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  1. Proceedings of the 2nd Workshop on Machine Learning for Interactive Systems (MLIS '13)
  2. Homogeneity analysis for object-action relation reasoning in kitchen scenarios
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Learning machines that perceive, act and communicate
Transfer learning with applications on text, sensors and images
Robots, skills, and symbols
Developmental robotics at Aldebaran A-Lab
Open knowledge for human-robot interaction
Machine learning for interactive systems and robots: a brief introduction
Expectation propagation learning of finite Beta-Liouville mixtures for spatio-temporal object recognition
Homogeneity analysis for object-action relation reasoning in kitchen scenarios
Shared control of a robot using EEG-based feedback signals
Automatic interface optimization through random exploration of available elements
Social signal and user adaptation in reinforcement learning-based dialogue management
Inverse reinforcement learning for interactive systems

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Thesis

Homogeneity analysis for object-action relation reasoning in kitchen scenarios

Content Provider ACM Digital Library
Author Piater, Justus Xiong, Hanchen Szedmak, Sandor
Abstract Modeling and learning object-action relations has been an active topic of robotic study since it can enable an agent to discover manipulation knowledge from empirical data, based on which, for instance, the effects of different actions on an unseen object can be inferred in a data-driven way. This paper introduces a novel object-action relational model, in which objects are represented in a multi-layer, action-oriented space, and actions are represented in an object-oriented space. Model learning is based on homogeneity analysis, with extra dependency learning and decomposition of unique object scores into different action layers. The model is evaluated on a dataset of objects and actions in a kitchen scenario, and the experimental results illustrate that the proposed model yields semantically reasonable interpretation of object-action relations. The learned object-action relation model is also tested in various practical tasks (e.g. action effect prediction, object selection), and it displays high accuracy and robustness to noise and missing data.
Starting Page 37
Ending Page 44
Page Count 8
File Format PDF
ISBN 9781450320191
DOI 10.1145/2493525.2493532
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2013-08-04
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Object-action relation learning Homogeneity analysis
Content Type Text
Resource Type Article
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