  Humans interpret scenes by recognizing both the identities and positions of
objects in their observations. For a robot to perform tasks such as
\enquote{pick and place}, understanding both what the objects are and where
they are located is crucial. While the former has been extensively discussed in
the literature that uses the large language model to enrich the text
descriptions, the latter remains underexplored. In this work, we introduce the
\textit{Object-Centric Instruction Augmentation (OCI)} framework to augment
highly semantic and information-dense language instruction with position cues.
We utilize a Multi-modal Large Language Model (MLLM) to weave knowledge of
object locations into natural language instruction, thus aiding the policy
network in mastering actions for versatile manipulation. Additionally, we
present a feature reuse mechanism to integrate the vision-language features
from off-the-shelf pre-trained MLLM into policy networks. Through a series of
simulated and real-world robotic tasks, we demonstrate that robotic manipulator
imitation policies trained with our enhanced instructions outperform those
relying solely on traditional language instructions.
