HERB: a home exploring robotic butler

Srinivasa, Siddhartha S.; Ferguson, Dave; Helfrich, Casey J.; Berenson, Dmitry; Collet, Alvaro; Diankov, Rosen; Gallagher, Garratt; Hollinger, Geoffrey A.; Kuffner, James; Weghe, M. Vande

doi:10.1007/s10514-009-9160-9

Cited by 261 publications

(177 citation statements)

References 26 publications

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“…In contrast, a wide variety of methods have been developed that allow robots to recognize previously observed objects. The majority of these methods use 2D and 3D visual features (see [14], [15], [7], [9], [11]). Other vision-based approaches have also been proposed for finding image regions from multiple views that contain the same object [16].…”

Section: B Roboticsmentioning

confidence: 99%

Grounded object individuation by a humanoid robot

Sinapov

Stoytchev

2013

2013 IEEE International Conference on Robotics and Automation

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Abstract-This paper proposes a theoretical model that enables a robot to partition its unlabeled sensorimotor experience with different objects into discrete clusters, each corresponding to a specific object. To solve this object individuation problem, the robot was trained to detect whether two perceptual stimuli were produced by the same object or by two different objects. The model was tested using a large-scale experiment in which a humanoid robot explored 100 different objects by performing a variety of exploratory behaviors on them and detecting the resulting sensory feedback from several sensory modalities. The results show that with a small amount of prior training, the robot's model was able to successfully individuate the objects with a high degree of accuracy.

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Section: B Roboticsmentioning

confidence: 99%

Grounded object individuation by a humanoid robot

Sinapov

Stoytchev

2013

2013 IEEE International Conference on Robotics and Automation

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“…Actually, it is expected that service robots will soon be playing a role of companion to elderly people, or a role of assistant to humans with special needs at home [3], [17], [6], [2], [9], [5]. In particular, one of the most demanding tasks by users will be the go-and-fetch of objects that are needed for their everyday activities [18], [1].…”

Section: Introductionmentioning

confidence: 99%

The Intelligent Room for Elderly Care

Mozos

Tsuji

Chae

et al. 2013

Natural and Artificial Models in Computation and Biology

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Abstract. Daily life assistance for elderly is one of the most promising and interesting scenarios for advanced technologies in the near future. Improving the quality of life of elderly is also some of the first priorities in modern countries and societies where the percentage of elder people is rapidly increasing due mainly to great improvements in medicine during the last decades. In this paper, we present an overview of our informationally structured room that supports daily life activities of elderly with the aim of improving their quality of life. Our environment contains different distributed sensors including a floor sensing system and several intelligent cabinets. Sensor information is sent to a centralized management system which processes the data and makes it available to a service robot which assists the people in the room. One important restriction in our intelligent environment is to maintain a small number of sensors to avoid interfering with the daily activities of people and to reduce as much as possible the invasion of their privacy. In addition we discuss some experiments using our real environment and robot.

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“…We do not consider the full SLAM problem here, but instead work in a simulation of CrunchBot having zero odometry noise to avoid the localisation problem and focus on mapping only. Related object-based mapping models have recently appeared [51,23,46,40] using laser sensors to recognise and learn complex but nonhierarchical spatial models. However as data available through whiskers to CrunchBot is much sparser than that from laser scanners, the required level of sensor detail is unavailable, therefore we compensate with the new mapping technique of fusing contact reports into hierarchical models.…”

Section: Introductionmentioning

confidence: 99%

Towards hierarchical blackboard mapping on a whiskered robot

Fox

Evans

Pearson

et al. 2012

Robotics and Autonomous Systems

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The paradigm case for robotic mapping assumes large quantities of sensory information which allow the use of relatively weak priors. In contrast, the present study considers the mapping problem for a mobile robot, CrunchBot, where only sparse, local tactile information from whisker sensors is available. To compensate for such weak likelihood information, we make use of low-level signal processing and strong hierarchical object priors. Hierarchical models were popular in classical blackboard systems but are here applied in a Bayesian setting as a mapping algorithm. The hierarchical models require reports of whisker distance to contact and of surface orientation at contact, and we demonstrate that this information can be retrieved by classifiers from strain data collected by CrunchBot's physical whiskers. We then provide a demonstration in simulation of how this information can be used to build maps (but not yet full SLAM) in an zero-odometry-noise environment containing walls and table-like hierarchical objects.

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HERB: a home exploring robotic butler

Cited by 261 publications

References 26 publications

Grounded object individuation by a humanoid robot

Grounded object individuation by a humanoid robot

The Intelligent Room for Elderly Care

Towards hierarchical blackboard mapping on a whiskered robot

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