  Embodied Instruction Following (EIF) requires agents to complete human
instruction by interacting objects in complicated surrounding environments.
Conventional methods directly consider the sparse human instruction to generate
action plans for agents, which usually fail to achieve human goals because of
the instruction incoherence in action descriptions. On the contrary, we propose
ThinkBot that reasons the thought chain in human instruction to recover the
missing action descriptions, so that the agent can successfully complete human
goals by following the coherent instruction. Specifically, we first design an
instruction completer based on large language models to recover the missing
actions with interacted objects between consecutive human instruction, where
the perceived surrounding environments and the completed sub-goals are
considered for instruction completion. Based on the partially observed scene
semantic maps, we present an object localizer to infer the position of
interacted objects for agents to achieve complex human goals. Extensive
experiments in the simulated environment show that our ThinkBot outperforms the
state-of-the-art EIF methods by a sizable margin in both success rate and
execution efficiency.
