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1. Multi-Object Search using Object-Oriented POMDPs

   https://doi.org/10.1109/ICRA.2019.8793888  

   Wandzel, Arthur; Oh, Yoonseon; Fishman, Michael; Kumar, Nishanth; Wong, Lawson L.S.; Tellex, Stefanie (May 2019, IEEE International Conference on Robotics and Automation (ICRA))
2. Multi-Object Search using Object-Oriented POMDPs

   Arthur Wandzel, Yoonseon Oh (January 2019, IEEE International Conference on Robotics and Automation)

   Abstract— A core capability of robots is to reason about mul- tiple objects under uncertainty. Partially Observable Markov Decision Processes (POMDPs) provide a means of reasoning under uncertainty for sequential decision making, but are computationally intractable in large domains. In this paper, we propose Object-Oriented POMDPs (OO-POMDPs), which represent the state and observation spaces in terms of classes and objects. The structure afforded by OO-POMDPs support a factorization of the agent’s belief into independent object distributions, which enables the size of the belief to scale linearly versus exponentially in the number of objects. We formulate a novel Multi-Object Search (MOS) task as an OO-POMDP for mobile robotics domains in which the agent must find the locations of multiple objects. Our solution exploits the structure of OO-POMDPs by featuring human language to selectively update the belief at task onset. Using this structure, we develop a new algorithm for efficiently solving OO-POMDPs: Object- Oriented Partially Observable Monte-Carlo Planning (OO- POMCP). We show that OO-POMCP with grounded language commands is sufficient for solving challenging MOS tasks both in simulation and on a physical mobile robot. 

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3. Using Generated Object Reconstructions to Study Object-based Attention

   https://doi.org/10.32470/CCN.2023.1685-0  

   Ahn, Seoyoung; Adeli, Hossein; Zelinsky, Gregory (January 2023, Proceedings of 2023 Conference on Cognitive Computational Neuroscience)
4. Multi-Object Manipulation via Object-Centric Neural Scattering Functions

   https://doi.org/10.1109/CVPR52729.2023.00871  

   Tian, Stephen; Cai, Yancheng; Yu, Hong–Xing; Zakharov, Sergey; Liu, Katherine; Gaidon, Adrien; Li, Yunzhu; Wu, Jiajun (June 2023, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR))
5. O2O-Afford: Annotation-Free Large-Scale Object-Object Affordance Learning

   Kaichun Mo, Yuzhe Qin (November 2021, Conference on Robot Learning (CoRL) 2021)

   Contrary to the vast literature in modeling, perceiving, and understanding agent-object (e.g., human-object, hand-object, robot-object) interaction in computer vision and robotics, very few past works have studied the task of object-object interaction, which also plays an important role in robotic manipulation and planning tasks. There is a rich space of object-object interaction scenarios in our daily life, such as placing an object on a messy tabletop, fitting an object inside a drawer, pushing an object using a tool, etc. In this paper, we propose a unified affordance learning framework to learn object-object interaction for various tasks. By constructing four object-object interaction task environments using physical simulation (SAPIEN) and thousands of ShapeNet models with rich geometric diversity, we are able to conduct large-scale object-object affordance learning without the need for human annotations or demonstrations. At the core of technical contribution, we propose an object-kernel point convolution network to reason about detailed interaction between two objects. Experiments on large-scale synthetic data and real-world data prove the effectiveness of the proposed approach. 

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