Prior-assisted propagation of spatial information for object search

Lorbach, Malte; Höfer, Sebastian; Brock, Oliver

doi:10.1109/iros.2014.6942962

Cited by 31 publications

(18 citation statements)

References 9 publications

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“…Several researchers have leveraged the fact that our environments are rich with cues that can assist robots in various tasks that require reasoning about objects and their locations. For example, different works have addressed object classification, predicting the locations of objects using typical 3D structures in indoor environments or object-object relations such as co-occurrences in a scene (Aydemir and Jensfelt, 2012; Joho et al, 2011; Kunze et al, 2014; Lorbach et al, 2014; Nyga et al, 2014). However, our work is concerned with learning pairwise object preferences to compute preferred arrangements when tidying up.…”

Section: Related Workmentioning

confidence: 99%

Organizing objects by predicting user preferences through collaborative filtering

Abdo

Stachniss

Spinello

et al. 2016

The International Journal of Robotics Research

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As service robots become more and more capable of performing useful tasks for us, there is a growing need to teach robots how we expect them to carry out these tasks. However, different users typically have their own preferences, for example with respect to arranging objects on different shelves. As many of these preferences depend on a variety of factors including personal taste, cultural background, or common sense, it is challenging for an expert to pre-program a robot in order to accommodate all potential users. At the same time, it is impractical for robots to constantly query users about how they should perform individual tasks. In this work, we present an approach to learn patterns in user preferences for the task of tidying up objects in containers, e.g. shelves or boxes. Our method builds upon the paradigm of collaborative filtering for making personalized recommendations and relies on data from different users which we gather using crowdsourcing. To deal with novel objects for which we have no data, we propose a method that compliments standard collaborative filtering by leveraging information mined from the Web. When solving a tidy-up task, we first predict pairwise object preferences of the user. Then, we subdivide the objects in containers by modeling a spectral clustering problem. Our solution is easy to update, does not require complex modeling, and improves with the amount of user data. We evaluate our approach using crowdsourcing data from over 1200 users and demonstrate its effectiveness for two tidy-up scenarios. Additionally, we show that a real robot can reliably predict user preferences using our approach.

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Section: Related Workmentioning

confidence: 99%

Organizing objects by predicting user preferences through collaborative filtering

Abdo

Stachniss

Spinello

et al. 2016

The International Journal of Robotics Research

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“…Much subsequent work in robotics has expanded on this formulation; Aydemir et al [1] provides a good overview of the numerous works. Most of the later work attempt to capture additional structural information found in typical household environments, including object-location and object-object co-occurrences [8], [1], spatial relations [9], [11], object affordances [12] and scene ontologies [14]. While from these studies it is clear that additional knowledge is beneficial, most are focused on relatively uncluttered domains.…”

Section: Related Workmentioning

confidence: 99%

Searching for physical objects in partially known environments

Nie

Wong

Kaelbling

2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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We address the problem of a mobile manipulation robot searching for an object in a cluttered domain that is populated with an unknown number of objects in an unknown arrangement. The robot must move around its environment, looking in containers, moving occluding objects to improve its view, and reasoning about collocation of objects of different types, all in service of finding a desired object. The key contribution in reasoning is a Markov-chain Monte Carlo (MCMC) method for drawing samples of the arrangements of objects in an occluded container, conditioned on previous observations of other objects as well as spatial constraints. The key contribution in planning is a receding-horizon forward search in the space of distributions over arrangements (including number and type) of objects in the domain; to maintain tractability the search is formulated in a model that abstracts both the observations and actions available to the robot. The strategy is shown empirically to improve upon a baseline systematic search strategy, and sometimes outperforms a method from previous work.

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“…It has been shown that the refinement of the locations considered for search has a positive influence to search performance [15]. For example, object search based on the basis of Qualitative Spatial Relations is presented in [16], while five priors representing structure of the world and the specific scene are introduced and encoded into a probabilistic model and used to build consistent hypotheses about object locations in [17].…”

Section: Related Workmentioning

confidence: 99%

Where's waldo at time t ? using spatio-temporal models for mobile robot search

Krajník

Kulich

Mudrová

et al. 2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-We present a novel approach to mobile robot search for non-stationary objects in partially known environments. We formulate the search as a path planning problem in an environment where the probability of object occurrences at particular locations is a function of time. We propose to explicitly model the dynamics of the object occurrences by their frequency spectra. Using the environment model proposed, our path planning algorithm can construct plans that reflect the likelihoods of object locations at the time the search is performed.Three datasets collected over several months containing person and object occurrences in residential and office environments were chosen to evaluate the approach. Several types of spatio-temporal models are created for each of these datasets and the efficiency of the search method is assessed by measuring the time it took to locate a particular object. The experiments indicate that modeling the dynamics of objects' occurrence reduces the average search time by 35% to 65% compared to maps that neglect these dynamics.

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Prior-assisted propagation of spatial information for object search

Cited by 31 publications

References 9 publications

Organizing objects by predicting user preferences through collaborative filtering

Organizing objects by predicting user preferences through collaborative filtering

Searching for physical objects in partially known environments

Where's waldo at time t ? using spatio-temporal models for mobile robot search

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