumer Electronics ◽  
Structure Information ◽  
Learning Machine

Multi-exposure image fusion is becoming increasingly influential in enhancing the quality of experience of consumer electronics. However, until now few works have been conducted on the performance evaluation of multi-exposure image fusion, especially colorful multi-exposure image fusion. Conventional quality assessment methods for multi-exposure image fusion mainly focus on grayscale information, while ignoring the color components, which also convey vital visual information. We propose an objective method for the quality assessment of colored multi-exposure image fusion based on image saturation, together with texture and structure similarities, which are able to measure the perceived color, texture, and structure information of fused images. The final image quality is predicted using an extreme learning machine with texture, structure, and saturation similarities as image features. Experimental results for a public multi-exposure image fusion database show that the proposed model can accurately predict colored multi-exposure image fusion image quality and correlates well with human perception. Compared with state-of-the-art image quality assessment models for image fusion, the proposed metric has better evaluation performance.

scholarly journals One-shot categorization of novel object classes in humans

2019 ◽  
Vol 165 ◽  
pp. 98-108 ◽  
Author(s):  
Yaniv Morgenstern ◽  
Filipp Schmidt ◽  
Roland W. Fleming
Keyword(s):  
Novel Object ◽  
Object Classes
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