Reliable execution of robot manipulation actions in cluttered environments requires that the robot is able to understand relations between objects and reason about consequences of actions applied to these objects. We present an approach for extracting physically plausible support relations between objects based on visual information which does not require any prior knowledge about physical object properties, e. g. mass distribution or friction coefficients. Based on a scene representation enriched by such physically plausible support relations between objects, we derive predictions about action effects. These predictions take into account uncertainty about support relations and allow applying strategies for safe bimanual object manipulation when needed. The extraction of physically plausible support relations is evaluated both in simulation and in real world experiments using real data from a depth camera, whereas the handling of support relation uncertainties is validated on the humanoid robot ARMAR-III.

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Humans use semantic concepts such as spatial relations between objects to describe scenes and communicate tasks such as “Put the tea to the right of the cup” or “Move the plate between the fork and the spoon.” Just as children, assistive robots must be able to learn the sub-symbolic meaning of such concepts from human demonstrations and instructions. We address the problem of incrementally learning geometric models of spatial relations from few demonstrations collected online during interaction with a human. Such models enable a robot to manipulate objects in order to fulfill desired spatial relations specified by verbal instructions. At the start, we assume the robot has no geometric model of spatial relations. Given a task as above, the robot requests the user to demonstrate the task once in order to create a model from a single demonstration, leveraging cylindrical probability distribution as generative representation of spatial relations. We show how this model can be updated incrementally with each new demonstration without access to past examples in a sample-efficient way using incremental maximum likelihood estimation, and demonstrate the approach on a real humanoid robot.

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