  Given the current visual observations, the traditional procedure planning
task in instructional videos requires a model to generate goal-directed plans
within a given action space. All previous methods for this task conduct
training and inference under the same action space, and they can only plan for
pre-defined events in the training set. We argue this setting is not applicable
for human assistance in real lives and aim to propose a more general and
practical planning paradigm. Specifically, in this paper, we introduce a new
task named Open-event Procedure Planning (OEPP), which extends the traditional
procedure planning to the open-event setting. OEPP aims to verify whether a
planner can transfer the learned knowledge to similar events that have not been
seen during training. We rebuild a new benchmark of OpenEvent for this task
based on existing datasets and divide the events involved into base and novel
parts. During the data collection process, we carefully ensure the transfer
ability of procedural knowledge for base and novel events by evaluating the
similarity between the descriptions of different event steps with multiple
stages. Based on the collected data, we further propose a simple and general
framework specifically designed for OEPP, and conduct extensive study with
various baseline methods, providing a detailed and insightful analysis on the
results for this task.
