  For decades, human-computer interaction has fundamentally been manual. Even
today, almost all productive work done on the computer necessitates human input
at every step. Autonomous virtual agents represent an exciting step in
automating many of these menial tasks. Virtual agents would empower users with
limited technical proficiency to harness the full possibilities of computer
systems. They could also enable the efficient streamlining of numerous computer
tasks, ranging from calendar management to complex travel bookings, with
minimal human intervention. In this paper, we introduce OmniACT, the
first-of-a-kind dataset and benchmark for assessing an agent's capability to
generate executable programs to accomplish computer tasks. Our scope extends
beyond traditional web automation, covering a diverse range of desktop
applications. The dataset consists of fundamental tasks such as "Play the next
song", as well as longer horizon tasks such as "Send an email to John Doe
mentioning the time and place to meet". Specifically, given a pair of screen
image and a visually-grounded natural language task, the goal is to generate a
script capable of fully executing the task. We run several strong baseline
language model agents on our benchmark. The strongest baseline, GPT-4, performs
the best on our benchmark However, its performance level still reaches only 15%
of the human proficiency in generating executable scripts capable of completing
the task, demonstrating the challenge of our task for conventional web agents.
Our benchmark provides a platform to measure and evaluate the progress of
language model agents in automating computer tasks and motivates future work
towards building multimodal models that bridge large language models and the
visual grounding of computer screens.
