| 1. |
- Bekiroglu, Yasemin, 1982, et al.
(author)
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Probabilistic Consolidation of Grasp Experience
- 2016
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In: Proceedings - IEEE International Conference on Robotics and Automation. - : IEEE conference proceedings. - 1050-4729. ; , s. 193-200
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Conference paper (peer-reviewed)abstract
- We present a probabilistic model for joint representation of several sensory modalities and action parameters in a robotic grasping scenario. Our non-linear probabilistic latent variable model encodes relationships between grasp-related parameters, learns the importance of features, and expresses confidence in estimates. The model learns associations between stable and unstable grasps that it experiences during an exploration phase. We demonstrate the applicability of the model for estimating grasp stability, correcting grasps, identifying objects based on tactile imprints and predicting tactile imprints from object-relative gripper poses. We performed experiments on a real platform with both known and novel objects, i.e., objects the robot trained with, and previously unseen objects. Grasp correction had a 75% success rate on known objects, and 73% on new objects. We compared our model to a traditional regression model that succeeded in correcting grasps in only 38% of cases.
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| 2. |
- Damianou, Andreas, et al.
(author)
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A Top-Down Approach for a Synthetic Autobiographical Memory System
- 2015
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In: BIOMIMETIC AND BIOHYBRID SYSTEMS, LIVING MACHINES 2015. - Cham : Springer. - 9783319229799 - 9783319229782 ; , s. 280-292
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Conference paper (peer-reviewed)abstract
- Autobiographical memory (AM) refers to the organisation of one's experience into a coherent narrative. The exact neural mechanisms responsible for the manifestation of AM in humans are unknown. On the other hand, the field of psychology has provided us with useful understanding about the functionality of a bio-inspired synthetic AM (SAM) system, in a higher level of description. This paper is concerned with a top-down approach to SAM, where known components and organisation guide the architecture but the unknown details of each module are abstracted. By using Bayesian latent variable models we obtain a transparent SAM system with which we can interact in a structured way. This allows us to reveal the properties of specific sub-modules and map them to functionality observed in biological systems. The top-down approach can cope well with the high performance requirements of a bio-inspired cognitive system. This is demonstrated in experiments using faces data.
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| 3. |
- Zhang, Cheng, 1987-, et al.
(författare)
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Factorized Topic Models
- 2013
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Konferensbidrag (refereegranskat)abstract
- In this paper we present a modification to a latent topic model, which makes themodel exploit supervision to produce a factorized representation of the observeddata. The structured parameterization separately encodes variance that is sharedbetween classes from variance that is private to each class by the introduction of anew prior over the topic space. The approach allows for a more efficient inferenceand provides an intuitive interpretation of the data in terms of an informative signaltogether with structured noise. The factorized representation is shown to enhanceinference performance for image, text, and video classification.
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