  Symbolic task planning is a widely used approach to enforce robot autonomy
due to its ease of understanding and deployment in robot architectures.
However, techniques for symbolic task planning are difficult to scale in
real-world, human-robot collaboration scenarios because of the poor performance
in complex planning domains or when frequent re-planning is needed. We present
a framework, Teriyaki, specifically aimed at bridging the gap between symbolic
task planning and machine learning approaches. The rationale is training Large
Language Models (LLMs), namely GPT-3, into a neurosymbolic task planner
compatible with the Planning Domain Definition Language (PDDL), and then
leveraging its generative capabilities to overcome a number of limitations
inherent to symbolic task planners. Potential benefits include (i) a better
scalability in so far as the planning domain complexity increases, since LLMs'
response time linearly scales with the combined length of the input and the
output, and (ii) the ability to synthesize a plan action-by-action instead of
end-to-end, making each action available for execution as soon as it is
generated instead of waiting for the whole plan to be available, which in turn
enables concurrent planning and execution. Recently, significant efforts have
been devoted by the research community to evaluate the cognitive capabilities
of LLMs, with alternate successes. Instead, with Teriyaki we aim to provide an
overall planning performance comparable to traditional planners in specific
planning domains, while leveraging LLMs capabilities to build a look-ahead
predictive planning model. Preliminary results in selected domains show that
our method can: (i) solve 95.5% of problems in a test data set of 1,000
samples; (ii) produce plans up to 13.5% shorter than a traditional symbolic
planner; (iii) reduce average overall waiting times for a plan availability by
up to 61.4%
