Title
ACT-Thor: A Controlled Benchmark for Embodied Action Understanding in Simulated Environments.
Abstract
Artificial agents are nowadays challenged to perform embodied AI tasks. To succeed, agents must understand the meaning of verbs and how their corresponding actions transform the surrounding world. In this work, we propose ACT-Thor, a novel controlled benchmark for embodied action understanding. We use the AI2-THOR simulated environment to produce a controlled setup in which an agent, given a before-image and an associated action command, has to determine what the correct after-image is among a set of possible candidates. First, we assess the feasibility of the task via a human evaluation that resulted in 81.4% accuracy, and very high inter-annotator agreement (84.9%). Second, we design both unimodal and multimodal baselines, using state-of-the-art visual feature extractors. Our evaluation and error analysis suggest that only models that have a very structured representation of the actions together with powerful visual features can perform well on the task. However, they still fall behind human performance in a zero-shot scenario where the model is exposed to unseen (action, object) pairs. This paves the way for a systematic way of evaluating embodied AI agents that understand grounded actions.
Year
Venue
DocType
2022
International Conference on Computational Linguistics
Conference
Volume
Citations 
PageRank 
Proceedings of the 29th International Conference on Computational Linguistics
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Michael Hanna100.34
Federico Pedeni200.34
Alessandro Suglia3154.28
Alberto Testoni402.03
Raffaella Bernardi538038.05