Computer Science > Robotics
arXiv:2401.02814 (cs)
[Submitted on 5 Jan 2024 (v1), last revised 1 Feb 2024 (this version, v2)]
Title:Object-Centric Instruction Augmentation for Robotic Manipulation
Authors:Junjie Wen,Yichen Zhu,Minjie Zhu,Jinming Li,Zhiyuan Xu,Zhengping Che,Chaomin Shen,Yaxin Peng,Dong Liu,Feifei Feng,Jian Tang
View a PDF of the paper titled Object-Centric Instruction Augmentation for Robotic Manipulation, by Junjie Wen and 10 other authors
View PDFHTML (experimental)Abstract: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.
| Comments: | accepted to ICRA2024 |
| Subjects: | Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2401.02814 [cs.RO] |
| (orarXiv:2401.02814v2 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2401.02814 arXiv-issued DOI via DataCite |
Submission history
From: Yichen Zhu [view email][v1] Fri, 5 Jan 2024 13:54:45 UTC (24,899 KB)
[v2] Thu, 1 Feb 2024 08:34:46 UTC (24,899 KB)
Full-text links:
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
- Other Formats
View a PDF of the paper titled Object-Centric Instruction Augmentation for Robotic Manipulation, by Junjie Wen and 10 other authors
References & Citations
Bookmark
Bibliographic and Citation Tools
Bibliographic Explorer(What is the Explorer?)
Connected Papers(What is Connected Papers?)
Litmaps(What is Litmaps?)
scite Smart Citations(What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv(What is alphaXiv?)
CatalyzeX Code Finder for Papers(What is CatalyzeX?)
DagsHub(What is DagsHub?)
Gotit.pub(What is GotitPub?)
Hugging Face(What is Huggingface?)
Papers with Code(What is Papers with Code?)
ScienceCast(What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower(What are Influence Flowers?)
CORE Recommender(What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.