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

## TL;DR

This paper introduces a method for robots to understand human context in 3D environments by learning spatial affordances through virtual skeleton models, enhancing human-robot interaction without needing real human data.

## Contribution

It proposes a novel spatial affordance map approach using virtual human models and SVMs to infer human context from geometric features in 3D scenes.

## Key findings

- Promising results in real 3D scene datasets
- Effective mapping of human context without real human observation
- Supports better human-robot interaction in cluttered environments

## Abstract

Robots are often required to operate in environments where humans are not present, but yet require the human context information for better human-robot interaction. Even when humans are present in the environment, detecting their presence in cluttered environments could be challenging. As a solution to this problem, this paper presents the concept of spatial affordance map which learns human context by looking at geometric features of the environment. Instead of observing real humans to learn human context, it uses virtual human models and their relationships with the environment to map hidden human affordances in 3D scenes by placing virtual skeleton models in 3D scenes with their confidence values. The spatial affordance map learning problem is formulated as a multi-label classification problem that can be learned using Support Vector Machine (SVM) based learners. Experiments carried out in a real 3D scene dataset recorded promising results and proved the applicability of affordance-map for mapping human context.