Object-Centric Instruction Augmentation for Robotic Manipulation

Humans interpret scenes by recognizing both the identities and positions of objects in their observations. For a robot to perform tasks such as "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 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.

Authors

• Junjie Wen
• Yichen Zhu 0001
• Minjie Zhu
• Jinming Li
• Zhiyuan Xu
• Zhengping Che
• Chaomin Shen 0001
• Yaxin Peng
• Dong Liu
• Feifei Feng
• Jian Tang 0008

Keywords

• Knowledge engineering
• Visualization
• Large language models
• Human intelligence
• Semantics
• Natural languages
• Manipulators
• Robot Manipulator
• Natural Language
• Language Teaching
• Object Position
• Policy Network
• Visual Representation
• Bounding Box
• Manipulation Tasks
• Real-world Experiments
• Blue Box
• Tokenized
• Policy Learning
• Absolute Position
• Overview Of Framework
• Polar Bears
• Foundation Model
• Visual Encoding
• Left Box
• Unseen Domains