Publications

@inproceedings{2021-Kumar-GCSGIEUDRL,

title = {Graph-based Cluttered Scene Generation and Interactive Exploration using Deep Reinforcement Learning},

author = {Niranjan Kumar and Irfan Essa and Sehoon Ha},

url = {https://doi.org/10.1109/ICRA46639.2022.9811874

https://arxiv.org/abs/2109.10460

https://arxiv.org/pdf/2109.10460

https://www.kniranjankumar.com/projects/5_clutr

https://kniranjankumar.github.io/assets/pdf/graph_based_clutter.pdf

https://youtu.be/T2Jo7wwaXss},

doi = {10.1109/ICRA46639.2022.9811874},

year  = {2022},

date = {2022-05-01},

urldate = {2022-05-01},

booktitle = {Proceedings International Conference on Robotics and Automation (ICRA)},

journal = {arXiv},

number = {2109.10460},

pages = {7521-7527},

abstract = {We introduce a novel method to teach a robotic agent to interactively explore cluttered yet structured scenes, such as kitchen pantries and grocery shelves, by leveraging the physical plausibility of the scene. We propose a novel learning framework to train an effective scene exploration policy to discover hidden objects with minimal interactions. First, we define a novel scene grammar to represent structured clutter. Then we train a Graph Neural Network (GNN) based Scene Generation agent using deep reinforcement learning (deep RL), to manipulate this Scene Grammar to create a diverse set of stable scenes, each containing multiple hidden objects. Given such cluttered scenes, we then train a Scene Exploration agent, using deep RL, to uncover hidden objects by interactively rearranging the scene.



},

keywords = {ICRA, machine learning, reinforcement learning, robotics},

pubstate = {published},

tppubtype = {inproceedings}

}