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

arXiv: https://arxiv.org/abs/1909.07572 · HTML: https://mlsanity.com/paper/1909.07572/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2019.

## Built on by (most cited first, 6 of 6)

- [A General Framework for the Representation of Function and Affordance: A Cognitive, Causal, and Grounded Approach, and a Step Toward AGI](https://mlsanity.com/paper/2206.05273/) (2022)
- [Object-agnostic Affordance Categorization via Unsupervised Learning of Graph Embeddings](https://mlsanity.com/paper/2304.05989/) (2023)
- [CRAFT: Designing Creative and Functional 3D Objects](https://mlsanity.com/paper/2412.03889/) (2024)
- [Put the Bear on the Chair! Intelligent Robot Interaction with Previously Unseen Chairs via Robot Imagination](https://mlsanity.com/paper/2108.05539/) (2021)
- [AROS: Affordance Recognition with One-Shot Human Stances](https://mlsanity.com/paper/2210.11725/) (2022)
- [Why is That a Good or Not a Good Frying Pan? -- Knowledge Representation for Functions of Objects and Tools for Design Understanding, Improvement, and Generation](https://mlsanity.com/paper/2303.06152/) (2023)

## Abstract (arXiv)

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.