scholarly journals Tactile-based active object discrimination and target object search in an unknown workspace

2018 ◽  
Vol 43 (1) ◽  
pp. 123-152 ◽  
Author(s):  
Mohsen Kaboli ◽  
Kunpeng Yao ◽  
Di Feng ◽  
Gordon Cheng
2018 ◽  
Vol 71 (6) ◽  
pp. 1457-1468
Author(s):  
Péter Pongrácz ◽  
András Péter ◽  
Ádám Miklósi

A central problem of behavioural studies providing artificial visual stimuli for non-human animals is to determine how subjects perceive and process these stimuli. Especially in the case of videos, it is important to ascertain that animals perceive the actual content of the images and are not just reacting to the motion cues in the presentation. In this study, we set out to investigate how dogs process life-sized videos. We aimed to find out whether dogs perceive the actual content of video images or whether they only react to the videos as a set of dynamic visual elements. For this purpose, dogs were presented with an object search task where a life-sized projected human was hiding a target object. The videos were either normally oriented or displayed upside down, and we analysed dogs’ reactions towards the projector screen after the video presentations, and their performance in the search task. Results indicated that in the case of the normally oriented videos, dogs spontaneously perceived the actual content of the images. However, the ‘Inverted’ videos were first processed as a set of unrelated visual elements, and only after some exposure to these videos did the dogs show signs of perceiving the unusual configuration of the depicted scene. Our most important conclusion was that dogs process the same type of artificial visual stimuli in different ways, depending on the familiarity of the depicted scene, and that the processing mode can change with exposure to unfamiliar stimuli.


2015 ◽  
Vol 03 (04) ◽  
pp. 299-310
Author(s):  
Lasitha Piyathilaka ◽  
Sarath Kodagoda

Ability to learn human context in an environment could be one of the most desired fundamental abilities that a robot should have when sharing a workspace with human co-workers. Arguably, a robot with appropriate human context awareness could lead to a better human–robot interaction. In this paper, we address the problem of learning human context in an office environment by only using 3D point cloud data. Our approach is based on the concept of affordance-map, which involves mapping latent human actions in a given environment by looking at geometric features of the environment. This enables us to learn the human context in the environment without observing real human behaviors which themselves are a nontrivial task to detect. Once learned, affordance-map allows us to assign an affordance cost value for each grid location of the map. These cost maps are later used to develop an active object search strategy and to develop a context-aware global path planning strategy.


Author(s):  
Jie Wu ◽  
Tianshui Chen ◽  
Lishan Huang ◽  
Hefeng Wu ◽  
Guanbin Li ◽  
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Keyword(s):  

Author(s):  
Пилип Олександрович Приставка ◽  
Дмитро Ігорович Гісь ◽  
Артем Валерійович Чирков

2018 ◽  
Author(s):  
Noam Roth ◽  
Nicole C. Rust

ABSTRACTSearching for a specific visual object requires our brain to compare the items in view with a remembered representation of the sought target to determine whether a target match is present. This comparison is thought to be implemented, in part, via the combination of top-down modulations reflecting target identity with feed-forward visual representations. However, it remains unclear whether top-down signals are integrated at a single locus within the ventral visual pathway (e.g. V4) or at multiple stages (e.g. both V4 and inferotemporal cortex, IT). To investigate, we recorded neural responses in V4 and IT as rhesus monkeys performed a task that required them to identify when a target object appeared across variation in position, size and background context. We found non-visual, task-specific signals in both V4 and IT. To evaluate whether V4 was the only locus for the integration of top-down signals, we evaluated several feed-forward accounts of processing from V4 to IT, including a model in which IT preferentially sampled from the best V4 units and a model that allowed for nonlinear IT computation. IT task-specific modulation was not accounted for by any of these feed-forward descriptions, suggesting that during object search, top-down signals are integrated directly within IT.NEW & NOTEWORTHYTo find specific objects, the brain must integrate top-down, target-specific signals with visual information about objects in view. However, the exact route of this integration in the ventral visual pathway is unclear. In the first study to systematically compare V4 and IT during an invariant object search task, we demonstrate that top-down signals found in IT cannot be described as being inherited from V4, but rather must be integrated directly within IT itself.


Author(s):  
Zhen Zeng ◽  
Adrian Röfer ◽  
Odest Chadwicke Jenkins

We aim for mobile robots to function in a variety of common human environments, which requires them to efficiently search previously unseen target objects. We can exploit background knowledge about common spatial relations between landmark objects and target objects to narrow down search space. In this paper, we propose an active visual object search strategy method through our introduction of the Semantic Linking Maps (SLiM) model. SLiM simultaneously maintains the belief over a target object's location as well as landmark objects' locations, while accounting for probabilistic inter-object spatial relations. Based on SLiM, we describe a hybrid search strategy that selects the next best view pose for searching for the target object based on the maintained belief. We demonstrate the efficiency of our SLiM-based search strategy through comparative experiments in simulated environments. We further demonstrate the real-world applicability of SLiM-based search in scenarios with a Fetch mobile manipulation robot.


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