Any House Any Task: Scalable Long-Horizon Planning for Abstract Human Tasks
The work proposes Any House Any Task (AHAT), a planner for long-horizon robot tasks in large environments with ambiguous human instructions. AHAT uses an LLM to map instructions to symbolic subgoals in PDDL and solves them using explicit reasoning. It introduces Trace-Guided Policy Optimization (TGPO), an RL algorithm that improves subgoal generation by correcting errors in intermediate reasoning traces, leading to significant performance gains over existing methods.