ACT-Thor: A Controlled Benchmark for Embodied Action Understanding in Simulated Environments (2022.coling-1)
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| Challenge: | embodied AI tasks require a strong understanding of verbs and their corresponding actions. |
| Approach: | They propose a controlled benchmark for embodied action understanding using a simulated environment and a visual feature extractor. |
| Outcome: | The proposed benchmark achieves 81.4% accuracy and high inter-annotator agreement . the proposed model falls behind human models in a zero-shot scenario . |
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