Robust Robotic Task Planning Via Immutable Subgoals

Abstract

Service robots require instruction-following capabilities to perform various tasks regardless of environmental changes. A task planner must accurately infer user intent even when human instructions are ambiguous. To this end, we propose TIGER, a task planning framework that generates reliable action sequences by deriving immutable subgoals from instructions. TIGER employs an Immutable Subgoal Planner (ISP) to decompose instructions into environment-independent subgoals and a Target Grounder (TG) to ground abstract keywords to real-world objects via visual perception and reasoning. A task-representative one-shot strategy improves subgoal generation using only seven annotated examples. TIGER outperformed LLM-Planner in the ALFRED benchmark, increasing success rates from 15.09% to 35.06% on the seen set and from 19.73% to 42.57% on the unseen set. Its scalability was also verified in real-world experiments with a UR5e robot.