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Robots are often required to operate in environments where humans are notpresent, but yet require the human context information for better human-robotinteraction. Even when humans are present in the environment, detecting theirpresence in cluttered environments could be challenging. As a solution to thisproblem, this paper presents the concept of spatial affordance map which learnshuman context by looking at geometric features of the environment. Instead ofobserving real humans to learn human context, it uses virtual human models andtheir relationships with the environment to map hidden human affordances in 3Dscenes by placing virtual skeleton models in 3D scenes with their confidencevalues. The spatial affordance map learning problem is formulated as amulti-label classification problem that can be learned using Support VectorMachine (SVM) based learners. Experiments carried out in a real 3D scenedataset recorded promising results and proved the applicability ofaffordance-map for mapping human context.

Understanding Human Context in 3D Scenes by Learning Spatial Affordances with Virtual Skeleton Models