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Spatial affordance can be defined as the functionality a space, or place, lends to human activity. Different places afford different activity possibilities - sleeping is mostly done in the bedroom, and cooking is mostly done in the kitchen. Semantic place labels like kitchen and bedroom, therefore, provide context with which a robot can better infer human activity. Real rooms, however, often defy...
We describe a technique to build an affordance map interactively for robotic tasks. Affordances are predicted by a trained classifier using geometric features extracted from objects. Based on 2D occupancy grid, a Markov Random Field (MRF) model builds an affordance map with relational affordance with neighboring cells. The quality of the affordance map is refined by sequences of interactive manipulations...
When a robot is deployed it needs to understand the nature of its surroundings. In this paper, we address the problem of semantic labeling 3D point clouds by object affordance (e.g., ‘pushable’, ‘liftable’). We propose a technique to extract geometric features from point cloud segments and build a classifier to predict associated object affordances. With the classifier, we have developed an algorithm...
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