Tanis Mar
Istituto Italiano di Tecnologia
8 Papers
55 Citations
Tanis Mar is an academic researcher from Istituto Italiano di Tecnologia. The author has contributed to research in topics: iCub & Humanoid robot. The author has an hindex of 7, co-authored 8 publications. Previous affiliations of Tanis Mar include University of Genoa.
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Papers
Optimization of ECG Classification by Means of Feature Selection
TL;DR: Results show that by applying the proposed methods, the performance obtained in similar studies under the same constraints can be exceeded, while keeping the requirements suitable for ambulatory monitoring.
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Self-supervised learning of grasp dependent tool affordances on the iCub Humanoid robot
Tanis Mar,Vadim Tikhanoff,Giorgio Metta,Lorenzo Natale +3 more
- 26 May 2015
TL;DR: This paper presents a method that tackles both issues simultaneously by using an extended set of functional features and a novel representation of the effect of the tool use, which implicitly account for the grasping configuration and allow the iCub to generalize among tools based on their geometry.
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Multi-model approach based on 3D functional features for tool affordance learning in robotics
Tanis Mar,Vadim Tikhanoff,Giorgio Metta,Lorenzo Natale +3 more
- 28 Dec 2015
TL;DR: Results show that the combination of OMS-EGI 3D features and multi-model affordance learning approach is able to produce quite accurate predictions of the effect that an action performed with a tool grasped on a particular way will have, even for unseen tools or grasp configurations.
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Self-supervised learning of tool affordances from 3D tool representation through parallel SOM mapping
Tanis Mar,Vadim Tikhanoff,Giorgio Metta,Lorenzo Natale +3 more
- 01 May 2017
TL;DR: This method enables the robot to accurately predict the effect of its actions using tools, and thus to select the best action for a given goal, even with tools not seen on the learning phase.
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•Posted Content
Enabling Depth-driven Visual Attention on the iCub Humanoid Robot: Instructions for Use and New Perspectives
Giulia Pasquale,Giulia Pasquale,Tanis Mar,Tanis Mar,Carlo Ciliberto,Carlo Ciliberto,Lorenzo Rosasco,Lorenzo Rosasco,Lorenzo Rosasco,Lorenzo Natale +9 more
TL;DR: It is shown that this ELAS Matching algorithm allows reliable depth perception and experimental evidence that demonstrates that it can be used to solve challenging visual tasks in real-world, indoor settings.
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