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.

Grammar Rules

Below is a list of all the grammar rules used in our experiments (7 objects)

Links:

14 object dataset source: https://www.turbosquid.com/3d-models/3d-model-supermarket-shelves-pack-pasta/1089057

@inproceedings{kumar2022graphbased,
    title={Graph-based Cluttered Scene Generation and Interactive Exploration using Deep Reinforcement Learning},
    archiveprefix  = {arXiv},
    arxiv = {https://arxiv.org/abs/2109.10460},
    author={K. Niranjan Kumar and Irfan Essa and Sehoon Ha},
    booktitle={2022 IEEE International Conference on Robotics and Automation (ICRA)},
    organization={IEEE},
    pdf={https://www.kniranjankumar.com/assets/pdf/graph_based_clutter.pdf},
    primaryClass={cs.RO},
    url={https://www.kniranjankumar.com/projects/5_clutr/},
    video={https://youtu.be/T2Jo7wwaXss},
    year={2022}}