Learning search heuristics for finding objects in structured environments

Joho, Dominik; Senk, Martin; Burgard, Wolfram

doi:10.1016/j.robot.2011.02.012

Cited by 27 publications

(31 citation statements)

References 24 publications

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“…blind is the cost of an optimal tour of the environment, ignoring the prior. Finally, reactive (cheating) is the reactive learning approach of [9]: training an ID3 decision tree [16] to directly predict search actions. The tree was pruned using a combination of maxdepth pruning and reduced error pruning, using the test set to optimally prune the tree, thus giving this method an unfair advantage.…”

Section: Learning Resultsmentioning

confidence: 99%

Learning to locate from demonstrated searches

Vernaza

Stentz

2014

Robotics: Science and Systems X

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Abstract-We consider the problem of learning to locate targets from demonstrated searches. In this concept, a human demonstrates tours of environments that are assumed to minimize the human's expected time to locate the target, given the person's latent prior over potential target locations. The latent prior is then learned as a function of environmental features, enabling a robot to search novel environments in a way that would be deemed efficient by the teacher. We present novel approaches to solve both the inference problem of planning an expected-timeoptimal tour given a prior and the learning problem of deducing the prior from observed tours. Our learning algorithm is inspired by and similar to maximum margin planning (MMP), although it differs in key ways. On the inference side, we advance the stateof-the-art by proposing novel relaxations that are integrated into a heuristic-driven search algorithm. An application to a home assistant scenario is discussed, and experimental results are given validating our methods in this domain.

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Section: Learning Resultsmentioning

confidence: 99%

Learning to locate from demonstrated searches

Vernaza

Stentz

2014

Robotics: Science and Systems X

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“…As shown in IV, increasing search performance in the case where the environment is a close match for these models. Other recent work on object search has tackled larger scale space but used predefined view cones within rooms [14], or has allowed searching over rooms or scenes for unknown objects without constraining their location in 3D [15], [16] such as we are.…”

Section: Related Workmentioning

confidence: 99%

Using Qualitative Spatial Relations for indirect object search

Kunze

Doreswamy

Hawes

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Finding objects in human environments requires autonomous mobile robots to reason about potential object locations and to plan to perceive them accordingly. By using information about the 3D structure of the environment, knowledge about landmark objects and their spatial relationship to the sought object, search can be improved by directing the robot towards the most likely object locations.In this paper we have designed, implemented and evaluated an approach for searching for objects on the basis of Qualitative Spatial Relations (QSRs) such as left-of and in-front-of. On the basis of QSRs between landmarks and the sought object we generate metric poses of potential object locations using an extended version of the ternary point calculus and employ this information for view planning. Preliminary results show that search methods based on QSRs are faster and more reliable than methods not considering them.

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“…As a second consequence, lost objects are not considered by the maintenance strategy and will never be approached, unless they are accidently re-observed. Active object search strategies like the ones described in Joho et al (2011);Elfring et al (2013b) can increase the probability of re-detecting objects which are lost as a result of unexpected movements. However, active object search is time-consuming and the success of the search cannot be guaranteed.…”

Section: Impact Of Unpredicted Object Movements and Unreliable Percepmentioning

confidence: 99%