Learning adaptive planning representations with natural language
  guidance
Lionel Wong Jiayuan Mao Pratyusha Sharma Zachary S. Siegel Jiahai Feng Noa Korneev Joshua B. Tenenbaum Jacob Andreas
Effective planning in the real world requires not only world knowledge, but
the ability to leverage that knowledge to build the right representation of the
task at hand. Decades of hierarchical planning techniques have used
domain-specific temporal action abstractions to support efficient and accurate
planning, almost always relying on human priors and domain knowledge to
decompose hard tasks into smaller subproblems appropriate for a goal or set of
goals. This paper describes Ada (Action Domain Acquisition), a framework for
automatically constructing task-specific planning representations using
task-general background knowledge from language models (LMs). Starting with a
general-purpose hierarchical planner and a low-level goal-conditioned policy,
Ada interactively learns a library of planner-compatible high-level action
abstractions and low-level controllers adapted to a particular domain of
planning tasks. On two language-guided interactive planning benchmarks (Mini
Minecraft and ALFRED Household Tasks), Ada strongly outperforms other
approaches that use LMs for sequential decision-making, offering more accurate
plans and better generalization to complex tasks.