Is That a Chair? Imagining Affordances Using Simulations of an Articulated Human Body

TL;DR

This paper introduces a simulation-based approach for robots to imagine object affordances, specifically chairs, by simulating physical interactions with an articulated human body, enabling better classification and pose prediction.

Contribution

It presents a novel simulation method for affordance reasoning that outperforms deep learning in limited data scenarios and predicts functional poses aligned with human judgments.

Findings

Outperforms deep learning methods with only 30 training models.

Accurately predicts upright and functional poses of chairs.

Aligns well with human judgments on synthetic and real objects.

Abstract

For robots to exhibit a high level of intelligence in the real world, they must be able to assess objects for which they have no prior knowledge. Therefore, it is crucial for robots to perceive object affordances by reasoning about physical interactions with the object. In this paper, we propose a novel method to provide robots with an ability to imagine object affordances using physical simulations. The class of chair is chosen here as an initial category of objects to illustrate a more general paradigm. In our method, the robot "imagines" the affordance of an arbitrarily oriented object as a chair by simulating a physical sitting interaction between an articulated human body and the object. This object affordance reasoning is used as a cue for object classification (chair vs non-chair). Moreover, if an object is classified as a chair, the affordance reasoning can also predict the upright pose of the object which allows the sitting interaction to take place. We call this type of poses the functional pose. We demonstrate our method in chair classification on synthetic 3D CAD models. Although our method uses only 30 models for training, it outperforms appearance-based deep learning methods, which require a large amount of training data, when the upright orientation is not assumed to be known a priori. In addition, we showcase that the functional pose predictions of our method align well with human judgments on both synthetic models and real objects scanned by a depth camera.