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To enable progress towards egocentric agents capable of understandingeveryday tasks specified in natural language, we propose a benchmark and asynthetic dataset called Egocentric Task Verification (EgoTV). The goal inEgoTV is to verify the execution of tasks from egocentric videos based on thenatural language description of these tasks. EgoTV contains pairs of videos andtheir task descriptions for multi-step tasks -- these tasks contain multiplesub-task decompositions, state changes, object interactions, and sub-taskordering constraints. In addition, EgoTV also provides abstracted taskdescriptions that contain only partial details about ways to accomplish a task.Consequently, EgoTV requires causal, temporal, and compositional reasoning ofvideo and language modalities, which is missing in existing datasets. We alsofind that existing vision-language models struggle at such all round reasoningneeded for task verification in EgoTV. Inspired by the needs of EgoTV, wepropose a novel Neuro-Symbolic Grounding (NSG) approach that leverages symbolicrepresentations to capture the compositional and temporal structure of tasks.We demonstrate NSG's capability towards task tracking and verification on ourEgoTV dataset and a real-world dataset derived from CrossTask (CTV). Weopen-source the EgoTV and CTV datasets and the NSG model for future research onegocentric assistive agents.

EgoTV: Egocentric Task Verification from Natural Language Task Descriptions