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Pretraining over Interactions for Learning Grounded Object Representations

Large language models have been criticized for their limited ability to reason about \textit{affordances} - the actions that can be performed on an object. It has been argued that to accomplish this, models need some form of grounding, i.e., connection, to objects and how they interact in the physical world. Inspired by the way humans learn about the world through interaction, we develop an approach to learning physical properties directly. We introduce a dataset of 200k object interactions in a 3D virtual environment and a self-supervised pretraining objective for learning representations of these objects. We show with probing and clustering experiments that even in the zero-shot setting, derived models learn robust representations of objects and their affordances in an unsupervised manner. Our model outperforms pretrained language and vision models on an affordance prediction baseline, suggesting that pretraining on observed interactions encodes grounded information that is not readily learned in conventional text or vision models.

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