Learning grasping affordances from local visual descriptors
DEVLRN '09 Proceedings of the 2009 IEEE 8th International Conference on Development and Learning
Learning Object Affordances: From Sensory--Motor Coordination to Imitation
IEEE Transactions on Robotics
Machine learning for interactive systems and robots: a brief introduction
Proceedings of the 2nd Workshop on Machine Learning for Interactive Systems: Bridging the Gap Between Perception, Action and Communication
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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.