In this paper we perform a novel in-depth evaluation of text-only and multimodal LLMs’ abilities to reason about object *habitats* or conditions on how objects are situated in their environments that affect the types of behaviors (or *affordances*) that can be enacted upon them. We present a novel curated multimodal dataset of questions about object habitats and affordances, which are formally grounded in the underlying lexical semantics literature, with multiple images from various sources that depict the scenario described in the question. We evaluate 16 text-only and multimodal LLMs on this challenging data. Our findings indicate that while certain LLMs can perform reasonably well on reasoning about affordances, there appears to be a consistent low upper bound on habitat-centered reasoning performance. We discuss how the formal semantics of habitats in fact predicts this behavior and propose this as a challenge to the community.

We present a novel method for using agent experiences gathered through an embodied simulation to ground contextualized word vectors to object representations. We use similarity learning to make comparisons between different object types based on their properties when interacted with, and to extract common features pertaining to the objects’ behavior. We then use an affine transformation to calculate a projection matrix that transforms contextualized word vectors from different transformer-based language models into this learned space, and evaluate whether new test instances of transformed token vectors identify the correct concept in the object embedding space. Our results expose properties of the embedding spaces of four different transformer models and show that grounding object token vectors is usually more helpful to grounding verb and attribute token vectors than the reverse, which reflects earlier conclusions in the analogical reasoning and psycholinguistic literature.