The Explorer System

Sjöö, Kristoffer; Zender, Hendrik; Kruijff, Geert-Jan M.; Pronobis, Andrzej; Hawes, Nick; Brenner, Michael

doi:10.1007/978-3-642-11694-0_10

Cited by 7 publications

(9 citation statements)

References 12 publications

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“…In order to show the applicability to a real robot system, we integrated our methods into the KNOWROB knowledge processing system 3 . In Figure 9 we show the system overview.…”

Section: B Results and Discussionmentioning

confidence: 99%

“…In a simulated shopping sequence, TUM-James first placed items in a shopping 3 We provide an open source implementation of our algorithm to solve the object allocation task as an extension to the KNOWROB system [1] at http://code.in.tum.de/pubsvn/knowrob/tags/latest/comp orgprinciples/ basket and then emptied the basket on a tabletop. It subsequently identified the objects based on visual models it had previously acquired from product images, determined the ontology concepts to which the items belong and subsequently inferred storage locations, given knowledge about the locations of other objects and their attributes, using the techniques we described above.…”

Section: B Results and Discussionmentioning

confidence: 99%

“…There is a large body of related work that considers organizational principles at a fairly coarse level, seeking to exploit knowledge about object-room associations for the purpose of room classification in the context of mapping and navigation [2] or, inversely, visual search for objects given the classes of rooms within a map [3], or both [4,5]. Object-room associations are typically described using logical knowledge, e.g.…”

Section: Related Workmentioning

confidence: 99%

“…Object-room associations are typically described using logical knowledge, e.g. represented in a description logics knowledge base [3,6], and/or using conditional probability distributions [4,5,7]. The problem we consider is qualitatively different, because we do not assume that there is a given, globally applicable model which captures the structure of an arbitrary environment, regardless of, for example, the personalities and preferences of the environment's inhabitants.…”

Section: Related Workmentioning

confidence: 99%

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Learning organizational principles in human environments

Schuster

Jain

Tenorth

et al. 2012

2012 IEEE International Conference on Robotics and Automation

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Abstract-In the context of robotic assistants in human everyday environments, pick and place tasks are beginning to be competently solved at the technical level. The question of where to place objects or where to pick them up from, among other higher-level reasoning tasks, is therefore gaining practical relevance. In this work, we consider the problem of identifying the organizational structure within an environment, i.e. the problem of determining organizational principles that would allow a robot to infer where to best place a particular, previously unseen object or where to reasonably search for a particular type of object given past observations about the allocation of objects to locations in the environment. This problem can be reasonably formulated as a classification task. We claim that organizational principles are governed by the notion of similarity and provide an empirical analysis of the importance of various features in datasets describing the organizational structure of kitchens. For the aforementioned classification tasks, we compare standard classification methods, reaching average accuracies of at least 79% in all scenarios. We thereby show that, in particular, ontology-based similarity measures are well-suited as highly discriminative features. We demonstrate the use of learned models of organizational principles in a kitchen environment on a real robot system, where the robot identifies a newly acquired item, determines a suitable location and then stores the item accordingly.

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“…In order to show the applicability to a real robot system, we integrated our methods into the KNOWROB knowledge processing system 3 . In Figure 9 we show the system overview.…”

Section: B Results and Discussionmentioning

confidence: 99%

Section: B Results and Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Learning organizational principles in human environments

Schuster

Jain

Tenorth

et al. 2012

2012 IEEE International Conference on Robotics and Automation

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“…Service robots able to distinguish the previous indoor places, and able to assign them the corresponding label, i.e. office or laboratory, will greatly improve their communication capabilities with humans [1].…”

Section: Introductionmentioning

confidence: 99%

Categorization of indoor places by combining local binary pattern histograms of range and reflectance data from laser range finders

et al. 2013

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This paper presents an approach to categorize typical places in indoor environments using 3D scans provided by a laser range finder. Examples of such places are offices, laboratories, or kitchens.In our method, we combine the range and reflectance data from the laser scan for the final categorization of places. Range and reflectance images are transformed into histograms of local binary patterns and combined into a single feature vector. This vector is later classified using support vector machines. The results of the presented experiments demonstrate the capability of our technique to categorize indoor places with high accuracy. We also show that the combination of range and reflectance information improves the final categorization results in comparison with a single modality.

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Architecture for Agent Decision Cycle

2023

Parallel Population and Parallel Human

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The Explorer System

Cited by 7 publications

References 12 publications

Learning organizational principles in human environments

Learning organizational principles in human environments

Categorization of indoor places by combining local binary pattern histograms of range and reflectance data from laser range finders

Architecture for Agent Decision Cycle

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